id	sid	tid	token	lemma	pos
cana-4790	1	1	communications	communication	NOUN
cana-4790	1	2	on	on	ADP
cana-4790	1	3	applied	apply	VERB
cana-4790	1	4	nonlinear	nonlinear	ADJ
cana-4790	1	5	analysis	analysis	NOUN
cana-4790	1	6	issn	issn	NOUN
cana-4790	1	7	:	:	PUNCT
cana-4790	1	8	1074	1074	NUM
cana-4790	1	9	-	-	PUNCT
cana-4790	1	10	133x	133x	NUM
cana-4790	1	11	vol	vol	NOUN
cana-4790	1	12	31	31	NUM
cana-4790	1	13	no	no	NOUN
cana-4790	1	14	.	.	PUNCT
cana-4790	2	1	1s	1s	NUM
cana-4790	2	2	(	(	PUNCT
cana-4790	2	3	2024	2024	NUM
cana-4790	2	4	)	)	PUNCT
cana-4790	2	5	200	200	NUM
cana-4790	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	2	7	learning	learn	VERB
cana-4790	2	8	spatial	spatial	ADJ
cana-4790	2	9	and	and	CCONJ
cana-4790	2	10	temporal	temporal	ADJ
cana-4790	2	11	eeg	eeg	NOUN
cana-4790	2	12	patterns	pattern	NOUN
cana-4790	2	13	for	for	ADP
cana-4790	2	14	alzheimer	alzheimer	PROPN
cana-4790	2	15	’s	’s	PART
cana-4790	2	16	disease	disease	NOUN
cana-4790	2	17	detection	detection	NOUN
cana-4790	2	18	with	with	ADP
cana-4790	2	19	cnn	cnn	PROPN
cana-4790	2	20	-	-	PUNCT
cana-4790	2	21	lstm	lstm	PROPN
cana-4790	2	22	networks	network	NOUN
cana-4790	2	23	sharyu	sharyu	ADJ
cana-4790	2	24	ikhar	ikhar	VERB
cana-4790	2	25	,	,	PUNCT
cana-4790	2	26	dr	dr	PROPN
cana-4790	2	27	.	.	PROPN
cana-4790	2	28	priya	priya	PROPN
cana-4790	2	29	vij	vij	PROPN
cana-4790	2	30	research	research	PROPN
cana-4790	2	31	scholar	scholar	NOUN
cana-4790	2	32	,	,	PUNCT
cana-4790	2	33	department	department	NOUN
cana-4790	2	34	of	of	ADP
cana-4790	2	35	computer	computer	NOUN
cana-4790	2	36	science	science	NOUN
cana-4790	2	37	and	and	CCONJ
cana-4790	2	38	engineering	engineering	NOUN
cana-4790	2	39	kalinga	kalinga	PROPN
cana-4790	2	40	university	university	PROPN
cana-4790	2	41	raipur	raipur	PROPN
cana-4790	2	42	department	department	PROPN
cana-4790	2	43	of	of	ADP
cana-4790	2	44	computer	computer	NOUN
cana-4790	2	45	science	science	NOUN
cana-4790	2	46	and	and	CCONJ
cana-4790	2	47	engineering	engineering	NOUN
cana-4790	2	48	kalinga	kalinga	PROPN
cana-4790	2	49	university	university	PROPN
cana-4790	2	50	raipur	raipur	PROPN
cana-4790	2	51	article	article	NOUN
cana-4790	2	52	history	history	NOUN
cana-4790	2	53	:	:	PUNCT
cana-4790	2	54	received	receive	VERB
cana-4790	2	55	:	:	PUNCT
cana-4790	2	56	10	10	NUM
cana-4790	2	57	-	-	PUNCT
cana-4790	2	58	02	02	NUM
cana-4790	2	59	-	-	PUNCT
cana-4790	2	60	2024	2024	NUM
cana-4790	2	61	revised	revise	VERB
cana-4790	2	62	:	:	PUNCT
cana-4790	2	63	12	12	NUM
cana-4790	2	64	-	-	PUNCT
cana-4790	2	65	04	04	NUM
cana-4790	2	66	-	-	PUNCT
cana-4790	2	67	2024	2024	NUM
cana-4790	2	68	accepted	accept	VERB
cana-4790	2	69	:	:	PUNCT
cana-4790	2	70	26	26	NUM
cana-4790	2	71	-	-	PUNCT
cana-4790	2	72	04	04	NUM
cana-4790	2	73	-	-	PUNCT
cana-4790	2	74	2024	2024	NUM
cana-4790	2	75	abstract	abstract	NOUN
cana-4790	2	76	:	:	PUNCT
cana-4790	2	77	alzheimer	alzheimer	PROPN
cana-4790	2	78	's	's	PART
cana-4790	2	79	disease	disease	NOUN
cana-4790	2	80	(	(	PUNCT
cana-4790	2	81	ad	ad	NOUN
cana-4790	2	82	)	)	PUNCT
cana-4790	2	83	is	be	AUX
cana-4790	2	84	a	a	DET
cana-4790	2	85	neurological	neurological	ADJ
cana-4790	2	86	disorder	disorder	NOUN
cana-4790	2	87	that	that	PRON
cana-4790	2	88	gets	get	VERB
cana-4790	2	89	worse	bad	ADJ
cana-4790	2	90	over	over	ADP
cana-4790	2	91	time	time	NOUN
cana-4790	2	92	and	and	CCONJ
cana-4790	2	93	makes	make	VERB
cana-4790	2	94	it	it	PRON
cana-4790	2	95	very	very	ADV
cana-4790	2	96	hard	hard	ADV
cana-4790	2	97	to	to	PART
cana-4790	2	98	remember	remember	VERB
cana-4790	2	99	things	thing	NOUN
cana-4790	2	100	and	and	CCONJ
cana-4790	2	101	think	think	VERB
cana-4790	2	102	clearly	clearly	ADV
cana-4790	2	103	.	.	PUNCT
cana-4790	3	1	it	it	PRON
cana-4790	3	2	is	be	AUX
cana-4790	3	3	very	very	ADV
cana-4790	3	4	important	important	ADJ
cana-4790	3	5	to	to	PART
cana-4790	3	6	find	find	VERB
cana-4790	3	7	ad	ad	NOUN
cana-4790	3	8	early	early	ADV
cana-4790	3	9	so	so	SCONJ
cana-4790	3	10	that	that	SCONJ
cana-4790	3	11	it	it	PRON
cana-4790	3	12	can	can	AUX
cana-4790	3	13	be	be	AUX
cana-4790	3	14	treated	treat	VERB
cana-4790	3	15	effectively	effectively	ADV
cana-4790	3	16	.	.	PUNCT
cana-4790	4	1	electroencephalography	electroencephalography	NOUN
cana-4790	4	2	(	(	PUNCT
cana-4790	4	3	eeg	eeg	NOUN
cana-4790	4	4	)	)	PUNCT
cana-4790	4	5	has	have	AUX
cana-4790	4	6	become	become	VERB
cana-4790	4	7	a	a	DET
cana-4790	4	8	hopeful	hopeful	ADJ
cana-4790	4	9	,	,	PUNCT
cana-4790	4	10	non	non	ADJ
cana-4790	4	11	-	-	ADJ
cana-4790	4	12	invasive	invasive	ADJ
cana-4790	4	13	,	,	PUNCT
cana-4790	4	14	and	and	CCONJ
cana-4790	4	15	low	low	ADJ
cana-4790	4	16	-	-	PUNCT
cana-4790	4	17	cost	cost	NOUN
cana-4790	4	18	way	way	NOUN
cana-4790	4	19	to	to	PART
cana-4790	4	20	find	find	VERB
cana-4790	4	21	problems	problem	NOUN
cana-4790	4	22	in	in	ADP
cana-4790	4	23	the	the	DET
cana-4790	4	24	brain	brain	NOUN
cana-4790	4	25	that	that	PRON
cana-4790	4	26	are	be	AUX
cana-4790	4	27	linked	link	VERB
cana-4790	4	28	to	to	ADP
cana-4790	4	29	alzheimer	alzheimer	PROPN
cana-4790	4	30	's	's	PART
cana-4790	4	31	disease	disease	NOUN
cana-4790	4	32	.	.	PUNCT
cana-4790	5	1	conversely	conversely	ADV
cana-4790	5	2	,	,	PUNCT
cana-4790	5	3	conventional	conventional	ADJ
cana-4790	5	4	machine	machine	NOUN
cana-4790	5	5	learning	learning	NOUN
cana-4790	5	6	techniques	technique	NOUN
cana-4790	5	7	can	can	AUX
cana-4790	5	8	rely	rely	VERB
cana-4790	5	9	on	on	ADP
cana-4790	5	10	manually	manually	ADV
cana-4790	5	11	developed	develop	VERB
cana-4790	5	12	features	feature	NOUN
cana-4790	5	13	and	and	CCONJ
cana-4790	5	14	struggle	struggle	VERB
cana-4790	5	15	to	to	PART
cana-4790	5	16	capture	capture	VERB
cana-4790	5	17	the	the	DET
cana-4790	5	18	complex	complex	ADJ
cana-4790	5	19	spatial	spatial	ADJ
cana-4790	5	20	and	and	CCONJ
cana-4790	5	21	temporal	temporal	ADJ
cana-4790	5	22	patterns	pattern	NOUN
cana-4790	5	23	of	of	ADP
cana-4790	5	24	eeg	eeg	PROPN
cana-4790	5	25	data	datum	NOUN
cana-4790	5	26	.	.	PUNCT
cana-4790	6	1	this	this	DET
cana-4790	6	2	paper	paper	NOUN
cana-4790	6	3	presents	present	VERB
cana-4790	6	4	a	a	DET
cana-4790	6	5	hybrid	hybrid	ADJ
cana-4790	6	6	deep	deep	ADJ
cana-4790	6	7	learning	learning	NOUN
cana-4790	6	8	model	model	NOUN
cana-4790	6	9	that	that	PRON
cana-4790	6	10	automatically	automatically	ADV
cana-4790	6	11	learns	learn	VERB
cana-4790	6	12	and	and	CCONJ
cana-4790	6	13	sorts	sort	NOUN
cana-4790	6	14	eeg	eeg	NOUN
cana-4790	6	15	data	datum	NOUN
cana-4790	6	16	depending	depend	VERB
cana-4790	6	17	on	on	ADP
cana-4790	6	18	patterns	pattern	NOUN
cana-4790	6	19	in	in	ADP
cana-4790	6	20	space	space	NOUN
cana-4790	6	21	and	and	CCONJ
cana-4790	6	22	time	time	NOUN
cana-4790	6	23	using	use	VERB
cana-4790	6	24	both	both	CCONJ
cana-4790	6	25	convolutional	convolutional	ADJ
cana-4790	6	26	neural	neural	ADJ
cana-4790	6	27	networks	network	NOUN
cana-4790	6	28	(	(	PUNCT
cana-4790	6	29	cnn	cnn	PROPN
cana-4790	6	30	)	)	PUNCT
cana-4790	6	31	and	and	CCONJ
cana-4790	6	32	long	long	ADJ
cana-4790	6	33	short	short	ADJ
cana-4790	6	34	-	-	PUNCT
cana-4790	6	35	term	term	NOUN
cana-4790	6	36	memory	memory	NOUN
cana-4790	6	37	(	(	PUNCT
cana-4790	6	38	lstm	lstm	NOUN
cana-4790	6	39	)	)	PUNCT
cana-4790	6	40	networks	network	NOUN
cana-4790	6	41	.	.	PUNCT
cana-4790	7	1	using	use	VERB
cana-4790	7	2	a	a	DET
cana-4790	7	3	publicly	publicly	ADV
cana-4790	7	4	accessible	accessible	ADJ
cana-4790	7	5	eeg	eeg	NOUN
cana-4790	7	6	dataset	dataset	NOUN
cana-4790	7	7	that	that	PRON
cana-4790	7	8	contained	contain	VERB
cana-4790	7	9	recordings	recording	NOUN
cana-4790	7	10	from	from	ADP
cana-4790	7	11	alzheimer	alzheimer	PROPN
cana-4790	7	12	's	's	PART
cana-4790	7	13	patients	patient	NOUN
cana-4790	7	14	and	and	CCONJ
cana-4790	7	15	healthy	healthy	ADJ
cana-4790	7	16	controls	control	NOUN
cana-4790	7	17	,	,	PUNCT
cana-4790	7	18	we	we	PRON
cana-4790	7	19	developed	develop	VERB
cana-4790	7	20	a	a	DET
cana-4790	7	21	robust	robust	ADJ
cana-4790	7	22	filtering	filtering	NOUN
cana-4790	7	23	process	process	NOUN
cana-4790	7	24	.	.	PUNCT
cana-4790	8	1	we	we	PRON
cana-4790	8	2	then	then	ADV
cana-4790	8	3	instructed	instruct	VERB
cana-4790	8	4	the	the	DET
cana-4790	8	5	cnn	cnn	PROPN
cana-4790	8	6	-	-	PUNCT
cana-4790	8	7	lstm	lstm	PROPN
cana-4790	8	8	model	model	NOUN
cana-4790	8	9	to	to	PART
cana-4790	8	10	differentiate	differentiate	VERB
cana-4790	8	11	between	between	ADP
cana-4790	8	12	the	the	DET
cana-4790	8	13	two	two	NUM
cana-4790	8	14	groups	group	NOUN
cana-4790	8	15	.	.	PUNCT
cana-4790	9	1	the	the	DET
cana-4790	9	2	model	model	NOUN
cana-4790	9	3	outperformed	outperform	VERB
cana-4790	9	4	simple	simple	ADJ
cana-4790	9	5	models	model	NOUN
cana-4790	9	6	such	such	ADJ
cana-4790	9	7	as	as	ADP
cana-4790	9	8	svm	svm	ADJ
cana-4790	9	9	,	,	PUNCT
cana-4790	9	10	random	random	ADJ
cana-4790	9	11	forest	forest	NOUN
cana-4790	9	12	,	,	PUNCT
cana-4790	9	13	cnn	cnn	PROPN
cana-4790	9	14	-	-	PUNCT
cana-4790	9	15	only	only	ADV
cana-4790	9	16	,	,	PUNCT
cana-4790	9	17	and	and	CCONJ
cana-4790	9	18	lstm	lstm	NOUN
cana-4790	9	19	-	-	PUNCT
cana-4790	9	20	only	only	ADV
cana-4790	9	21	designs	design	NOUN
cana-4790	9	22	with	with	ADP
cana-4790	9	23	an	an	DET
cana-4790	9	24	accuracy	accuracy	NOUN
cana-4790	9	25	of	of	ADP
cana-4790	9	26	93.2	93.2	NUM
cana-4790	9	27	%	%	NOUN
cana-4790	9	28	,	,	PUNCT
cana-4790	9	29	a	a	DET
cana-4790	9	30	precision	precision	NOUN
cana-4790	9	31	of	of	ADP
cana-4790	9	32	91.5	91.5	NUM
cana-4790	9	33	%	%	NOUN
cana-4790	9	34	,	,	PUNCT
cana-4790	9	35	a	a	DET
cana-4790	9	36	recall	recall	NOUN
cana-4790	9	37	of	of	ADP
cana-4790	9	38	94.8	94.8	NUM
cana-4790	9	39	%	%	NOUN
cana-4790	9	40	,	,	PUNCT
cana-4790	9	41	and	and	CCONJ
cana-4790	9	42	an	an	DET
cana-4790	9	43	f1	f1	NOUN
cana-4790	9	44	-	-	PUNCT
cana-4790	9	45	score	score	NOUN
cana-4790	9	46	of	of	ADP
cana-4790	9	47	93.1	93.1	NUM
cana-4790	9	48	%	%	NOUN
cana-4790	9	49	.	.	PUNCT
cana-4790	10	1	the	the	DET
cana-4790	10	2	results	result	NOUN
cana-4790	10	3	show	show	VERB
cana-4790	10	4	that	that	SCONJ
cana-4790	10	5	mixing	mix	VERB
cana-4790	10	6	spatial	spatial	ADJ
cana-4790	10	7	and	and	CCONJ
cana-4790	10	8	temporal	temporal	ADJ
cana-4790	10	9	feature	feature	NOUN
cana-4790	10	10	extraction	extraction	NOUN
cana-4790	10	11	works	work	VERB
cana-4790	10	12	well	well	ADV
cana-4790	10	13	for	for	ADP
cana-4790	10	14	accurate	accurate	ADJ
cana-4790	10	15	eegbased	eegbased	PROPN
cana-4790	10	16	alzheimer	alzheimer	PROPN
cana-4790	10	17	's	's	PART
cana-4790	10	18	detection	detection	NOUN
cana-4790	10	19	,	,	PUNCT
cana-4790	10	20	which	which	PRON
cana-4790	10	21	opens	open	VERB
cana-4790	10	22	up	up	ADP
cana-4790	10	23	a	a	DET
cana-4790	10	24	lot	lot	NOUN
cana-4790	10	25	of	of	ADP
cana-4790	10	26	possibilities	possibility	NOUN
cana-4790	10	27	for	for	ADP
cana-4790	10	28	real	real	ADJ
cana-4790	10	29	-	-	PUNCT
cana-4790	10	30	time	time	NOUN
cana-4790	10	31	and	and	CCONJ
cana-4790	10	32	clinical	clinical	ADJ
cana-4790	10	33	diagnostic	diagnostic	ADJ
cana-4790	10	34	uses	use	NOUN
cana-4790	10	35	.	.	PUNCT
cana-4790	11	1	keywords	keyword	NOUN
cana-4790	11	2	:	:	PUNCT
cana-4790	11	3	alzheimer	alzheimer	PROPN
cana-4790	11	4	’s	’s	PART
cana-4790	11	5	disease	disease	NOUN
cana-4790	11	6	,	,	PUNCT
cana-4790	11	7	eeg	eeg	PROPN
cana-4790	11	8	,	,	PUNCT
cana-4790	11	9	cnn	cnn	PROPN
cana-4790	11	10	-	-	PUNCT
cana-4790	11	11	lstm	lstm	PROPN
cana-4790	11	12	,	,	PUNCT
cana-4790	11	13	deep	deep	ADJ
cana-4790	11	14	learning	learning	NOUN
cana-4790	11	15	,	,	PUNCT
cana-4790	11	16	spatiotemporal	spatiotemporal	ADJ
cana-4790	11	17	modeling	modeling	NOUN
cana-4790	11	18	,	,	PUNCT
cana-4790	11	19	brain	brain	NOUN
cana-4790	11	20	signal	signal	NOUN
cana-4790	11	21	analysis	analysis	NOUN
cana-4790	11	22	,	,	PUNCT
cana-4790	11	23	neurodiagnosis	neurodiagnosis	NOUN
cana-4790	11	24	,	,	PUNCT
cana-4790	11	25	machine	machine	NOUN
cana-4790	11	26	learning	learning	NOUN
cana-4790	11	27	.	.	PUNCT
cana-4790	12	1	1	1	X
cana-4790	12	2	.	.	X
cana-4790	12	3	introduction	introduction	NOUN
cana-4790	12	4	mostly	mostly	ADV
cana-4790	12	5	affecting	affect	VERB
cana-4790	12	6	elderly	elderly	ADJ
cana-4790	12	7	persons	person	NOUN
cana-4790	12	8	,	,	PUNCT
cana-4790	12	9	alzheimer	alzheimer	PROPN
cana-4790	12	10	's	's	PART
cana-4790	12	11	disease	disease	NOUN
cana-4790	12	12	(	(	PUNCT
cana-4790	12	13	ad	ad	NOUN
cana-4790	12	14	)	)	PUNCT
cana-4790	12	15	is	be	AUX
cana-4790	12	16	a	a	DET
cana-4790	12	17	neurological	neurological	ADJ
cana-4790	12	18	condition	condition	NOUN
cana-4790	12	19	that	that	PRON
cana-4790	12	20	worsens	worsen	VERB
cana-4790	12	21	with	with	ADP
cana-4790	12	22	time	time	NOUN
cana-4790	12	23	.	.	PUNCT
cana-4790	13	1	it	it	PRON
cana-4790	13	2	makes	make	VERB
cana-4790	13	3	behavioural	behavioural	ADJ
cana-4790	13	4	skills	skill	NOUN
cana-4790	13	5	,	,	PUNCT
cana-4790	13	6	linguistic	linguistic	ADJ
cana-4790	13	7	ability	ability	NOUN
cana-4790	13	8	,	,	PUNCT
cana-4790	13	9	cognitive	cognitive	ADJ
cana-4790	13	10	function	function	NOUN
cana-4790	13	11	,	,	PUNCT
cana-4790	13	12	and	and	CCONJ
cana-4790	13	13	memory	memory	NOUN
cana-4790	13	14	steadily	steadily	ADV
cana-4790	13	15	decline	decline	VERB
cana-4790	13	16	.	.	PUNCT
cana-4790	14	1	among	among	ADP
cana-4790	14	2	the	the	DET
cana-4790	14	3	most	most	ADV
cana-4790	14	4	prevalent	prevalent	ADJ
cana-4790	14	5	forms	form	NOUN
cana-4790	14	6	of	of	ADP
cana-4790	14	7	dementia	dementia	NOUN
cana-4790	14	8	,	,	PUNCT
cana-4790	14	9	ad	ad	NOUN
cana-4790	14	10	impacts	impact	VERB
cana-4790	14	11	individuals	individual	NOUN
cana-4790	14	12	all	all	ADV
cana-4790	14	13	around	around	ADV
cana-4790	14	14	.	.	PUNCT
cana-4790	15	1	as	as	ADP
cana-4790	15	2	the	the	DET
cana-4790	15	3	population	population	NOUN
cana-4790	15	4	of	of	ADP
cana-4790	15	5	the	the	DET
cana-4790	15	6	globe	globe	NOUN
cana-4790	15	7	ages	age	NOUN
cana-4790	15	8	,	,	PUNCT
cana-4790	15	9	this	this	DET
cana-4790	15	10	figure	figure	NOUN
cana-4790	15	11	is	be	AUX
cana-4790	15	12	expected	expect	VERB
cana-4790	15	13	to	to	PART
cana-4790	15	14	climb	climb	VERB
cana-4790	15	15	considerably	considerably	ADV
cana-4790	15	16	.	.	PUNCT
cana-4790	16	1	though	though	SCONJ
cana-4790	16	2	we	we	PRON
cana-4790	16	3	understand	understand	VERB
cana-4790	16	4	more	more	ADJ
cana-4790	16	5	about	about	ADP
cana-4790	16	6	how	how	SCONJ
cana-4790	16	7	ad	ad	NOUN
cana-4790	16	8	works	work	VERB
cana-4790	16	9	,	,	PUNCT
cana-4790	16	10	early	early	ADJ
cana-4790	16	11	and	and	CCONJ
cana-4790	16	12	accurate	accurate	ADJ
cana-4790	16	13	diagnosis	diagnosis	NOUN
cana-4790	16	14	in	in	ADP
cana-4790	16	15	clinical	clinical	ADJ
cana-4790	16	16	practice	practice	NOUN
cana-4790	16	17	remains	remain	VERB
cana-4790	16	18	difficult	difficult	ADJ
cana-4790	16	19	.	.	PUNCT
cana-4790	17	1	this	this	PRON
cana-4790	17	2	is	be	AUX
cana-4790	17	3	particularly	particularly	ADV
cana-4790	17	4	true	true	ADJ
cana-4790	17	5	given	give	VERB
cana-4790	17	6	the	the	DET
cana-4790	17	7	varied	varied	ADJ
cana-4790	17	8	progression	progression	NOUN
cana-4790	17	9	of	of	ADP
cana-4790	17	10	the	the	DET
cana-4790	17	11	illness	illness	NOUN
cana-4790	17	12	and	and	CCONJ
cana-4790	17	13	the	the	DET
cana-4790	17	14	gradual	gradual	ADJ
cana-4790	17	15	onset	onset	NOUN
cana-4790	17	16	of	of	ADP
cana-4790	17	17	cognitive	cognitive	ADJ
cana-4790	17	18	impairment	impairment	NOUN
cana-4790	17	19	in	in	ADP
cana-4790	17	20	the	the	DET
cana-4790	17	21	early	early	ADJ
cana-4790	17	22	stages	stage	NOUN
cana-4790	17	23	.	.	PUNCT
cana-4790	18	1	brain	brain	NOUN
cana-4790	18	2	scans	scan	NOUN
cana-4790	18	3	,	,	PUNCT
cana-4790	18	4	mris	mris	NOUN
cana-4790	18	5	,	,	PUNCT
cana-4790	18	6	and	and	CCONJ
cana-4790	18	7	pet	pet	ADJ
cana-4790	18	8	scans	scan	NOUN
cana-4790	18	9	are	be	AUX
cana-4790	18	10	some	some	PRON
cana-4790	18	11	of	of	ADP
cana-4790	18	12	the	the	DET
cana-4790	18	13	traditional	traditional	ADJ
cana-4790	18	14	ways	way	NOUN
cana-4790	18	15	to	to	PART
cana-4790	18	16	diagnose	diagnose	VERB
cana-4790	18	17	ad	ad	NOUN
cana-4790	18	18	.	.	PUNCT
cana-4790	19	1	these	these	PRON
cana-4790	19	2	include	include	VERB
cana-4790	19	3	mental	mental	ADJ
cana-4790	19	4	tests	test	NOUN
cana-4790	19	5	,	,	PUNCT
cana-4790	19	6	csf	csf	ADJ
cana-4790	19	7	analysis	analysis	NOUN
cana-4790	19	8	,	,	PUNCT
cana-4790	19	9	and	and	CCONJ
cana-4790	19	10	positron	positron	NOUN
cana-4790	19	11	emission	emission	NOUN
cana-4790	19	12	tomography	tomography	NOUN
cana-4790	19	13	(	(	PUNCT
cana-4790	19	14	pet	pet	NOUN
cana-4790	19	15	)	)	PUNCT
cana-4790	19	16	.	.	PUNCT
cana-4790	20	1	even	even	ADV
cana-4790	20	2	though	though	SCONJ
cana-4790	20	3	these	these	DET
cana-4790	20	4	methods	method	NOUN
cana-4790	20	5	give	give	VERB
cana-4790	20	6	useful	useful	ADJ
cana-4790	20	7	information	information	NOUN
cana-4790	20	8	,	,	PUNCT
cana-4790	20	9	they	they	PRON
cana-4790	20	10	are	be	AUX
cana-4790	20	11	often	often	ADV
cana-4790	20	12	intrusive	intrusive	ADJ
cana-4790	20	13	,	,	PUNCT
cana-4790	20	14	expensive	expensive	ADJ
cana-4790	20	15	,	,	PUNCT
cana-4790	20	16	time	time	NOUN
cana-4790	20	17	-	-	PUNCT
cana-4790	20	18	consuming	consume	VERB
cana-4790	20	19	,	,	PUNCT
cana-4790	20	20	and	and	CCONJ
cana-4790	20	21	hard	hard	ADJ
cana-4790	20	22	for	for	SCONJ
cana-4790	20	23	many	many	ADJ
cana-4790	20	24	people	people	NOUN
cana-4790	20	25	to	to	PART
cana-4790	20	26	use	use	VERB
cana-4790	20	27	.	.	PUNCT
cana-4790	21	1	these	these	DET
cana-4790	21	2	problems	problem	NOUN
cana-4790	21	3	make	make	VERB
cana-4790	21	4	regular	regular	ADJ
cana-4790	21	5	screening	screening	NOUN
cana-4790	21	6	and	and	CCONJ
cana-4790	21	7	tracking	track	VERB
cana-4790	21	8	harder	hard	ADV
cana-4790	21	9	,	,	PUNCT
cana-4790	21	10	especially	especially	ADV
cana-4790	21	11	in	in	ADP
cana-4790	21	12	places	place	NOUN
cana-4790	21	13	with	with	ADP
cana-4790	21	14	few	few	ADJ
cana-4790	21	15	resources	resource	NOUN
cana-4790	21	16	.	.	PUNCT
cana-4790	22	1	therefore	therefore	ADV
cana-4790	22	2	,	,	PUNCT
cana-4790	22	3	there	there	PRON
cana-4790	22	4	is	be	VERB
cana-4790	22	5	an	an	DET
cana-4790	22	6	urgent	urgent	ADJ
cana-4790	22	7	need	need	NOUN
cana-4790	22	8	for	for	ADP
cana-4790	22	9	testing	testing	NOUN
cana-4790	22	10	options	option	NOUN
cana-4790	22	11	that	that	PRON
cana-4790	22	12	are	be	AUX
cana-4790	22	13	easy	easy	ADJ
cana-4790	22	14	to	to	PART
cana-4790	22	15	get	get	VERB
cana-4790	22	16	,	,	PUNCT
cana-4790	22	17	do	do	AUX
cana-4790	22	18	n't	not	PART
cana-4790	22	19	cost	cost	VERB
cana-4790	22	20	a	a	DET
cana-4790	22	21	lot	lot	NOUN
cana-4790	22	22	,	,	PUNCT
cana-4790	22	23	and	and	CCONJ
cana-4790	22	24	can	can	AUX
cana-4790	22	25	help	help	VERB
cana-4790	22	26	with	with	ADP
cana-4790	22	27	early	early	ADJ
cana-4790	22	28	diagnosis	diagnosis	NOUN
cana-4790	22	29	and	and	CCONJ
cana-4790	22	30	response	response	NOUN
cana-4790	22	31	methods	method	NOUN
cana-4790	22	32	.	.	PUNCT
cana-4790	23	1	in	in	ADP
cana-4790	23	2	this	this	DET
cana-4790	23	3	context	context	NOUN
cana-4790	23	4	,	,	PUNCT
cana-4790	23	5	electroencephalography	electroencephalography	NOUN
cana-4790	23	6	(	(	PUNCT
cana-4790	23	7	eeg	eeg	NOUN
cana-4790	23	8	)	)	PUNCT
cana-4790	23	9	has	have	AUX
cana-4790	23	10	emerged	emerge	VERB
cana-4790	23	11	as	as	ADP
cana-4790	23	12	a	a	DET
cana-4790	23	13	really	really	ADV
cana-4790	23	14	promising	promising	ADJ
cana-4790	23	15	technique	technique	NOUN
cana-4790	23	16	.	.	PUNCT
cana-4790	24	1	eeg	eeg	NOUN
cana-4790	24	2	records	record	VERB
cana-4790	24	3	the	the	DET
cana-4790	24	4	electrical	electrical	ADJ
cana-4790	24	5	activity	activity	NOUN
cana-4790	24	6	of	of	ADP
cana-4790	24	7	the	the	DET
cana-4790	24	8	brain	brain	NOUN
cana-4790	24	9	using	use	VERB
cana-4790	24	10	probes	probe	NOUN
cana-4790	24	11	placed	place	VERB
cana-4790	24	12	on	on	ADP
cana-4790	24	13	the	the	DET
cana-4790	24	14	head	head	NOUN
cana-4790	24	15	.	.	PUNCT
cana-4790	25	1	it	it	PRON
cana-4790	25	2	provides	provide	VERB
cana-4790	25	3	a	a	DET
cana-4790	25	4	real	real	ADJ
cana-4790	25	5	-	-	PUNCT
cana-4790	25	6	time	time	NOUN
cana-4790	25	7	view	view	NOUN
cana-4790	25	8	of	of	ADP
cana-4790	25	9	how	how	SCONJ
cana-4790	25	10	the	the	DET
cana-4790	25	11	brain	brain	NOUN
cana-4790	25	12	functions	function	NOUN
cana-4790	25	13	.	.	PUNCT
cana-4790	26	1	it	it	PRON
cana-4790	26	2	does	do	AUX
cana-4790	26	3	n't	not	PART
cana-4790	26	4	harm	harm	VERB
cana-4790	26	5	the	the	DET
cana-4790	26	6	brain	brain	NOUN
cana-4790	26	7	,	,	PUNCT
cana-4790	26	8	does	do	AUX
cana-4790	26	9	n't	not	PART
cana-4790	26	10	break	break	VERB
cana-4790	26	11	the	the	DET
cana-4790	26	12	bank	bank	NOUN
cana-4790	26	13	,	,	PUNCT
cana-4790	26	14	and	and	CCONJ
cana-4790	26	15	can	can	AUX
cana-4790	26	16	capture	capture	VERB
cana-4790	26	17	both	both	CCONJ
cana-4790	26	18	the	the	DET
cana-4790	26	19	spatial	spatial	ADJ
cana-4790	26	20	and	and	CCONJ
cana-4790	26	21	temporal	temporal	ADJ
cana-4790	26	22	patterns	pattern	NOUN
cana-4790	26	23	of	of	ADP
cana-4790	26	24	cerebral	cerebral	ADJ
cana-4790	26	25	activity	activity	NOUN
cana-4790	26	26	.	.	PUNCT
cana-4790	27	1	many	many	ADJ
cana-4790	27	2	studies	study	NOUN
cana-4790	27	3	have	have	AUX
cana-4790	27	4	shown	show	VERB
cana-4790	27	5	that	that	SCONJ
cana-4790	27	6	certain	certain	ADJ
cana-4790	27	7	alterations	alteration	NOUN
cana-4790	27	8	in	in	ADP
cana-4790	27	9	eeg	eeg	PROPN
cana-4790	27	10	patterns	pattern	NOUN
cana-4790	27	11	connect	connect	VERB
cana-4790	27	12	alzheimer	alzheimer	PROPN
cana-4790	27	13	's	's	PART
cana-4790	27	14	disease	disease	NOUN
cana-4790	27	15	(	(	PUNCT
cana-4790	27	16	ad	ad	NOUN
cana-4790	27	17	)	)	PUNCT
cana-4790	27	18	with	with	ADP
cana-4790	27	19	moderate	moderate	ADJ
cana-4790	27	20	cognitive	cognitive	ADJ
cana-4790	27	21	impairment	impairment	NOUN
cana-4790	27	22	(	(	PUNCT
cana-4790	27	23	mci	mci	PROPN
cana-4790	27	24	)	)	PUNCT
cana-4790	27	25	,	,	PUNCT
cana-4790	27	26	the	the	DET
cana-4790	27	27	preclinical	preclinical	ADJ
cana-4790	27	28	stage	stage	NOUN
cana-4790	27	29	of	of	ADP
cana-4790	27	30	ad	ad	NOUN
cana-4790	27	31	.	.	PUNCT
cana-4790	28	1	these	these	DET
cana-4790	28	2	alterations	alteration	NOUN
cana-4790	28	3	consist	consist	VERB
cana-4790	28	4	of	of	ADP
cana-4790	28	5	reduced	reduce	VERB
cana-4790	28	6	complexity	complexity	NOUN
cana-4790	28	7	,	,	PUNCT
cana-4790	28	8	lower	low	ADJ
cana-4790	28	9	power	power	NOUN
cana-4790	28	10	in	in	ADP
cana-4790	28	11	higher	high	ADJ
cana-4790	28	12	frequency	frequency	NOUN
cana-4790	28	13	bands	band	NOUN
cana-4790	28	14	,	,	PUNCT
cana-4790	28	15	and	and	CCONJ
cana-4790	28	16	altered	alter	VERB
cana-4790	28	17	connectivity	connectivity	NOUN
cana-4790	28	18	across	across	ADP
cana-4790	28	19	brain	brain	NOUN
cana-4790	28	20	regions	region	NOUN
cana-4790	28	21	.	.	PUNCT
cana-4790	29	1	these	these	DET
cana-4790	29	2	elements	element	NOUN
cana-4790	29	3	make	make	VERB
cana-4790	29	4	it	it	PRON
cana-4790	29	5	possible	possible	ADJ
cana-4790	29	6	for	for	SCONJ
cana-4790	29	7	eeg	eeg	PROPN
cana-4790	29	8	to	to	PART
cana-4790	29	9	automatically	automatically	ADV
cana-4790	29	10	identify	identify	VERB
cana-4790	29	11	ad	ad	NOUN
cana-4790	29	12	.	.	PUNCT
cana-4790	30	1	but	but	CCONJ
cana-4790	30	2	it	it	PRON
cana-4790	30	3	's	be	AUX
cana-4790	30	4	not	not	PART
cana-4790	30	5	easy	easy	ADJ
cana-4790	30	6	to	to	PART
cana-4790	30	7	use	use	VERB
cana-4790	30	8	eeg	eeg	NOUN
cana-4790	30	9	data	datum	NOUN
cana-4790	30	10	analysis	analysis	NOUN
cana-4790	30	11	for	for	ADP
cana-4790	30	12	professional	professional	ADJ
cana-4790	30	13	purposes	purpose	NOUN
cana-4790	30	14	.	.	PUNCT
cana-4790	31	1	it	it	PRON
cana-4790	31	2	's	be	AUX
cana-4790	31	3	naturally	naturally	ADV
cana-4790	31	4	non	non	ADJ
cana-4790	31	5	-	-	ADJ
cana-4790	31	6	linear	linear	ADJ
cana-4790	31	7	and	and	CCONJ
cana-4790	31	8	non	non	ADJ
cana-4790	31	9	-	-	ADJ
cana-4790	31	10	stationary	stationary	ADJ
cana-4790	31	11	for	for	SCONJ
cana-4790	31	12	eeg	eeg	PROPN
cana-4790	31	13	data	datum	NOUN
cana-4790	31	14	to	to	PART
cana-4790	31	15	be	be	AUX
cana-4790	31	16	free	free	ADJ
cana-4790	31	17	of	of	ADP
cana-4790	31	18	noise	noise	NOUN
cana-4790	31	19	and	and	CCONJ
cana-4790	31	20	other	other	ADJ
cana-4790	31	21	errors	error	NOUN
cana-4790	31	22	.	.	PUNCT
cana-4790	32	1	normal	normal	ADJ
cana-4790	32	2	communications	communication	NOUN
cana-4790	32	3	on	on	ADP
cana-4790	32	4	applied	apply	VERB
cana-4790	32	5	nonlinear	nonlinear	ADJ
cana-4790	32	6	analysis	analysis	NOUN
cana-4790	32	7	issn	issn	NOUN
cana-4790	32	8	:	:	PUNCT
cana-4790	32	9	1074	1074	NUM
cana-4790	32	10	-	-	PUNCT
cana-4790	32	11	133x	133x	NUM
cana-4790	32	12	vol	vol	NOUN
cana-4790	32	13	31	31	NUM
cana-4790	32	14	no	no	NOUN
cana-4790	32	15	.	.	PUNCT
cana-4790	33	1	1s	1s	NUM
cana-4790	33	2	(	(	PUNCT
cana-4790	33	3	2024	2024	NUM
cana-4790	33	4	)	)	PUNCT
cana-4790	33	5	201	201	NUM
cana-4790	33	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-4790	33	7	signal	signal	NOUN
cana-4790	33	8	processing	processing	NOUN
cana-4790	33	9	and	and	CCONJ
cana-4790	33	10	statistical	statistical	ADJ
cana-4790	33	11	methods	method	NOUN
cana-4790	33	12	need	need	VERB
cana-4790	33	13	a	a	DET
cana-4790	33	14	lot	lot	NOUN
cana-4790	33	15	of	of	ADP
cana-4790	33	16	subject	subject	ADJ
cana-4790	33	17	knowledge	knowledge	NOUN
cana-4790	33	18	and	and	CCONJ
cana-4790	33	19	ca	can	AUX
cana-4790	33	20	n't	not	PART
cana-4790	33	21	fully	fully	ADV
cana-4790	33	22	capture	capture	VERB
cana-4790	33	23	the	the	DET
cana-4790	33	24	brain	brain	NOUN
cana-4790	33	25	's	's	PART
cana-4790	33	26	complex	complex	ADJ
cana-4790	33	27	spatiotemporal	spatiotemporal	ADJ
cana-4790	33	28	relationships	relationship	NOUN
cana-4790	33	29	.	.	PUNCT
cana-4790	34	1	because	because	SCONJ
cana-4790	34	2	of	of	ADP
cana-4790	34	3	this	this	PRON
cana-4790	34	4	,	,	PUNCT
cana-4790	34	5	there	there	PRON
cana-4790	34	6	is	be	VERB
cana-4790	34	7	more	more	ADV
cana-4790	34	8	and	and	CCONJ
cana-4790	34	9	more	more	ADJ
cana-4790	34	10	interest	interest	NOUN
cana-4790	34	11	in	in	ADP
cana-4790	34	12	machine	machine	NOUN
cana-4790	34	13	learning	learning	NOUN
cana-4790	34	14	(	(	PUNCT
cana-4790	34	15	ml	ml	NOUN
cana-4790	34	16	)	)	PUNCT
cana-4790	34	17	and	and	CCONJ
cana-4790	34	18	deep	deep	ADJ
cana-4790	34	19	learning	learning	NOUN
cana-4790	34	20	(	(	PUNCT
cana-4790	34	21	dl	dl	NOUN
cana-4790	34	22	)	)	PUNCT
cana-4790	34	23	methods	method	NOUN
cana-4790	34	24	that	that	PRON
cana-4790	34	25	can	can	AUX
cana-4790	34	26	automatically	automatically	ADV
cana-4790	34	27	learn	learn	VERB
cana-4790	34	28	features	feature	NOUN
cana-4790	34	29	that	that	PRON
cana-4790	34	30	can	can	AUX
cana-4790	34	31	tell	tell	VERB
cana-4790	34	32	eeg	eeg	NOUN
cana-4790	34	33	patterns	pattern	NOUN
cana-4790	34	34	apart	apart	ADV
cana-4790	34	35	from	from	ADP
cana-4790	34	36	ones	one	NOUN
cana-4790	34	37	that	that	PRON
cana-4790	34	38	have	have	AUX
cana-4790	34	39	been	be	AUX
cana-4790	34	40	barely	barely	ADV
cana-4790	34	41	handled	handle	VERB
cana-4790	34	42	.	.	PUNCT
cana-4790	35	1	two	two	NUM
cana-4790	35	2	deep	deep	ADJ
cana-4790	35	3	learning	learning	NOUN
cana-4790	35	4	models	model	NOUN
cana-4790	35	5	that	that	PRON
cana-4790	35	6	have	have	AUX
cana-4790	35	7	shown	show	VERB
cana-4790	35	8	great	great	ADJ
cana-4790	35	9	potential	potential	NOUN
cana-4790	35	10	in	in	ADP
cana-4790	35	11	eeg	eeg	NOUN
cana-4790	35	12	-	-	PUNCT
cana-4790	35	13	based	base	VERB
cana-4790	35	14	categorisation	categorisation	NOUN
cana-4790	35	15	activities	activity	NOUN
cana-4790	35	16	are	be	AUX
cana-4790	35	17	convolutional	convolutional	ADJ
cana-4790	35	18	neural	neural	ADJ
cana-4790	35	19	networks	network	NOUN
cana-4790	35	20	(	(	PUNCT
cana-4790	35	21	cnns	cnns	PROPN
cana-4790	35	22	)	)	PUNCT
cana-4790	35	23	and	and	CCONJ
cana-4790	35	24	recurrent	recurrent	ADJ
cana-4790	35	25	neural	neural	ADJ
cana-4790	35	26	networks	network	NOUN
cana-4790	35	27	(	(	PUNCT
cana-4790	35	28	rnns	rnns	PROPN
cana-4790	35	29	)	)	PUNCT
cana-4790	35	30	.	.	PUNCT
cana-4790	36	1	by	by	ADP
cana-4790	36	2	use	use	NOUN
cana-4790	36	3	of	of	ADP
cana-4790	36	4	convolutional	convolutional	ADJ
cana-4790	36	5	filters	filter	NOUN
cana-4790	36	6	,	,	PUNCT
cana-4790	36	7	cnns	cnn	NOUN
cana-4790	36	8	excel	excel	VERB
cana-4790	36	9	in	in	ADP
cana-4790	36	10	revealing	reveal	VERB
cana-4790	36	11	spatial	spatial	ADJ
cana-4790	36	12	relationships	relationship	NOUN
cana-4790	36	13	between	between	ADP
cana-4790	36	14	eeg	eeg	NOUN
cana-4790	36	15	channels	channel	NOUN
cana-4790	36	16	by	by	ADP
cana-4790	36	17	means	mean	NOUN
cana-4790	36	18	of	of	ADP
cana-4790	36	19	local	local	ADJ
cana-4790	36	20	pattern	pattern	NOUN
cana-4790	36	21	learning	learning	NOUN
cana-4790	36	22	.	.	PUNCT
cana-4790	37	1	rnns	rnns	PROPN
cana-4790	37	2	,	,	PUNCT
cana-4790	37	3	particularly	particularly	ADV
cana-4790	37	4	long	long	ADJ
cana-4790	37	5	short	short	ADJ
cana-4790	37	6	-	-	PUNCT
cana-4790	37	7	term	term	NOUN
cana-4790	37	8	memory	memory	NOUN
cana-4790	37	9	(	(	PUNCT
cana-4790	37	10	lstm	lstm	NOUN
cana-4790	37	11	)	)	PUNCT
cana-4790	37	12	networks	network	NOUN
cana-4790	37	13	,	,	PUNCT
cana-4790	37	14	on	on	ADP
cana-4790	37	15	the	the	DET
cana-4790	37	16	other	other	ADJ
cana-4790	37	17	hand	hand	NOUN
cana-4790	37	18	,	,	PUNCT
cana-4790	37	19	excel	excel	VERB
cana-4790	37	20	in	in	ADP
cana-4790	37	21	demonstrating	demonstrate	VERB
cana-4790	37	22	how	how	SCONJ
cana-4790	37	23	data	datum	NOUN
cana-4790	37	24	evolves	evolve	VERB
cana-4790	37	25	over	over	ADP
cana-4790	37	26	time	time	NOUN
cana-4790	37	27	.	.	PUNCT
cana-4790	38	1	both	both	DET
cana-4790	38	2	designs	design	NOUN
cana-4790	38	3	have	have	VERB
cana-4790	38	4	their	their	PRON
cana-4790	38	5	own	own	ADJ
cana-4790	38	6	advantages	advantage	NOUN
cana-4790	38	7	,	,	PUNCT
cana-4790	38	8	but	but	CCONJ
cana-4790	38	9	eeg	eeg	NOUN
cana-4790	38	10	signals	signal	NOUN
cana-4790	38	11	inherently	inherently	ADV
cana-4790	38	12	display	display	VERB
cana-4790	38	13	temporal	temporal	ADJ
cana-4790	38	14	(	(	PUNCT
cana-4790	38	15	i.e.	i.e.	X
cana-4790	38	16	,	,	PUNCT
cana-4790	38	17	signal	signal	ADJ
cana-4790	38	18	changes	change	NOUN
cana-4790	38	19	over	over	ADP
cana-4790	38	20	time	time	NOUN
cana-4790	38	21	)	)	PUNCT
cana-4790	38	22	and	and	CCONJ
cana-4790	38	23	spatial	spatial	ADJ
cana-4790	38	24	(	(	PUNCT
cana-4790	38	25	i.e.	i.e.	X
cana-4790	38	26	,	,	PUNCT
cana-4790	38	27	channel	channel	NOUN
cana-4790	38	28	interactions	interaction	NOUN
cana-4790	38	29	)	)	PUNCT
cana-4790	38	30	characteristics	characteristic	NOUN
cana-4790	38	31	.	.	PUNCT
cana-4790	39	1	because	because	SCONJ
cana-4790	39	2	they	they	PRON
cana-4790	39	3	can	can	AUX
cana-4790	39	4	capture	capture	VERB
cana-4790	39	5	both	both	CCONJ
cana-4790	39	6	spatial	spatial	ADJ
cana-4790	39	7	and	and	CCONJ
cana-4790	39	8	temporal	temporal	ADJ
cana-4790	39	9	data	datum	NOUN
cana-4790	39	10	in	in	ADP
cana-4790	39	11	a	a	DET
cana-4790	39	12	single	single	ADJ
cana-4790	39	13	structure	structure	NOUN
cana-4790	39	14	,	,	PUNCT
cana-4790	39	15	hybrid	hybrid	NOUN
cana-4790	39	16	models	model	NOUN
cana-4790	39	17	combining	combine	VERB
cana-4790	39	18	cnn	cnn	PROPN
cana-4790	39	19	and	and	CCONJ
cana-4790	39	20	lstm	lstm	ADJ
cana-4790	39	21	layers	layer	NOUN
cana-4790	39	22	are	be	AUX
cana-4790	39	23	growingly	growingly	ADV
cana-4790	39	24	popular	popular	ADJ
cana-4790	39	25	.	.	PUNCT
cana-4790	40	1	cnn	cnn	PROPN
cana-4790	40	2	-	-	PUNCT
cana-4790	40	3	lstm	lstm	ADJ
cana-4790	40	4	mixed	mixed	ADJ
cana-4790	40	5	design	design	NOUN
cana-4790	40	6	has	have	AUX
cana-4790	40	7	been	be	AUX
cana-4790	40	8	used	use	VERB
cana-4790	40	9	successfully	successfully	ADV
cana-4790	40	10	in	in	ADP
cana-4790	40	11	many	many	ADJ
cana-4790	40	12	areas	area	NOUN
cana-4790	40	13	,	,	PUNCT
cana-4790	40	14	such	such	ADJ
cana-4790	40	15	as	as	ADP
cana-4790	40	16	recognising	recognise	VERB
cana-4790	40	17	speech	speech	NOUN
cana-4790	40	18	,	,	PUNCT
cana-4790	40	19	detecting	detect	VERB
cana-4790	40	20	emotions	emotion	NOUN
cana-4790	40	21	,	,	PUNCT
cana-4790	40	22	and	and	CCONJ
cana-4790	40	23	figuring	figure	VERB
cana-4790	40	24	out	out	ADP
cana-4790	40	25	the	the	DET
cana-4790	40	26	stage	stage	NOUN
cana-4790	40	27	of	of	ADP
cana-4790	40	28	sleep	sleep	NOUN
cana-4790	40	29	.	.	PUNCT
cana-4790	41	1	in	in	ADP
cana-4790	41	2	the	the	DET
cana-4790	41	3	case	case	NOUN
cana-4790	41	4	of	of	ADP
cana-4790	41	5	finding	find	VERB
cana-4790	41	6	ad	ad	NOUN
cana-4790	41	7	,	,	PUNCT
cana-4790	41	8	it	it	PRON
cana-4790	41	9	is	be	AUX
cana-4790	41	10	a	a	DET
cana-4790	41	11	great	great	ADJ
cana-4790	41	12	chance	chance	NOUN
cana-4790	41	13	to	to	PART
cana-4790	41	14	get	get	VERB
cana-4790	41	15	spatial	spatial	ADJ
cana-4790	41	16	patterns	pattern	NOUN
cana-4790	41	17	from	from	ADP
cana-4790	41	18	eeg	eeg	PROPN
cana-4790	41	19	channel	channel	NOUN
cana-4790	41	20	distributions	distribution	NOUN
cana-4790	41	21	and	and	CCONJ
cana-4790	41	22	model	model	VERB
cana-4790	41	23	how	how	SCONJ
cana-4790	41	24	brain	brain	NOUN
cana-4790	41	25	activity	activity	NOUN
cana-4790	41	26	changes	change	NOUN
cana-4790	41	27	over	over	ADP
cana-4790	41	28	time	time	NOUN
cana-4790	41	29	at	at	ADP
cana-4790	41	30	the	the	DET
cana-4790	41	31	same	same	ADJ
cana-4790	41	32	time	time	NOUN
cana-4790	41	33	.	.	PUNCT
cana-4790	42	1	although	although	SCONJ
cana-4790	42	2	it	it	PRON
cana-4790	42	3	has	have	VERB
cana-4790	42	4	a	a	DET
cana-4790	42	5	lot	lot	NOUN
cana-4790	42	6	of	of	ADP
cana-4790	42	7	promise	promise	NOUN
cana-4790	42	8	,	,	PUNCT
cana-4790	42	9	cnn	cnn	PROPN
cana-4790	42	10	-	-	PUNCT
cana-4790	42	11	lstm	lstm	ADJ
cana-4790	42	12	networks	network	NOUN
cana-4790	42	13	have	have	AUX
cana-4790	42	14	n't	not	PART
cana-4790	42	15	been	be	AUX
cana-4790	42	16	used	use	VERB
cana-4790	42	17	much	much	ADV
cana-4790	42	18	to	to	PART
cana-4790	42	19	find	find	VERB
cana-4790	42	20	ad	ad	NOUN
cana-4790	42	21	yet	yet	ADV
cana-4790	42	22	,	,	PUNCT
cana-4790	42	23	and	and	CCONJ
cana-4790	42	24	there	there	PRON
cana-4790	42	25	have	have	AUX
cana-4790	42	26	n't	not	PART
cana-4790	42	27	been	be	AUX
cana-4790	42	28	many	many	ADJ
cana-4790	42	29	studies	study	NOUN
cana-4790	42	30	that	that	PRON
cana-4790	42	31	look	look	VERB
cana-4790	42	32	at	at	ADP
cana-4790	42	33	both	both	CCONJ
cana-4790	42	34	spatial	spatial	ADJ
cana-4790	42	35	and	and	CCONJ
cana-4790	42	36	temporal	temporal	ADJ
cana-4790	42	37	eeg	eeg	NOUN
cana-4790	42	38	data	datum	NOUN
cana-4790	42	39	in	in	ADP
cana-4790	42	40	a	a	DET
cana-4790	42	41	complete	complete	ADJ
cana-4790	42	42	way	way	NOUN
cana-4790	42	43	.	.	PUNCT
cana-4790	43	1	this	this	DET
cana-4790	43	2	work	work	NOUN
cana-4790	43	3	aims	aim	VERB
cana-4790	43	4	to	to	PART
cana-4790	43	5	close	close	VERB
cana-4790	43	6	that	that	DET
cana-4790	43	7	gap	gap	NOUN
cana-4790	43	8	by	by	ADP
cana-4790	43	9	developing	develop	VERB
cana-4790	43	10	a	a	DET
cana-4790	43	11	cnn	cnn	PROPN
cana-4790	43	12	-	-	PUNCT
cana-4790	43	13	lstm	lstm	NOUN
cana-4790	43	14	-	-	PUNCT
cana-4790	43	15	based	base	VERB
cana-4790	43	16	deep	deep	ADJ
cana-4790	43	17	learning	learning	NOUN
cana-4790	43	18	model	model	NOUN
cana-4790	43	19	capable	capable	ADJ
cana-4790	43	20	of	of	ADP
cana-4790	43	21	autonomously	autonomously	ADV
cana-4790	43	22	detecting	detect	VERB
cana-4790	43	23	alzheimer	alzheimer	NOUN
cana-4790	43	24	's	's	PART
cana-4790	43	25	disease	disease	NOUN
cana-4790	43	26	using	use	VERB
cana-4790	43	27	eeg	eeg	PROPN
cana-4790	43	28	data	datum	NOUN
cana-4790	43	29	.	.	PUNCT
cana-4790	44	1	the	the	DET
cana-4790	44	2	proposed	propose	VERB
cana-4790	44	3	approach	approach	NOUN
cana-4790	44	4	achieves	achieve	VERB
cana-4790	44	5	this	this	PRON
cana-4790	44	6	by	by	ADP
cana-4790	44	7	using	use	VERB
cana-4790	44	8	cnn	cnn	PROPN
cana-4790	44	9	layers	layer	NOUN
cana-4790	44	10	'	'	PART
cana-4790	44	11	spatial	spatial	ADJ
cana-4790	44	12	filtering	filtering	NOUN
cana-4790	44	13	to	to	PART
cana-4790	44	14	process	process	VERB
cana-4790	44	15	raw	raw	ADJ
cana-4790	44	16	eeg	eeg	NOUN
cana-4790	44	17	data	datum	NOUN
cana-4790	44	18	from	from	ADP
cana-4790	44	19	many	many	ADJ
cana-4790	44	20	electrode	electrode	NOUN
cana-4790	44	21	locations	location	NOUN
cana-4790	44	22	and	and	CCONJ
cana-4790	44	23	lstm	lstm	NOUN
cana-4790	44	24	units	unit	NOUN
cana-4790	44	25	'	'	PART
cana-4790	44	26	temporal	temporal	ADJ
cana-4790	44	27	memory	memory	NOUN
cana-4790	44	28	to	to	PART
cana-4790	44	29	monitor	monitor	VERB
cana-4790	44	30	signal	signal	NOUN
cana-4790	44	31	changes	change	NOUN
cana-4790	44	32	over	over	ADP
cana-4790	44	33	time	time	NOUN
cana-4790	44	34	.	.	PUNCT
cana-4790	45	1	combining	combine	VERB
cana-4790	45	2	these	these	DET
cana-4790	45	3	two	two	NUM
cana-4790	45	4	components	component	NOUN
cana-4790	45	5	allows	allow	VERB
cana-4790	45	6	a	a	DET
cana-4790	45	7	robust	robust	ADJ
cana-4790	45	8	and	and	CCONJ
cana-4790	45	9	extendable	extendable	ADJ
cana-4790	45	10	model	model	NOUN
cana-4790	45	11	to	to	PART
cana-4790	45	12	learn	learn	VERB
cana-4790	45	13	complex	complex	ADJ
cana-4790	45	14	patterns	pattern	NOUN
cana-4790	45	15	indicating	indicate	VERB
cana-4790	45	16	cognitive	cognitive	ADJ
cana-4790	45	17	deterioration	deterioration	NOUN
cana-4790	45	18	without	without	ADP
cana-4790	45	19	requiring	require	VERB
cana-4790	45	20	many	many	ADJ
cana-4790	45	21	bespoke	bespoke	ADJ
cana-4790	45	22	characteristics	characteristic	NOUN
cana-4790	45	23	or	or	CCONJ
cana-4790	45	24	prior	prior	ADJ
cana-4790	45	25	knowledge	knowledge	NOUN
cana-4790	45	26	of	of	ADP
cana-4790	45	27	eeg	eeg	NOUN
cana-4790	45	28	indicators	indicator	NOUN
cana-4790	45	29	.	.	PUNCT
cana-4790	46	1	the	the	DET
cana-4790	46	2	objectives	objective	NOUN
cana-4790	46	3	of	of	ADP
cana-4790	46	4	this	this	DET
cana-4790	46	5	study	study	NOUN
cana-4790	46	6	are	be	AUX
cana-4790	46	7	as	as	SCONJ
cana-4790	46	8	follows	follow	VERB
cana-4790	46	9	:	:	PUNCT
cana-4790	46	10	1	1	X
cana-4790	46	11	.	.	PUNCT
cana-4790	46	12	to	to	PART
cana-4790	46	13	analyze	analyze	VERB
cana-4790	46	14	eeg	eeg	NOUN
cana-4790	46	15	recordings	recording	NOUN
cana-4790	46	16	from	from	ADP
cana-4790	46	17	individuals	individual	NOUN
cana-4790	46	18	with	with	ADP
cana-4790	46	19	ad	ad	NOUN
cana-4790	46	20	and	and	CCONJ
cana-4790	46	21	healthy	healthy	ADJ
cana-4790	46	22	controls	control	NOUN
cana-4790	46	23	and	and	CCONJ
cana-4790	46	24	identify	identify	VERB
cana-4790	46	25	discriminative	discriminative	NOUN
cana-4790	46	26	patterns	pattern	NOUN
cana-4790	46	27	across	across	ADP
cana-4790	46	28	time	time	NOUN
cana-4790	46	29	and	and	CCONJ
cana-4790	46	30	space	space	NOUN
cana-4790	46	31	.	.	PUNCT
cana-4790	47	1	2	2	X
cana-4790	47	2	.	.	PUNCT
cana-4790	47	3	to	to	PART
cana-4790	47	4	design	design	VERB
cana-4790	47	5	a	a	DET
cana-4790	47	6	cnn	cnn	PROPN
cana-4790	47	7	-	-	PUNCT
cana-4790	47	8	lstm	lstm	ADJ
cana-4790	47	9	hybrid	hybrid	ADJ
cana-4790	47	10	architecture	architecture	NOUN
cana-4790	47	11	capable	capable	ADJ
cana-4790	47	12	of	of	ADP
cana-4790	47	13	learning	learn	VERB
cana-4790	47	14	meaningful	meaningful	ADJ
cana-4790	47	15	spatial	spatial	ADJ
cana-4790	47	16	and	and	CCONJ
cana-4790	47	17	temporal	temporal	ADJ
cana-4790	47	18	representations	representation	NOUN
cana-4790	47	19	from	from	ADP
cana-4790	47	20	eeg	eeg	PROPN
cana-4790	47	21	data	datum	NOUN
cana-4790	47	22	.	.	PUNCT
cana-4790	48	1	3	3	X
cana-4790	48	2	.	.	PUNCT
cana-4790	48	3	to	to	PART
cana-4790	48	4	evaluate	evaluate	VERB
cana-4790	48	5	the	the	DET
cana-4790	48	6	performance	performance	NOUN
cana-4790	48	7	of	of	ADP
cana-4790	48	8	the	the	DET
cana-4790	48	9	proposed	propose	VERB
cana-4790	48	10	model	model	NOUN
cana-4790	48	11	against	against	ADP
cana-4790	48	12	traditional	traditional	ADJ
cana-4790	48	13	machine	machine	NOUN
cana-4790	48	14	learning	learn	VERB
cana-4790	48	15	classifiers	classifier	NOUN
cana-4790	48	16	and	and	CCONJ
cana-4790	48	17	individual	individual	ADJ
cana-4790	48	18	deep	deep	ADJ
cana-4790	48	19	learning	learning	NOUN
cana-4790	48	20	architectures	architecture	NOUN
cana-4790	48	21	(	(	PUNCT
cana-4790	48	22	i.e.	i.e.	X
cana-4790	48	23	,	,	PUNCT
cana-4790	48	24	standalone	standalone	ADJ
cana-4790	48	25	cnn	cnn	PROPN
cana-4790	48	26	and	and	CCONJ
cana-4790	48	27	lstm	lstm	NOUN
cana-4790	48	28	models	model	NOUN
cana-4790	48	29	)	)	PUNCT
cana-4790	48	30	.	.	PUNCT
cana-4790	49	1	4	4	X
cana-4790	49	2	.	.	PUNCT
cana-4790	49	3	to	to	PART
cana-4790	49	4	assess	assess	VERB
cana-4790	49	5	the	the	DET
cana-4790	49	6	feasibility	feasibility	NOUN
cana-4790	49	7	of	of	ADP
cana-4790	49	8	using	use	VERB
cana-4790	49	9	the	the	DET
cana-4790	49	10	model	model	NOUN
cana-4790	49	11	in	in	ADP
cana-4790	49	12	real	real	ADJ
cana-4790	49	13	-	-	PUNCT
cana-4790	49	14	time	time	NOUN
cana-4790	49	15	or	or	CCONJ
cana-4790	49	16	portable	portable	ADJ
cana-4790	49	17	eeg	eeg	NOUN
cana-4790	49	18	-	-	PUNCT
cana-4790	49	19	based	base	VERB
cana-4790	49	20	diagnostic	diagnostic	ADJ
cana-4790	49	21	systems	system	NOUN
cana-4790	49	22	for	for	ADP
cana-4790	49	23	clinical	clinical	ADJ
cana-4790	49	24	and	and	CCONJ
cana-4790	49	25	at	at	ADP
cana-4790	49	26	-	-	PUNCT
cana-4790	49	27	home	home	NOUN
cana-4790	49	28	use	use	NOUN
cana-4790	49	29	.	.	PUNCT
cana-4790	50	1	the	the	DET
cana-4790	50	2	following	following	NOUN
cana-4790	50	3	describes	describe	VERB
cana-4790	50	4	the	the	DET
cana-4790	50	5	organisation	organisation	NOUN
cana-4790	50	6	of	of	ADP
cana-4790	50	7	the	the	DET
cana-4790	50	8	remainder	remainder	NOUN
cana-4790	50	9	of	of	ADP
cana-4790	50	10	the	the	DET
cana-4790	50	11	article	article	NOUN
cana-4790	50	12	:	:	PUNCT
cana-4790	50	13	a	a	DET
cana-4790	50	14	great	great	ADJ
cana-4790	50	15	deal	deal	NOUN
cana-4790	50	16	of	of	ADP
cana-4790	50	17	connected	connected	ADJ
cana-4790	50	18	research	research	NOUN
cana-4790	50	19	in	in	ADP
cana-4790	50	20	eeg	eeg	PROPN
cana-4790	50	21	-	-	PUNCT
cana-4790	50	22	based	base	VERB
cana-4790	50	23	alzheimer	alzheimer	NOUN
cana-4790	50	24	's	's	PART
cana-4790	50	25	diagnosis	diagnosis	NOUN
cana-4790	50	26	and	and	CCONJ
cana-4790	50	27	deep	deep	ADJ
cana-4790	50	28	learning	learning	NOUN
cana-4790	50	29	applications	application	NOUN
cana-4790	50	30	in	in	ADP
cana-4790	50	31	eeg	eeg	PROPN
cana-4790	50	32	studies	study	NOUN
cana-4790	50	33	is	be	AUX
cana-4790	50	34	examined	examine	VERB
cana-4790	50	35	in	in	ADP
cana-4790	50	36	section	section	NOUN
cana-4790	50	37	2	2	NUM
cana-4790	50	38	.	.	PUNCT
cana-4790	51	1	the	the	DET
cana-4790	51	2	approach	approach	NOUN
cana-4790	51	3	is	be	AUX
cana-4790	51	4	covered	cover	VERB
cana-4790	51	5	in	in	ADP
cana-4790	51	6	further	further	ADJ
cana-4790	51	7	detail	detail	NOUN
cana-4790	51	8	in	in	ADP
cana-4790	51	9	section	section	NOUN
cana-4790	51	10	3	3	NUM
cana-4790	51	11	along	along	ADP
cana-4790	51	12	with	with	ADP
cana-4790	51	13	the	the	DET
cana-4790	51	14	dataset	dataset	NOUN
cana-4790	51	15	,	,	PUNCT
cana-4790	51	16	its	its	PRON
cana-4790	51	17	preparation	preparation	NOUN
cana-4790	51	18	methods	method	NOUN
cana-4790	51	19	,	,	PUNCT
cana-4790	51	20	the	the	DET
cana-4790	51	21	model	model	NOUN
cana-4790	51	22	's	's	PART
cana-4790	51	23	architecture	architecture	NOUN
cana-4790	51	24	,	,	PUNCT
cana-4790	51	25	and	and	CCONJ
cana-4790	51	26	the	the	DET
cana-4790	51	27	training	training	NOUN
cana-4790	51	28	procedures	procedure	NOUN
cana-4790	51	29	.	.	PUNCT
cana-4790	52	1	section	section	NOUN
cana-4790	52	2	4	4	NUM
cana-4790	52	3	discusses	discuss	VERB
cana-4790	52	4	the	the	DET
cana-4790	52	5	model	model	NOUN
cana-4790	52	6	's	's	PART
cana-4790	52	7	interpretability	interpretability	NOUN
cana-4790	52	8	and	and	CCONJ
cana-4790	52	9	limitations	limitation	NOUN
cana-4790	52	10	as	as	ADV
cana-4790	52	11	well	well	ADV
cana-4790	52	12	as	as	ADP
cana-4790	52	13	the	the	DET
cana-4790	52	14	findings	finding	NOUN
cana-4790	52	15	of	of	ADP
cana-4790	52	16	the	the	DET
cana-4790	52	17	experimental	experimental	ADJ
cana-4790	52	18	and	and	CCONJ
cana-4790	52	19	comparative	comparative	ADJ
cana-4790	52	20	research	research	NOUN
cana-4790	52	21	.	.	PUNCT
cana-4790	53	1	the	the	DET
cana-4790	53	2	study	study	NOUN
cana-4790	53	3	concludes	conclude	VERB
cana-4790	53	4	in	in	ADP
cana-4790	53	5	section	section	NOUN
cana-4790	53	6	5	5	NUM
cana-4790	53	7	with	with	ADP
cana-4790	53	8	a	a	DET
cana-4790	53	9	summary	summary	NOUN
cana-4790	53	10	of	of	ADP
cana-4790	53	11	its	its	PRON
cana-4790	53	12	findings	finding	NOUN
cana-4790	53	13	,	,	PUNCT
cana-4790	53	14	inputs	input	NOUN
cana-4790	53	15	,	,	PUNCT
cana-4790	53	16	and	and	CCONJ
cana-4790	53	17	potential	potential	ADJ
cana-4790	53	18	future	future	ADJ
cana-4790	53	19	research	research	NOUN
cana-4790	53	20	directions	direction	NOUN
cana-4790	53	21	.	.	PUNCT
cana-4790	54	1	this	this	DET
cana-4790	54	2	study	study	NOUN
cana-4790	54	3	adds	add	VERB
cana-4790	54	4	to	to	ADP
cana-4790	54	5	the	the	DET
cana-4790	54	6	growing	grow	VERB
cana-4790	54	7	amount	amount	NOUN
cana-4790	54	8	of	of	ADP
cana-4790	54	9	work	work	NOUN
cana-4790	54	10	on	on	ADP
cana-4790	54	11	ai	ai	ADJ
cana-4790	54	12	-	-	PUNCT
cana-4790	54	13	assisted	assist	VERB
cana-4790	54	14	healthcare	healthcare	NOUN
cana-4790	54	15	diagnostics	diagnostic	NOUN
cana-4790	54	16	by	by	ADP
cana-4790	54	17	creating	create	VERB
cana-4790	54	18	and	and	CCONJ
cana-4790	54	19	proving	prove	VERB
cana-4790	54	20	an	an	DET
cana-4790	54	21	end	end	NOUN
cana-4790	54	22	-	-	PUNCT
cana-4790	54	23	to	to	ADP
cana-4790	54	24	-	-	PUNCT
cana-4790	54	25	end	end	NOUN
cana-4790	54	26	cnn	cnn	PROPN
cana-4790	54	27	-	-	PUNCT
cana-4790	54	28	lstm	lstm	ADJ
cana-4790	54	29	system	system	NOUN
cana-4790	54	30	for	for	ADP
cana-4790	54	31	alzheimer	alzheimer	PROPN
cana-4790	54	32	's	's	PART
cana-4790	54	33	diagnosis	diagnosis	NOUN
cana-4790	54	34	.	.	PUNCT
cana-4790	55	1	more	more	ADV
cana-4790	55	2	importantly	importantly	ADV
cana-4790	55	3	,	,	PUNCT
cana-4790	55	4	it	it	PRON
cana-4790	55	5	provides	provide	VERB
cana-4790	55	6	a	a	DET
cana-4790	55	7	flexible	flexible	ADJ
cana-4790	55	8	and	and	CCONJ
cana-4790	55	9	easy	easy	ADJ
cana-4790	55	10	-	-	PUNCT
cana-4790	55	11	touse	touse	NOUN
cana-4790	55	12	way	way	NOUN
cana-4790	55	13	for	for	SCONJ
cana-4790	55	14	doctors	doctor	NOUN
cana-4790	55	15	to	to	PART
cana-4790	55	16	help	help	VERB
cana-4790	55	17	find	find	VERB
cana-4790	55	18	alzheimer	alzheimer	PROPN
cana-4790	55	19	's	's	PART
cana-4790	55	20	disease	disease	NOUN
cana-4790	55	21	early	early	ADV
cana-4790	55	22	and	and	CCONJ
cana-4790	55	23	correctly	correctly	ADV
cana-4790	55	24	,	,	PUNCT
cana-4790	55	25	which	which	PRON
cana-4790	55	26	allows	allow	VERB
cana-4790	55	27	for	for	ADP
cana-4790	55	28	early	early	ADJ
cana-4790	55	29	treatment	treatment	NOUN
cana-4790	55	30	and	and	CCONJ
cana-4790	55	31	better	well	ADJ
cana-4790	55	32	quality	quality	NOUN
cana-4790	55	33	of	of	ADP
cana-4790	55	34	life	life	NOUN
cana-4790	55	35	for	for	ADP
cana-4790	55	36	both	both	DET
cana-4790	55	37	patients	patient	NOUN
cana-4790	55	38	and	and	CCONJ
cana-4790	55	39	their	their	PRON
cana-4790	55	40	caretakers	caretaker	NOUN
cana-4790	55	41	.	.	PUNCT
cana-4790	56	1	significance	significance	NOUN
cana-4790	56	2	of	of	ADP
cana-4790	56	3	the	the	DET
cana-4790	56	4	study	study	NOUN
cana-4790	56	5	it	it	PRON
cana-4790	56	6	is	be	AUX
cana-4790	56	7	very	very	ADV
cana-4790	56	8	important	important	ADJ
cana-4790	56	9	to	to	PART
cana-4790	56	10	find	find	VERB
cana-4790	56	11	alzheimer	alzheimer	PROPN
cana-4790	56	12	's	's	PART
cana-4790	56	13	disease	disease	NOUN
cana-4790	56	14	early	early	ADV
cana-4790	56	15	so	so	SCONJ
cana-4790	56	16	that	that	SCONJ
cana-4790	56	17	patients	patient	NOUN
cana-4790	56	18	can	can	AUX
cana-4790	56	19	get	get	VERB
cana-4790	56	20	the	the	DET
cana-4790	56	21	right	right	ADJ
cana-4790	56	22	treatment	treatment	NOUN
cana-4790	56	23	,	,	PUNCT
cana-4790	56	24	slow	slow	VERB
cana-4790	56	25	the	the	DET
cana-4790	56	26	disease	disease	NOUN
cana-4790	56	27	's	's	PART
cana-4790	56	28	progression	progression	NOUN
cana-4790	56	29	,	,	PUNCT
cana-4790	56	30	and	and	CCONJ
cana-4790	56	31	possibly	possibly	ADV
cana-4790	56	32	take	take	VERB
cana-4790	56	33	part	part	NOUN
cana-4790	56	34	in	in	ADP
cana-4790	56	35	clinical	clinical	ADJ
cana-4790	56	36	trials	trial	NOUN
cana-4790	56	37	that	that	PRON
cana-4790	56	38	will	will	AUX
cana-4790	56	39	improve	improve	VERB
cana-4790	56	40	their	their	PRON
cana-4790	56	41	long	long	ADJ
cana-4790	56	42	-	-	PUNCT
cana-4790	56	43	term	term	NOUN
cana-4790	56	44	outcomes	outcome	NOUN
cana-4790	56	45	.	.	PUNCT
cana-4790	57	1	however	however	ADV
cana-4790	57	2	,	,	PUNCT
cana-4790	57	3	many	many	ADJ
cana-4790	57	4	people	people	NOUN
cana-4790	57	5	ca	can	AUX
cana-4790	57	6	n't	not	PART
cana-4790	57	7	use	use	VERB
cana-4790	57	8	the	the	DET
cana-4790	57	9	current	current	ADJ
cana-4790	57	10	diagnostic	diagnostic	ADJ
cana-4790	57	11	methods	method	NOUN
cana-4790	57	12	because	because	SCONJ
cana-4790	57	13	they	they	PRON
cana-4790	57	14	are	be	AUX
cana-4790	57	15	too	too	ADV
cana-4790	57	16	expensive	expensive	ADJ
cana-4790	57	17	,	,	PUNCT
cana-4790	57	18	hard	hard	ADJ
cana-4790	57	19	to	to	PART
cana-4790	57	20	get	get	VERB
cana-4790	57	21	to	to	ADP
cana-4790	57	22	,	,	PUNCT
cana-4790	57	23	or	or	CCONJ
cana-4790	57	24	do	do	AUX
cana-4790	57	25	n't	not	PART
cana-4790	57	26	have	have	VERB
cana-4790	57	27	the	the	DET
cana-4790	57	28	right	right	ADJ
cana-4790	57	29	facilities	facility	NOUN
cana-4790	57	30	.	.	PUNCT
cana-4790	58	1	using	use	VERB
cana-4790	58	2	eeg	eeg	NOUN
cana-4790	58	3	,	,	PUNCT
cana-4790	58	4	a	a	DET
cana-4790	58	5	simple	simple	ADJ
cana-4790	58	6	technique	technique	NOUN
cana-4790	58	7	that	that	PRON
cana-4790	58	8	can	can	AUX
cana-4790	58	9	be	be	AUX
cana-4790	58	10	tracked	track	VERB
cana-4790	58	11	from	from	ADP
cana-4790	58	12	home	home	NOUN
cana-4790	58	13	or	or	CCONJ
cana-4790	58	14	on	on	ADP
cana-4790	58	15	the	the	DET
cana-4790	58	16	move	move	NOUN
cana-4790	58	17	,	,	PUNCT
cana-4790	58	18	this	this	DET
cana-4790	58	19	research	research	NOUN
cana-4790	58	20	proposes	propose	VERB
cana-4790	58	21	a	a	DET
cana-4790	58	22	different	different	ADJ
cana-4790	58	23	approach	approach	NOUN
cana-4790	58	24	to	to	PART
cana-4790	58	25	identify	identify	VERB
cana-4790	58	26	early	early	ADJ
cana-4790	58	27	indicators	indicator	NOUN
cana-4790	58	28	of	of	ADP
cana-4790	58	29	ad	ad	NOUN
cana-4790	58	30	.	.	PUNCT
cana-4790	59	1	by	by	ADP
cana-4790	59	2	using	use	VERB
cana-4790	59	3	deep	deep	ADJ
cana-4790	59	4	learning	learning	NOUN
cana-4790	59	5	models	model	NOUN
cana-4790	59	6	that	that	PRON
cana-4790	59	7	need	need	VERB
cana-4790	59	8	little	little	ADJ
cana-4790	59	9	preparation	preparation	NOUN
cana-4790	59	10	and	and	CCONJ
cana-4790	59	11	can	can	AUX
cana-4790	59	12	adjust	adjust	VERB
cana-4790	59	13	to	to	ADP
cana-4790	59	14	many	many	ADJ
cana-4790	59	15	input	input	NOUN
cana-4790	59	16	factors	factor	NOUN
cana-4790	59	17	,	,	PUNCT
cana-4790	59	18	this	this	DET
cana-4790	59	19	work	work	NOUN
cana-4790	59	20	opens	open	VERB
cana-4790	59	21	the	the	DET
cana-4790	59	22	door	door	NOUN
cana-4790	59	23	for	for	ADP
cana-4790	59	24	more	more	ADV
cana-4790	59	25	open	open	ADJ
cana-4790	59	26	and	and	CCONJ
cana-4790	59	27	decentralised	decentralise	VERB
cana-4790	59	28	diagnostic	diagnostic	ADJ
cana-4790	59	29	tools	tool	NOUN
cana-4790	59	30	.	.	PUNCT
cana-4790	60	1	this	this	DET
cana-4790	60	2	work	work	NOUN
cana-4790	60	3	also	also	ADV
cana-4790	60	4	helps	help	VERB
cana-4790	60	5	to	to	PART
cana-4790	60	6	be	be	AUX
cana-4790	60	7	able	able	ADJ
cana-4790	60	8	to	to	PART
cana-4790	60	9	describe	describe	VERB
cana-4790	60	10	and	and	CCONJ
cana-4790	60	11	clinically	clinically	ADV
cana-4790	60	12	assess	assess	ADJ
cana-4790	60	13	.	.	PUNCT
cana-4790	61	1	many	many	ADJ
cana-4790	61	2	claim	claim	VERB
cana-4790	61	3	that	that	SCONJ
cana-4790	61	4	deep	deep	ADJ
cana-4790	61	5	learning	learning	NOUN
cana-4790	61	6	models	model	NOUN
cana-4790	61	7	are	be	AUX
cana-4790	61	8	like	like	ADP
cana-4790	61	9	"	"	PUNCT
cana-4790	61	10	black	black	ADJ
cana-4790	61	11	boxes	box	NOUN
cana-4790	61	12	,	,	PUNCT
cana-4790	61	13	"	"	PUNCT
cana-4790	61	14	however	however	ADV
cana-4790	61	15	we	we	PRON
cana-4790	61	16	have	have	AUX
cana-4790	61	17	included	include	VERB
cana-4790	61	18	techniques	technique	NOUN
cana-4790	61	19	to	to	PART
cana-4790	61	20	see	see	VERB
cana-4790	61	21	and	and	CCONJ
cana-4790	61	22	comprehend	comprehend	VERB
cana-4790	61	23	the	the	DET
cana-4790	61	24	spatial	spatial	ADJ
cana-4790	61	25	and	and	CCONJ
cana-4790	61	26	temporal	temporal	ADJ
cana-4790	61	27	components	component	NOUN
cana-4790	61	28	that	that	PRON
cana-4790	61	29	were	be	AUX
cana-4790	61	30	acquired	acquire	VERB
cana-4790	61	31	.	.	PUNCT
cana-4790	62	1	this	this	PRON
cana-4790	62	2	increases	increase	VERB
cana-4790	62	3	the	the	DET
cana-4790	62	4	dependability	dependability	NOUN
cana-4790	62	5	and	and	CCONJ
cana-4790	62	6	applicability	applicability	NOUN
cana-4790	62	7	of	of	ADP
cana-4790	62	8	the	the	DET
cana-4790	62	9	findings	finding	NOUN
cana-4790	62	10	for	for	ADP
cana-4790	62	11	medical	medical	ADJ
cana-4790	62	12	professionals	professional	NOUN
cana-4790	62	13	.	.	PUNCT
cana-4790	63	1	communications	communication	NOUN
cana-4790	63	2	on	on	ADP
cana-4790	63	3	applied	apply	VERB
cana-4790	63	4	nonlinear	nonlinear	ADJ
cana-4790	63	5	analysis	analysis	NOUN
cana-4790	63	6	issn	issn	NOUN
cana-4790	63	7	:	:	PUNCT
cana-4790	63	8	1074	1074	NUM
cana-4790	63	9	-	-	PUNCT
cana-4790	63	10	133x	133x	NUM
cana-4790	63	11	vol	vol	NOUN
cana-4790	63	12	31	31	NUM
cana-4790	63	13	no	no	NOUN
cana-4790	63	14	.	.	PUNCT
cana-4790	64	1	1s	1s	NUM
cana-4790	64	2	(	(	PUNCT
cana-4790	64	3	2024	2024	NUM
cana-4790	64	4	)	)	PUNCT
cana-4790	64	5	202	202	NUM
cana-4790	64	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	64	7	2	2	X
cana-4790	64	8	.	.	X
cana-4790	64	9	literature	literature	NOUN
cana-4790	64	10	review	review	NOUN
cana-4790	64	11	for	for	ADP
cana-4790	64	12	those	those	PRON
cana-4790	64	13	with	with	ADP
cana-4790	64	14	alzheimer	alzheimer	PROPN
cana-4790	64	15	's	's	PART
cana-4790	64	16	disease	disease	NOUN
cana-4790	64	17	(	(	PUNCT
cana-4790	64	18	ad	ad	NOUN
cana-4790	64	19	)	)	PUNCT
cana-4790	64	20	and	and	CCONJ
cana-4790	64	21	other	other	ADJ
cana-4790	64	22	neurological	neurological	ADJ
cana-4790	64	23	disorders	disorder	NOUN
cana-4790	64	24	,	,	PUNCT
cana-4790	64	25	electroencephalography	electroencephalography	NOUN
cana-4790	64	26	(	(	PUNCT
cana-4790	64	27	eeg	eeg	NOUN
cana-4790	64	28	)	)	PUNCT
cana-4790	64	29	has	have	AUX
cana-4790	64	30	emerged	emerge	VERB
cana-4790	64	31	as	as	ADP
cana-4790	64	32	a	a	DET
cana-4790	64	33	safe	safe	ADJ
cana-4790	64	34	,	,	PUNCT
cana-4790	64	35	non	non	ADJ
cana-4790	64	36	-	-	ADJ
cana-4790	64	37	invasive	invasive	ADJ
cana-4790	64	38	,	,	PUNCT
cana-4790	64	39	and	and	CCONJ
cana-4790	64	40	affordable	affordable	ADJ
cana-4790	64	41	method	method	NOUN
cana-4790	64	42	to	to	PART
cana-4790	64	43	examine	examine	VERB
cana-4790	64	44	brain	brain	NOUN
cana-4790	64	45	activity	activity	NOUN
cana-4790	64	46	.	.	PUNCT
cana-4790	65	1	many	many	ADJ
cana-4790	65	2	studies	study	NOUN
cana-4790	65	3	have	have	AUX
cana-4790	65	4	shown	show	VERB
cana-4790	65	5	that	that	SCONJ
cana-4790	65	6	eeg	eeg	NOUN
cana-4790	65	7	patterns	pattern	NOUN
cana-4790	65	8	in	in	ADP
cana-4790	65	9	those	those	PRON
cana-4790	65	10	with	with	ADP
cana-4790	65	11	ad	ad	NOUN
cana-4790	65	12	vary	vary	VERB
cana-4790	65	13	greatly	greatly	ADV
cana-4790	65	14	,	,	PUNCT
cana-4790	65	15	particularly	particularly	ADV
cana-4790	65	16	in	in	ADP
cana-4790	65	17	relation	relation	NOUN
cana-4790	65	18	to	to	ADP
cana-4790	65	19	spectral	spectral	ADJ
cana-4790	65	20	composition	composition	NOUN
cana-4790	65	21	and	and	CCONJ
cana-4790	65	22	connectivity	connectivity	NOUN
cana-4790	65	23	.	.	PUNCT
cana-4790	66	1	most	most	ADV
cana-4790	66	2	often	often	ADV
cana-4790	66	3	,	,	PUNCT
cana-4790	66	4	brain	brain	NOUN
cana-4790	66	5	rhythms	rhythm	NOUN
cana-4790	66	6	are	be	AUX
cana-4790	66	7	slowing	slow	VERB
cana-4790	66	8	down	down	ADV
cana-4790	66	9	everywhere	everywhere	ADV
cana-4790	66	10	.	.	PUNCT
cana-4790	67	1	more	more	ADJ
cana-4790	67	2	delta	delta	NOUN
cana-4790	67	3	(	(	PUNCT
cana-4790	67	4	0.5–4	0.5–4	NOUN
cana-4790	67	5	hz	hz	VERB
cana-4790	67	6	)	)	PUNCT
cana-4790	67	7	and	and	CCONJ
cana-4790	67	8	theta	theta	NOUN
cana-4790	67	9	(	(	PUNCT
cana-4790	67	10	4–8	4–8	NUM
cana-4790	67	11	hz	hz	ADJ
cana-4790	67	12	)	)	PUNCT
cana-4790	67	13	band	band	NOUN
cana-4790	67	14	power	power	NOUN
cana-4790	67	15	as	as	ADV
cana-4790	67	16	well	well	ADV
cana-4790	67	17	as	as	ADP
cana-4790	67	18	reduced	reduce	VERB
cana-4790	67	19	alpha	alpha	NOUN
cana-4790	67	20	(	(	PUNCT
cana-4790	67	21	8–13	8–13	NOUN
cana-4790	67	22	hz	hz	VERB
cana-4790	67	23	)	)	PUNCT
cana-4790	67	24	and	and	CCONJ
cana-4790	67	25	beta	beta	NOUN
cana-4790	67	26	(	(	PUNCT
cana-4790	67	27	13–30	13–30	NUM
cana-4790	67	28	hz	hz	ADJ
cana-4790	67	29	)	)	PUNCT
cana-4790	67	30	band	band	NOUN
cana-4790	67	31	power	power	NOUN
cana-4790	67	32	indicate	indicate	VERB
cana-4790	67	33	this	this	PRON
cana-4790	67	34	[	[	X
cana-4790	67	35	1	1	NUM
cana-4790	67	36	,	,	PUNCT
cana-4790	67	37	2	2	NUM
cana-4790	67	38	]	]	PUNCT
cana-4790	67	39	.	.	PUNCT
cana-4790	68	1	these	these	DET
cana-4790	68	2	alterations	alteration	NOUN
cana-4790	68	3	are	be	AUX
cana-4790	68	4	linked	link	VERB
cana-4790	68	5	to	to	ADP
cana-4790	68	6	ageing	age	VERB
cana-4790	68	7	in	in	ADP
cana-4790	68	8	the	the	DET
cana-4790	68	9	areas	area	NOUN
cana-4790	68	10	of	of	ADP
cana-4790	68	11	the	the	DET
cana-4790	68	12	brain	brain	NOUN
cana-4790	68	13	processing	processing	NOUN
cana-4790	68	14	information	information	NOUN
cana-4790	68	15	—	—	PUNCT
cana-4790	68	16	cortical	cortical	ADJ
cana-4790	68	17	and	and	CCONJ
cana-4790	68	18	subcortical	subcortical	ADJ
cana-4790	68	19	.	.	PUNCT
cana-4790	69	1	ad	ad	NOUN
cana-4790	69	2	groups	group	NOUN
cana-4790	69	3	show	show	VERB
cana-4790	69	4	decreasing	decrease	VERB
cana-4790	69	5	eeg	eeg	NOUN
cana-4790	69	6	complexity	complexity	NOUN
cana-4790	69	7	,	,	PUNCT
cana-4790	69	8	entropy	entropy	NOUN
cana-4790	69	9	,	,	PUNCT
cana-4790	69	10	and	and	CCONJ
cana-4790	69	11	inter	inter	ADJ
cana-4790	69	12	-	-	ADJ
cana-4790	69	13	hemispheric	hemispheric	ADJ
cana-4790	69	14	coherence	coherence	NOUN
cana-4790	69	15	[	[	X
cana-4790	69	16	3	3	NUM
cana-4790	69	17	,	,	PUNCT
cana-4790	69	18	4	4	NUM
cana-4790	69	19	]	]	PUNCT
cana-4790	69	20	.	.	PUNCT
cana-4790	70	1	apart	apart	ADV
cana-4790	70	2	from	from	ADP
cana-4790	70	3	variations	variation	NOUN
cana-4790	70	4	in	in	ADP
cana-4790	70	5	spectral	spectral	ADJ
cana-4790	70	6	patterns	pattern	NOUN
cana-4790	70	7	,	,	PUNCT
cana-4790	70	8	this	this	PRON
cana-4790	70	9	is	be	AUX
cana-4790	70	10	.	.	PUNCT
cana-4790	71	1	a	a	DET
cana-4790	71	2	major	major	ADJ
cana-4790	71	3	indicator	indicator	NOUN
cana-4790	71	4	of	of	ADP
cana-4790	71	5	growing	grow	VERB
cana-4790	71	6	cognitive	cognitive	ADJ
cana-4790	71	7	impairment	impairment	NOUN
cana-4790	71	8	is	be	AUX
cana-4790	71	9	the	the	DET
cana-4790	71	10	brain	brain	NOUN
cana-4790	71	11	's	's	PART
cana-4790	71	12	declining	decline	VERB
cana-4790	71	13	functional	functional	ADJ
cana-4790	71	14	unity	unity	NOUN
cana-4790	71	15	and	and	CCONJ
cana-4790	71	16	balance	balance	NOUN
cana-4790	71	17	.	.	PUNCT
cana-4790	72	1	eeg	eeg	PROPN
cana-4790	72	2	is	be	AUX
cana-4790	72	3	a	a	DET
cana-4790	72	4	great	great	ADJ
cana-4790	72	5	tool	tool	NOUN
cana-4790	72	6	for	for	ADP
cana-4790	72	7	automated	automate	VERB
cana-4790	72	8	detection	detection	NOUN
cana-4790	72	9	systems	system	NOUN
cana-4790	72	10	as	as	SCONJ
cana-4790	72	11	it	it	PRON
cana-4790	72	12	can	can	AUX
cana-4790	72	13	capture	capture	VERB
cana-4790	72	14	changes	change	NOUN
cana-4790	72	15	in	in	ADP
cana-4790	72	16	time	time	NOUN
cana-4790	72	17	as	as	ADV
cana-4790	72	18	well	well	ADV
cana-4790	72	19	as	as	ADP
cana-4790	72	20	changes	change	NOUN
cana-4790	72	21	in	in	ADP
cana-4790	72	22	place	place	NOUN
cana-4790	72	23	.	.	PUNCT
cana-4790	73	1	however	however	ADV
cana-4790	73	2	,	,	PUNCT
cana-4790	73	3	conventional	conventional	ADJ
cana-4790	73	4	techniques	technique	NOUN
cana-4790	73	5	are	be	AUX
cana-4790	73	6	less	less	ADV
cana-4790	73	7	effective	effective	ADJ
cana-4790	73	8	and	and	CCONJ
cana-4790	73	9	scalable	scalable	ADJ
cana-4790	73	10	in	in	ADP
cana-4790	73	11	clinical	clinical	ADJ
cana-4790	73	12	environments	environment	NOUN
cana-4790	73	13	as	as	SCONJ
cana-4790	73	14	they	they	PRON
cana-4790	73	15	rely	rely	VERB
cana-4790	73	16	on	on	ADP
cana-4790	73	17	topic	topic	NOUN
cana-4790	73	18	expertise	expertise	NOUN
cana-4790	73	19	and	and	CCONJ
cana-4790	73	20	manual	manual	ADJ
cana-4790	73	21	feature	feature	NOUN
cana-4790	73	22	extraction	extraction	NOUN
cana-4790	73	23	.	.	PUNCT
cana-4790	74	1	before	before	ADV
cana-4790	74	2	,	,	PUNCT
cana-4790	74	3	most	most	ADJ
cana-4790	74	4	of	of	ADP
cana-4790	74	5	the	the	DET
cana-4790	74	6	methods	method	NOUN
cana-4790	74	7	used	use	VERB
cana-4790	74	8	to	to	PART
cana-4790	74	9	find	find	VERB
cana-4790	74	10	ad	ad	NOUN
cana-4790	74	11	using	use	VERB
cana-4790	74	12	eeg	eeg	NOUN
cana-4790	74	13	were	be	AUX
cana-4790	74	14	basic	basic	ADJ
cana-4790	74	15	machine	machine	NOUN
cana-4790	74	16	learning	learn	VERB
cana-4790	74	17	algorithms	algorithm	NOUN
cana-4790	74	18	,	,	PUNCT
cana-4790	74	19	such	such	ADJ
cana-4790	74	20	as	as	ADP
cana-4790	74	21	support	support	NOUN
cana-4790	74	22	vector	vector	NOUN
cana-4790	74	23	machines	machine	NOUN
cana-4790	74	24	(	(	PUNCT
cana-4790	74	25	svm	svm	PROPN
cana-4790	74	26	)	)	PUNCT
cana-4790	74	27	,	,	PUNCT
cana-4790	74	28	k	k	X
cana-4790	74	29	-	-	PUNCT
cana-4790	74	30	nearest	near	ADJ
cana-4790	74	31	neighbours	neighbour	NOUN
cana-4790	74	32	(	(	PUNCT
cana-4790	74	33	k	k	NOUN
cana-4790	74	34	-	-	PUNCT
cana-4790	74	35	nn	nn	NOUN
cana-4790	74	36	)	)	PUNCT
cana-4790	74	37	,	,	PUNCT
cana-4790	74	38	random	random	ADJ
cana-4790	74	39	forest	forest	NOUN
cana-4790	74	40	(	(	PUNCT
cana-4790	74	41	rf	rf	NOUN
cana-4790	74	42	)	)	PUNCT
cana-4790	74	43	,	,	PUNCT
cana-4790	74	44	and	and	CCONJ
cana-4790	74	45	decision	decision	NOUN
cana-4790	74	46	trees	tree	NOUN
cana-4790	74	47	.	.	PUNCT
cana-4790	75	1	most	most	ADJ
cana-4790	75	2	of	of	ADP
cana-4790	75	3	the	the	DET
cana-4790	75	4	time	time	NOUN
cana-4790	75	5	,	,	PUNCT
cana-4790	75	6	these	these	DET
cana-4790	75	7	models	model	NOUN
cana-4790	75	8	were	be	AUX
cana-4790	75	9	taught	teach	VERB
cana-4790	75	10	using	use	VERB
cana-4790	75	11	custom	custom	NOUN
cana-4790	75	12	features	feature	NOUN
cana-4790	75	13	made	make	VERB
cana-4790	75	14	by	by	ADP
cana-4790	75	15	hand	hand	NOUN
cana-4790	75	16	,	,	PUNCT
cana-4790	75	17	like	like	ADP
cana-4790	75	18	spectral	spectral	ADJ
cana-4790	75	19	power	power	NOUN
cana-4790	75	20	,	,	PUNCT
cana-4790	75	21	entropy	entropy	NOUN
cana-4790	75	22	measures	measure	NOUN
cana-4790	75	23	,	,	PUNCT
cana-4790	75	24	and	and	CCONJ
cana-4790	75	25	wavelet	wavelet	NOUN
cana-4790	75	26	coefficients	coefficient	NOUN
cana-4790	75	27	from	from	ADP
cana-4790	75	28	eeg	eeg	NOUN
cana-4790	75	29	signals	signal	NOUN
cana-4790	75	30	[	[	X
cana-4790	75	31	5	5	NUM
cana-4790	75	32	,	,	PUNCT
cana-4790	75	33	6	6	NUM
cana-4790	75	34	]	]	PUNCT
cana-4790	75	35	.	.	PUNCT
cana-4790	76	1	for	for	ADP
cana-4790	76	2	example	example	NOUN
cana-4790	76	3	,	,	PUNCT
cana-4790	76	4	traits	trait	NOUN
cana-4790	76	5	from	from	ADP
cana-4790	76	6	certain	certain	ADJ
cana-4790	76	7	frequency	frequency	NOUN
cana-4790	76	8	bands	band	NOUN
cana-4790	76	9	were	be	AUX
cana-4790	76	10	used	use	VERB
cana-4790	76	11	to	to	PART
cana-4790	76	12	tell	tell	VERB
cana-4790	76	13	the	the	DET
cana-4790	76	14	difference	difference	NOUN
cana-4790	76	15	between	between	ADP
cana-4790	76	16	the	the	DET
cana-4790	76	17	groups	group	NOUN
cana-4790	76	18	of	of	ADP
cana-4790	76	19	healthy	healthy	ADJ
cana-4790	76	20	controls	control	NOUN
cana-4790	76	21	,	,	PUNCT
cana-4790	76	22	people	people	NOUN
cana-4790	76	23	with	with	ADP
cana-4790	76	24	mci	mci	PROPN
cana-4790	76	25	,	,	PUNCT
cana-4790	76	26	and	and	CCONJ
cana-4790	76	27	people	people	NOUN
cana-4790	76	28	with	with	ADP
cana-4790	76	29	ad	ad	NOUN
cana-4790	76	30	.	.	PUNCT
cana-4790	77	1	although	although	SCONJ
cana-4790	77	2	these	these	DET
cana-4790	77	3	techniques	technique	NOUN
cana-4790	77	4	were	be	AUX
cana-4790	77	5	quite	quite	ADV
cana-4790	77	6	accurate	accurate	ADJ
cana-4790	77	7	,	,	PUNCT
cana-4790	77	8	they	they	PRON
cana-4790	77	9	often	often	ADV
cana-4790	77	10	struggled	struggle	VERB
cana-4790	77	11	with	with	ADP
cana-4790	77	12	overfitting	overfitte	VERB
cana-4790	77	13	and	and	CCONJ
cana-4790	77	14	poor	poor	ADJ
cana-4790	77	15	generalisation	generalisation	NOUN
cana-4790	77	16	as	as	SCONJ
cana-4790	77	17	they	they	PRON
cana-4790	77	18	depended	depend	VERB
cana-4790	77	19	on	on	ADP
cana-4790	77	20	qualities	quality	NOUN
cana-4790	77	21	experts	expert	NOUN
cana-4790	77	22	specified	specify	VERB
cana-4790	77	23	.	.	PUNCT
cana-4790	78	1	they	they	PRON
cana-4790	78	2	also	also	ADV
cana-4790	78	3	failed	fail	VERB
cana-4790	78	4	to	to	PART
cana-4790	78	5	grasp	grasp	VERB
cana-4790	78	6	the	the	DET
cana-4790	78	7	intricate	intricate	ADJ
cana-4790	78	8	spatial	spatial	ADJ
cana-4790	78	9	patterns	pattern	NOUN
cana-4790	78	10	and	and	CCONJ
cana-4790	78	11	hierarchical	hierarchical	ADJ
cana-4790	78	12	representations	representation	NOUN
cana-4790	78	13	seen	see	VERB
cana-4790	78	14	in	in	ADP
cana-4790	78	15	eeg	eeg	PROPN
cana-4790	78	16	data	datum	NOUN
cana-4790	78	17	.	.	PUNCT
cana-4790	79	1	this	this	PRON
cana-4790	79	2	resulted	result	VERB
cana-4790	79	3	in	in	ADP
cana-4790	79	4	a	a	DET
cana-4790	79	5	shift	shift	NOUN
cana-4790	79	6	towards	towards	ADP
cana-4790	79	7	deep	deep	ADJ
cana-4790	79	8	learning	learning	NOUN
cana-4790	79	9	models	model	NOUN
cana-4790	79	10	capable	capable	ADJ
cana-4790	79	11	of	of	ADP
cana-4790	79	12	directly	directly	ADV
cana-4790	79	13	learning	learn	VERB
cana-4790	79	14	from	from	ADP
cana-4790	79	15	raw	raw	ADJ
cana-4790	79	16	data	datum	NOUN
cana-4790	79	17	or	or	CCONJ
cana-4790	79	18	signals	signal	NOUN
cana-4790	79	19	that	that	PRON
cana-4790	79	20	have	have	AUX
cana-4790	79	21	been	be	AUX
cana-4790	79	22	marginally	marginally	ADV
cana-4790	79	23	preprocessed	preprocesse	VERB
cana-4790	79	24	[	[	X
cana-4790	79	25	7	7	NUM
cana-4790	79	26	]	]	PUNCT
cana-4790	79	27	.	.	PUNCT
cana-4790	80	1	deep	deep	ADJ
cana-4790	80	2	learning	learning	NOUN
cana-4790	80	3	has	have	AUX
cana-4790	80	4	transformed	transform	VERB
cana-4790	80	5	the	the	DET
cana-4790	80	6	way	way	NOUN
cana-4790	80	7	eeg	eeg	NOUN
cana-4790	80	8	signals	signal	NOUN
cana-4790	80	9	are	be	AUX
cana-4790	80	10	examined	examine	VERB
cana-4790	80	11	by	by	ADP
cana-4790	80	12	allowing	allow	VERB
cana-4790	80	13	hierarchical	hierarchical	ADJ
cana-4790	80	14	learning	learning	NOUN
cana-4790	80	15	and	and	CCONJ
cana-4790	80	16	automated	automate	VERB
cana-4790	80	17	feature	feature	NOUN
cana-4790	80	18	extraction	extraction	NOUN
cana-4790	80	19	to	to	PART
cana-4790	80	20	occur	occur	VERB
cana-4790	80	21	.	.	PUNCT
cana-4790	81	1	in	in	ADP
cana-4790	81	2	particular	particular	ADJ
cana-4790	81	3	,	,	PUNCT
cana-4790	81	4	convolutional	convolutional	ADJ
cana-4790	81	5	neural	neural	ADJ
cana-4790	81	6	networks	network	NOUN
cana-4790	81	7	(	(	PUNCT
cana-4790	81	8	cnns	cnns	PROPN
cana-4790	81	9	)	)	PUNCT
cana-4790	81	10	are	be	AUX
cana-4790	81	11	very	very	ADV
cana-4790	81	12	excellent	excellent	ADJ
cana-4790	81	13	at	at	ADP
cana-4790	81	14	understanding	understand	VERB
cana-4790	81	15	how	how	SCONJ
cana-4790	81	16	distinct	distinct	ADJ
cana-4790	81	17	eeg	eeg	NOUN
cana-4790	81	18	bands	band	NOUN
cana-4790	81	19	rely	rely	VERB
cana-4790	81	20	on	on	ADP
cana-4790	81	21	one	one	NUM
cana-4790	81	22	another	another	DET
cana-4790	81	23	spatially	spatially	ADV
cana-4790	81	24	.	.	PUNCT
cana-4790	82	1	many	many	ADJ
cana-4790	82	2	tasks	task	NOUN
cana-4790	82	3	,	,	PUNCT
cana-4790	82	4	like	like	ADP
cana-4790	82	5	sorting	sort	VERB
cana-4790	82	6	motion	motion	NOUN
cana-4790	82	7	pictures	picture	NOUN
cana-4790	82	8	,	,	PUNCT
cana-4790	82	9	locating	locate	VERB
cana-4790	82	10	epileptic	epileptic	ADJ
cana-4790	82	11	convulsions	convulsion	NOUN
cana-4790	82	12	,	,	PUNCT
cana-4790	82	13	and	and	CCONJ
cana-4790	82	14	rating	rating	NOUN
cana-4790	82	15	sleep	sleep	NOUN
cana-4790	82	16	phases	phase	NOUN
cana-4790	82	17	,	,	PUNCT
cana-4790	82	18	require	require	VERB
cana-4790	82	19	cnns	cnn	NOUN
cana-4790	82	20	[	[	X
cana-4790	82	21	8	8	NUM
cana-4790	82	22	,	,	PUNCT
cana-4790	82	23	9	9	NUM
cana-4790	82	24	,	,	PUNCT
cana-4790	82	25	10	10	NUM
cana-4790	82	26	]	]	PUNCT
cana-4790	82	27	.	.	PUNCT
cana-4790	83	1	in	in	ADP
cana-4790	83	2	studies	study	NOUN
cana-4790	83	3	approximately	approximately	ADV
cana-4790	83	4	alzheimer	alzheimer	PROPN
cana-4790	83	5	's	's	PART
cana-4790	83	6	disease	disease	NOUN
cana-4790	83	7	,	,	PUNCT
cana-4790	83	8	cnns	cnn	NOUN
cana-4790	83	9	had	have	AUX
cana-4790	83	10	been	be	AUX
cana-4790	83	11	used	use	VERB
cana-4790	83	12	to	to	PART
cana-4790	83	13	describe	describe	VERB
cana-4790	83	14	regional	regional	ADJ
cana-4790	83	15	eeg	eeg	NOUN
cana-4790	83	16	styles	style	NOUN
cana-4790	83	17	by	by	ADP
cana-4790	83	18	way	way	NOUN
cana-4790	83	19	of	of	ADP
cana-4790	83	20	applying	apply	VERB
cana-4790	83	21	convolutions	convolution	NOUN
cana-4790	83	22	across	across	ADP
cana-4790	83	23	sensor	sensor	NOUN
cana-4790	83	24	maps	map	NOUN
cana-4790	83	25	or	or	CCONJ
cana-4790	83	26	time	time	NOUN
cana-4790	83	27	-	-	PUNCT
cana-4790	83	28	frequency	frequency	NOUN
cana-4790	83	29	maps	map	NOUN
cana-4790	83	30	.	.	PUNCT
cana-4790	84	1	in	in	ADP
cana-4790	84	2	comparison	comparison	NOUN
cana-4790	84	3	to	to	ADP
cana-4790	84	4	older	old	ADJ
cana-4790	84	5	device	device	NOUN
cana-4790	84	6	getting	get	VERB
cana-4790	84	7	to	to	PART
cana-4790	84	8	know	know	VERB
cana-4790	84	9	techniques	technique	NOUN
cana-4790	84	10	[	[	X
cana-4790	84	11	11	11	NUM
cana-4790	84	12	]	]	PUNCT
cana-4790	84	13	,	,	PUNCT
cana-4790	84	14	those	those	DET
cana-4790	84	15	models	model	NOUN
cana-4790	84	16	have	have	AUX
cana-4790	84	17	shown	show	VERB
cana-4790	84	18	higher	high	ADJ
cana-4790	84	19	accuracy	accuracy	NOUN
cana-4790	84	20	and	and	CCONJ
cana-4790	84	21	sturdiness	sturdiness	NOUN
cana-4790	84	22	in	in	ADP
cana-4790	84	23	classifying	classify	VERB
cana-4790	84	24	things	thing	NOUN
cana-4790	84	25	.	.	PUNCT
cana-4790	85	1	cnns	cnns	PROPN
cana-4790	85	2	,	,	PUNCT
cana-4790	85	3	however	however	ADV
cana-4790	85	4	,	,	PUNCT
cana-4790	85	5	usually	usually	ADV
cana-4790	85	6	file	file	VERB
cana-4790	85	7	features	feature	NOUN
cana-4790	85	8	in	in	ADP
cana-4790	85	9	space	space	NOUN
cana-4790	85	10	,	,	PUNCT
cana-4790	85	11	so	so	SCONJ
cana-4790	85	12	they	they	PRON
cana-4790	85	13	might	might	AUX
cana-4790	85	14	not	not	PART
cana-4790	85	15	be	be	AUX
cana-4790	85	16	the	the	DET
cana-4790	85	17	quality	quality	NOUN
cana-4790	85	18	way	way	NOUN
cana-4790	85	19	to	to	PART
cana-4790	85	20	model	model	VERB
cana-4790	85	21	lengthy	lengthy	ADJ
cana-4790	85	22	-	-	PUNCT
cana-4790	85	23	time	time	NOUN
cana-4790	85	24	period	period	NOUN
cana-4790	85	25	temporal	temporal	ADJ
cana-4790	85	26	relationships	relationship	NOUN
cana-4790	85	27	in	in	ADP
cana-4790	85	28	eeg	eeg	PROPN
cana-4790	85	29	sequences	sequence	NOUN
cana-4790	85	30	,	,	PUNCT
cana-4790	85	31	that	that	PRON
cana-4790	85	32	are	be	AUX
cana-4790	85	33	essential	essential	ADJ
cana-4790	85	34	for	for	ADP
cana-4790	85	35	maintaining	maintain	VERB
cana-4790	85	36	track	track	NOUN
cana-4790	85	37	of	of	ADP
cana-4790	85	38	how	how	SCONJ
cana-4790	85	39	cognition	cognition	NOUN
cana-4790	85	40	changes	change	VERB
cana-4790	85	41	in	in	ADP
cana-4790	85	42	advert	advert	NOUN
cana-4790	85	43	.	.	PUNCT
cana-4790	86	1	rnns	rnn	NOUN
cana-4790	86	2	,	,	PUNCT
cana-4790	86	3	especially	especially	ADV
cana-4790	86	4	lengthy	lengthy	ADJ
cana-4790	86	5	short	short	ADJ
cana-4790	86	6	-	-	PUNCT
cana-4790	86	7	term	term	NOUN
cana-4790	86	8	reminiscence	reminiscence	NOUN
cana-4790	86	9	(	(	PUNCT
cana-4790	86	10	lstm	lstm	NOUN
cana-4790	86	11	)	)	PUNCT
cana-4790	86	12	networks	network	NOUN
cana-4790	86	13	,	,	PUNCT
cana-4790	86	14	are	be	AUX
cana-4790	86	15	designed	design	VERB
cana-4790	86	16	to	to	PART
cana-4790	86	17	handle	handle	VERB
cana-4790	86	18	input	input	NOUN
cana-4790	86	19	in	in	ADP
cana-4790	86	20	a	a	DET
cana-4790	86	21	certain	certain	ADJ
cana-4790	86	22	series	series	NOUN
cana-4790	86	23	and	and	CCONJ
cana-4790	86	24	can	can	AUX
cana-4790	86	25	find	find	VERB
cana-4790	86	26	out	out	ADP
cana-4790	86	27	how	how	SCONJ
cana-4790	86	28	activities	activity	NOUN
cana-4790	86	29	rely	rely	VERB
cana-4790	86	30	on	on	ADP
cana-4790	86	31	one	one	NUM
cana-4790	86	32	another	another	DET
cana-4790	86	33	over	over	ADP
cana-4790	86	34	prolonged	prolong	VERB
cana-4790	86	35	intervals	interval	NOUN
cana-4790	86	36	.	.	PUNCT
cana-4790	87	1	lstm	lstm	ADJ
cana-4790	87	2	fashions	fashion	NOUN
cana-4790	87	3	were	be	AUX
cana-4790	87	4	efficiently	efficiently	ADV
cana-4790	87	5	used	use	VERB
cana-4790	87	6	in	in	ADP
cana-4790	87	7	eeg	eeg	NOUN
cana-4790	87	8	evaluation	evaluation	NOUN
cana-4790	87	9	for	for	ADP
cana-4790	87	10	jobs	job	NOUN
cana-4790	87	11	related	relate	VERB
cana-4790	87	12	to	to	ADP
cana-4790	87	13	temporal	temporal	ADJ
cana-4790	87	14	pattern	pattern	NOUN
cana-4790	87	15	recognition	recognition	NOUN
cana-4790	87	16	,	,	PUNCT
cana-4790	87	17	consisting	consist	VERB
cana-4790	87	18	of	of	ADP
cana-4790	87	19	figuring	figure	VERB
cana-4790	87	20	out	out	ADP
cana-4790	87	21	feelings	feeling	NOUN
cana-4790	87	22	,	,	PUNCT
cana-4790	87	23	cognitive	cognitive	ADJ
cana-4790	87	24	load	load	NOUN
cana-4790	87	25	,	,	PUNCT
cana-4790	87	26	and	and	CCONJ
cana-4790	87	27	interest	interest	NOUN
cana-4790	87	28	modelling	modelling	NOUN
cana-4790	87	29	[	[	X
cana-4790	87	30	12	12	NUM
cana-4790	87	31	,	,	PUNCT
cana-4790	87	32	13	13	NUM
cana-4790	87	33	]	]	PUNCT
cana-4790	87	34	.	.	PUNCT
cana-4790	88	1	temporal	temporal	ADJ
cana-4790	88	2	modelling	modelling	NOUN
cana-4790	88	3	is	be	AUX
cana-4790	88	4	very	very	ADV
cana-4790	88	5	indispensable	indispensable	ADJ
cana-4790	88	6	for	for	ADP
cana-4790	88	7	ad	ad	NOUN
cana-4790	88	8	detection	detection	NOUN
cana-4790	88	9	as	as	SCONJ
cana-4790	88	10	cognitive	cognitive	ADJ
cana-4790	88	11	impairment	impairment	NOUN
cana-4790	88	12	shows	show	VERB
cana-4790	88	13	slowly	slowly	ADV
cana-4790	88	14	and	and	CCONJ
cana-4790	88	15	is	be	AUX
cana-4790	88	16	pondered	ponder	VERB
cana-4790	88	17	in	in	ADP
cana-4790	88	18	changes	change	NOUN
cana-4790	88	19	in	in	ADP
cana-4790	88	20	intelligence	intelligence	NOUN
cana-4790	88	21	impulses	impulse	NOUN
cana-4790	88	22	through	through	ADP
cana-4790	88	23	the	the	DET
cana-4790	88	24	years	year	NOUN
cana-4790	88	25	.	.	PUNCT
cana-4790	89	1	lstms	lstms	PROPN
cana-4790	89	2	may	may	AUX
cana-4790	89	3	locate	locate	VERB
cana-4790	89	4	diffused	diffused	ADJ
cana-4790	89	5	adjustments	adjustment	NOUN
cana-4790	89	6	within	within	ADP
cana-4790	89	7	the	the	DET
cana-4790	89	8	eeg	eeg	NOUN
cana-4790	89	9	rhythms	rhythm	NOUN
cana-4790	89	10	that	that	PRON
cana-4790	89	11	wo	will	AUX
cana-4790	89	12	n't	not	PART
cana-4790	89	13	be	be	AUX
cana-4790	89	14	obvious	obvious	ADJ
cana-4790	89	15	in	in	ADP
cana-4790	89	16	a	a	DET
cana-4790	89	17	unmarried	unmarried	ADJ
cana-4790	89	18	time	time	NOUN
cana-4790	89	19	frame	frame	NOUN
cana-4790	89	20	.	.	PUNCT
cana-4790	90	1	several	several	ADJ
cana-4790	90	2	research	research	NOUN
cana-4790	90	3	have	have	AUX
cana-4790	90	4	shown	show	VERB
cana-4790	90	5	that	that	SCONJ
cana-4790	90	6	lstm	lstm	ADJ
cana-4790	90	7	fashions	fashion	NOUN
cana-4790	90	8	may	may	AUX
cana-4790	90	9	additionally	additionally	ADV
cana-4790	90	10	boom	boom	VERB
cana-4790	90	11	the	the	DET
cana-4790	90	12	accuracy	accuracy	NOUN
cana-4790	90	13	of	of	ADP
cana-4790	90	14	figuring	figure	VERB
cana-4790	90	15	out	out	ADP
cana-4790	90	16	eeg	eeg	NOUN
cana-4790	90	17	records	record	NOUN
cana-4790	90	18	used	use	VERB
cana-4790	90	19	to	to	PART
cana-4790	90	20	discover	discover	VERB
cana-4790	90	21	talent	talent	NOUN
cana-4790	90	22	diseases	disease	NOUN
cana-4790	90	23	[	[	X
cana-4790	90	24	14	14	NUM
cana-4790	90	25	]	]	PUNCT
cana-4790	90	26	.	.	PUNCT
cana-4790	91	1	researchers	researcher	NOUN
cana-4790	91	2	have	have	AUX
cana-4790	91	3	made	make	VERB
cana-4790	91	4	combined	combined	ADJ
cana-4790	91	5	models	model	NOUN
cana-4790	91	6	that	that	PRON
cana-4790	91	7	combine	combine	VERB
cana-4790	91	8	cnn	cnn	PROPN
cana-4790	91	9	and	and	CCONJ
cana-4790	91	10	lstm	lstm	NOUN
cana-4790	91	11	designs	design	NOUN
cana-4790	91	12	so	so	SCONJ
cana-4790	91	13	we	we	PRON
cana-4790	91	14	can	can	AUX
cana-4790	91	15	use	use	VERB
cana-4790	91	16	each	each	PRON
cana-4790	91	17	the	the	DET
cana-4790	91	18	spatial	spatial	ADJ
cana-4790	91	19	and	and	CCONJ
cana-4790	91	20	temporal	temporal	ADJ
cana-4790	91	21	components	component	NOUN
cana-4790	91	22	of	of	ADP
cana-4790	91	23	eeg	eeg	NOUN
cana-4790	91	24	facts	fact	NOUN
cana-4790	91	25	.	.	PUNCT
cana-4790	92	1	cnn	cnn	PROPN
cana-4790	92	2	layers	layer	NOUN
cana-4790	92	3	are	be	AUX
cana-4790	92	4	used	use	VERB
cana-4790	92	5	to	to	PART
cana-4790	92	6	get	get	VERB
cana-4790	92	7	spatial	spatial	ADJ
cana-4790	92	8	facts	fact	NOUN
cana-4790	92	9	from	from	ADP
cana-4790	92	10	eeg	eeg	NOUN
cana-4790	92	11	indicators	indicator	NOUN
cana-4790	92	12	,	,	PUNCT
cana-4790	92	13	like	like	ADP
cana-4790	92	14	how	how	SCONJ
cana-4790	92	15	electrodes	electrode	NOUN
cana-4790	92	16	are	be	AUX
cana-4790	92	17	linked	link	VERB
cana-4790	92	18	or	or	CCONJ
cana-4790	92	19	topographic	topographic	ADJ
cana-4790	92	20	maps	map	NOUN
cana-4790	92	21	,	,	PUNCT
cana-4790	92	22	and	and	CCONJ
cana-4790	92	23	lstm	lstm	NOUN
cana-4790	92	24	layers	layer	NOUN
cana-4790	92	25	are	be	AUX
cana-4790	92	26	used	use	VERB
cana-4790	92	27	to	to	PART
cana-4790	92	28	get	get	VERB
cana-4790	92	29	statistics	statistic	NOUN
cana-4790	92	30	about	about	ADP
cana-4790	92	31	how	how	SCONJ
cana-4790	92	32	matters	matter	NOUN
cana-4790	92	33	trade	trade	VERB
cana-4790	92	34	over	over	ADP
cana-4790	92	35	time	time	NOUN
cana-4790	92	36	windows	window	NOUN
cana-4790	92	37	or	or	CCONJ
cana-4790	92	38	signal	signal	ADJ
cana-4790	92	39	segments	segment	NOUN
cana-4790	93	1	[	[	X
cana-4790	93	2	15	15	NUM
cana-4790	93	3	]	]	PUNCT
cana-4790	93	4	.	.	PUNCT
cana-4790	94	1	in	in	ADP
cana-4790	94	2	lots	lot	NOUN
cana-4790	94	3	of	of	ADP
cana-4790	94	4	eeg	eeg	NOUN
cana-4790	94	5	-	-	PUNCT
cana-4790	94	6	primarily	primarily	ADV
cana-4790	94	7	based	base	VERB
cana-4790	94	8	activities	activity	NOUN
cana-4790	94	9	,	,	PUNCT
cana-4790	94	10	including	include	VERB
cana-4790	94	11	sleep	sleep	NOUN
cana-4790	94	12	level	level	NOUN
cana-4790	94	13	identity	identity	NOUN
cana-4790	94	14	[	[	X
cana-4790	94	15	16	16	NUM
cana-4790	94	16	]	]	PUNCT
cana-4790	94	17	,	,	PUNCT
cana-4790	94	18	seizure	seizure	VERB
cana-4790	94	19	prediction	prediction	NOUN
cana-4790	94	20	[	[	X
cana-4790	94	21	17	17	NUM
cana-4790	94	22	]	]	PUNCT
cana-4790	94	23	,	,	PUNCT
cana-4790	94	24	and	and	CCONJ
cana-4790	94	25	attention	attention	NOUN
cana-4790	94	26	tracking	track	VERB
cana-4790	94	27	[	[	X
cana-4790	94	28	18	18	NUM
cana-4790	94	29	]	]	PUNCT
cana-4790	94	30	,	,	PUNCT
cana-4790	94	31	hybrid	hybrid	ADJ
cana-4790	94	32	cnn	cnn	PROPN
cana-4790	94	33	-	-	PUNCT
cana-4790	94	34	lstm	lstm	ADJ
cana-4790	94	35	models	model	NOUN
cana-4790	94	36	have	have	AUX
cana-4790	94	37	completed	complete	VERB
cana-4790	94	38	higher	high	ADJ
cana-4790	94	39	.	.	PUNCT
cana-4790	95	1	those	those	DET
cana-4790	95	2	models	model	NOUN
cana-4790	95	3	integrate	integrate	VERB
cana-4790	95	4	cnns	cnn	NOUN
cana-4790	95	5	'	'	PART
cana-4790	95	6	capability	capability	NOUN
cana-4790	95	7	to	to	PART
cana-4790	95	8	handle	handle	VERB
cana-4790	95	9	spatial	spatial	ADJ
cana-4790	95	10	dimensions	dimension	NOUN
cana-4790	95	11	with	with	ADP
cana-4790	95	12	lstms	lstms	ADJ
cana-4790	95	13	'	'	PUNCT
cana-4790	95	14	capability	capability	NOUN
cana-4790	95	15	to	to	PART
cana-4790	95	16	stumble	stumble	VERB
cana-4790	95	17	on	on	ADP
cana-4790	95	18	temporal	temporal	ADJ
cana-4790	95	19	changes	change	NOUN
cana-4790	95	20	.	.	PUNCT
cana-4790	96	1	this	this	PRON
cana-4790	96	2	qualifies	qualify	VERB
cana-4790	96	3	them	they	PRON
cana-4790	96	4	properly	properly	ADV
cana-4790	96	5	for	for	ADP
cana-4790	96	6	tough	tough	ADJ
cana-4790	96	7	eeg	eeg	NOUN
cana-4790	96	8	categorisation	categorisation	NOUN
cana-4790	96	9	jobsseveral	jobsseveral	ADJ
cana-4790	96	10	recent	recent	ADJ
cana-4790	96	11	research	research	NOUN
cana-4790	96	12	have	have	AUX
cana-4790	96	13	begun	begin	VERB
cana-4790	96	14	investigating	investigate	VERB
cana-4790	96	15	cnn	cnn	PROPN
cana-4790	96	16	-	-	PUNCT
cana-4790	96	17	lstm	lstm	ADJ
cana-4790	96	18	models	model	NOUN
cana-4790	96	19	for	for	ADP
cana-4790	96	20	early	early	ADJ
cana-4790	96	21	identification	identification	NOUN
cana-4790	96	22	of	of	ADP
cana-4790	96	23	alzheimer	alzheimer	PROPN
cana-4790	96	24	's	's	PART
cana-4790	96	25	disease	disease	NOUN
cana-4790	96	26	using	use	VERB
cana-4790	96	27	eeg	eeg	NOUN
cana-4790	96	28	.	.	PUNCT
cana-4790	97	1	these	these	DET
cana-4790	97	2	experiments	experiment	NOUN
cana-4790	97	3	show	show	VERB
cana-4790	97	4	that	that	SCONJ
cana-4790	97	5	integrating	integrate	VERB
cana-4790	97	6	spatial	spatial	ADJ
cana-4790	97	7	and	and	CCONJ
cana-4790	97	8	temporal	temporal	ADJ
cana-4790	97	9	data	datum	NOUN
cana-4790	97	10	improves	improve	VERB
cana-4790	97	11	classification	classification	NOUN
cana-4790	97	12	performance	performance	NOUN
cana-4790	97	13	over	over	ADP
cana-4790	97	14	cnn	cnn	PROPN
cana-4790	97	15	or	or	CCONJ
cana-4790	97	16	lstm	lstm	NOUN
cana-4790	97	17	models	model	NOUN
cana-4790	97	18	operating	operate	VERB
cana-4790	97	19	independently	independently	ADV
cana-4790	97	20	[	[	X
cana-4790	97	21	19	19	NUM
cana-4790	97	22	,	,	PUNCT
cana-4790	97	23	20	20	NUM
cana-4790	97	24	]	]	PUNCT
cana-4790	97	25	.	.	PUNCT
cana-4790	98	1	many	many	ADJ
cana-4790	98	2	of	of	ADP
cana-4790	98	3	these	these	DET
cana-4790	98	4	research	research	NOUN
cana-4790	98	5	,	,	PUNCT
cana-4790	98	6	nevertheless	nevertheless	ADV
cana-4790	98	7	,	,	PUNCT
cana-4790	98	8	are	be	AUX
cana-4790	98	9	constrained	constrain	VERB
cana-4790	98	10	in	in	ADP
cana-4790	98	11	their	their	PRON
cana-4790	98	12	dataset	dataset	NOUN
cana-4790	98	13	size	size	NOUN
cana-4790	98	14	,	,	PUNCT
cana-4790	98	15	capacity	capacity	NOUN
cana-4790	98	16	to	to	PART
cana-4790	98	17	generalise	generalise	VERB
cana-4790	98	18	,	,	PUNCT
cana-4790	98	19	or	or	CCONJ
cana-4790	98	20	real	real	ADJ
cana-4790	98	21	-	-	PUNCT
cana-4790	98	22	time	time	NOUN
cana-4790	98	23	applicability	applicability	NOUN
cana-4790	98	24	.	.	PUNCT
cana-4790	99	1	more	more	ADJ
cana-4790	99	2	research	research	NOUN
cana-4790	99	3	is	be	AUX
cana-4790	99	4	thus	thus	ADV
cana-4790	99	5	required	require	VERB
cana-4790	99	6	to	to	PART
cana-4790	99	7	develop	develop	VERB
cana-4790	99	8	more	more	ADV
cana-4790	99	9	consistent	consistent	ADJ
cana-4790	99	10	and	and	CCONJ
cana-4790	99	11	scalable	scalable	ADJ
cana-4790	99	12	solutions	solution	NOUN
cana-4790	99	13	.	.	PUNCT
cana-4790	100	1	table	table	NOUN
cana-4790	100	2	1	1	NUM
cana-4790	100	3	.	.	PUNCT
cana-4790	100	4	related	relate	VERB
cana-4790	100	5	research	research	NOUN
cana-4790	100	6	ref	ref	NOUN
cana-4790	100	7	method	method	NOUN
cana-4790	100	8	/	/	SYM
cana-4790	100	9	approach	approach	NOUN
cana-4790	100	10	dataset	dataset	VERB
cana-4790	100	11	/	/	SYM
cana-4790	100	12	eeg	eeg	NOUN
cana-4790	100	13	signals	signal	NOUN
cana-4790	100	14	used	use	VERB
cana-4790	100	15	features	feature	NOUN
cana-4790	100	16	/	/	SYM
cana-4790	100	17	model	model	NOUN
cana-4790	100	18	used	use	VERB
cana-4790	100	19	key	key	ADJ
cana-4790	100	20	findings	finding	NOUN
cana-4790	100	21	/	/	SYM
cana-4790	100	22	performance	performance	NOUN
cana-4790	100	23	[	[	X
cana-4790	100	24	1	1	NUM
cana-4790	100	25	]	]	X
cana-4790	100	26	spectral	spectral	ADJ
cana-4790	100	27	eeg	eeg	PROPN
cana-4790	100	28	analysis	analysis	NOUN
cana-4790	100	29	ad	ad	NOUN
cana-4790	100	30	patient	patient	NOUN
cana-4790	100	31	eeg	eeg	NOUN
cana-4790	100	32	power	power	NOUN
cana-4790	100	33	in	in	ADP
cana-4790	100	34	delta	delta	PROPN
cana-4790	100	35	,	,	PUNCT
cana-4790	100	36	theta	theta	NOUN
cana-4790	100	37	,	,	PUNCT
cana-4790	100	38	alpha	alpha	NOUN
cana-4790	100	39	,	,	PUNCT
cana-4790	100	40	beta	beta	NOUN
cana-4790	100	41	bands	band	NOUN
cana-4790	100	42	slowing	slow	VERB
cana-4790	100	43	of	of	ADP
cana-4790	100	44	eeg	eeg	NOUN
cana-4790	100	45	rhythms	rhythm	NOUN
cana-4790	100	46	in	in	ADP
cana-4790	100	47	ad	ad	NOUN
cana-4790	100	48	communications	communication	NOUN
cana-4790	100	49	on	on	ADP
cana-4790	100	50	applied	apply	VERB
cana-4790	100	51	nonlinear	nonlinear	ADJ
cana-4790	100	52	analysis	analysis	NOUN
cana-4790	100	53	issn	issn	NOUN
cana-4790	100	54	:	:	PUNCT
cana-4790	100	55	1074	1074	NUM
cana-4790	100	56	-	-	PUNCT
cana-4790	100	57	133x	133x	NUM
cana-4790	100	58	vol	vol	NOUN
cana-4790	100	59	31	31	NUM
cana-4790	100	60	no	no	NOUN
cana-4790	100	61	.	.	PUNCT
cana-4790	101	1	1s	1s	NUM
cana-4790	101	2	(	(	PUNCT
cana-4790	101	3	2024	2024	NUM
cana-4790	101	4	)	)	PUNCT
cana-4790	101	5	203	203	NUM
cana-4790	101	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	102	1	[	[	X
cana-4790	102	2	2	2	NUM
cana-4790	102	3	]	]	PUNCT
cana-4790	102	4	frequency	frequency	NOUN
cana-4790	102	5	domain	domain	NOUN
cana-4790	102	6	analysis	analysis	NOUN
cana-4790	102	7	clinical	clinical	ADJ
cana-4790	102	8	eeg	eeg	NOUN
cana-4790	102	9	signals	signal	NOUN
cana-4790	102	10	power	power	NOUN
cana-4790	102	11	spectral	spectral	ADJ
cana-4790	102	12	density	density	NOUN
cana-4790	102	13	increased	increase	VERB
cana-4790	102	14	theta	theta	NOUN
cana-4790	102	15	,	,	PUNCT
cana-4790	102	16	decreased	decrease	VERB
cana-4790	102	17	alpha	alpha	NOUN
cana-4790	102	18	in	in	ADP
cana-4790	102	19	ad	ad	NOUN
cana-4790	102	20	[	[	X
cana-4790	102	21	3	3	NUM
cana-4790	102	22	]	]	X
cana-4790	102	23	complexity	complexity	NOUN
cana-4790	102	24	analysis	analysis	NOUN
cana-4790	102	25	resting	rest	VERB
cana-4790	102	26	-	-	PUNCT
cana-4790	102	27	state	state	NOUN
cana-4790	102	28	eeg	eeg	PROPN
cana-4790	102	29	approximate	approximate	PROPN
cana-4790	102	30	entropy	entropy	PROPN
cana-4790	102	31	,	,	PUNCT
cana-4790	102	32	sample	sample	NOUN
cana-4790	102	33	entropy	entropy	NOUN
cana-4790	102	34	reduced	reduce	VERB
cana-4790	102	35	signal	signal	NOUN
cana-4790	102	36	complexity	complexity	NOUN
cana-4790	102	37	in	in	ADP
cana-4790	102	38	ad	ad	NOUN
cana-4790	102	39	patients	patient	NOUN
cana-4790	102	40	[	[	X
cana-4790	102	41	4	4	NUM
cana-4790	102	42	]	]	X
cana-4790	102	43	functional	functional	ADJ
cana-4790	102	44	connectivity	connectivity	NOUN
cana-4790	102	45	eeg	eeg	NOUN
cana-4790	102	46	from	from	ADP
cana-4790	102	47	ad	ad	NOUN
cana-4790	102	48	vs.	vs.	ADP
cana-4790	102	49	controls	control	NOUN
cana-4790	102	50	coherence	coherence	NOUN
cana-4790	102	51	,	,	PUNCT
cana-4790	102	52	phase	phase	NOUN
cana-4790	102	53	lag	lag	NOUN
cana-4790	102	54	index	index	NOUN
cana-4790	102	55	decreased	decrease	VERB
cana-4790	102	56	inter	inter	ADJ
cana-4790	102	57	-	-	ADJ
cana-4790	102	58	hemispheric	hemispheric	ADJ
cana-4790	102	59	coherence	coherence	NOUN
cana-4790	102	60	in	in	ADP
cana-4790	102	61	ad	ad	NOUN
cana-4790	102	62	[	[	X
cana-4790	102	63	5	5	NUM
cana-4790	102	64	]	]	PUNCT
cana-4790	102	65	svm	svm	VERB
cana-4790	102	66	classification	classification	NOUN
cana-4790	102	67	preprocessed	preprocesse	VERB
cana-4790	102	68	eeg	eeg	PROPN
cana-4790	102	69	datasets	dataset	NOUN
cana-4790	102	70	spectral	spectral	ADJ
cana-4790	102	71	and	and	CCONJ
cana-4790	102	72	wavelet	wavelet	NOUN
cana-4790	102	73	features	feature	VERB
cana-4790	102	74	moderate	moderate	ADJ
cana-4790	102	75	accuracy	accuracy	NOUN
cana-4790	102	76	;	;	PUNCT
cana-4790	102	77	reliant	reliant	ADJ
cana-4790	102	78	on	on	ADP
cana-4790	102	79	hand	hand	NOUN
cana-4790	102	80	-	-	PUNCT
cana-4790	102	81	crafted	craft	VERB
cana-4790	102	82	features	feature	NOUN
cana-4790	102	83	[	[	X
cana-4790	102	84	6	6	NUM
cana-4790	102	85	]	]	X
cana-4790	102	86	random	random	ADJ
cana-4790	102	87	forest	forest	NOUN
cana-4790	102	88	,	,	PUNCT
cana-4790	102	89	knn	knn	PROPN
cana-4790	102	90	eeg	eeg	PROPN
cana-4790	102	91	from	from	ADP
cana-4790	102	92	elderly	elderly	ADJ
cana-4790	102	93	cohorts	cohort	NOUN
cana-4790	102	94	time	time	NOUN
cana-4790	102	95	-	-	PUNCT
cana-4790	102	96	frequency	frequency	NOUN
cana-4790	102	97	features	feature	VERB
cana-4790	102	98	reasonable	reasonable	ADJ
cana-4790	102	99	accuracy	accuracy	NOUN
cana-4790	102	100	;	;	PUNCT
cana-4790	102	101	low	low	ADJ
cana-4790	102	102	generalization	generalization	NOUN
cana-4790	102	103	[	[	X
cana-4790	102	104	7	7	NUM
cana-4790	102	105	]	]	ADJ
cana-4790	102	106	decision	decision	NOUN
cana-4790	102	107	trees	tree	NOUN
cana-4790	102	108	,	,	PUNCT
cana-4790	102	109	pca	pca	PROPN
cana-4790	102	110	alzheimer	alzheimer	PROPN
cana-4790	102	111	’s	’s	PART
cana-4790	102	112	eeg	eeg	PROPN
cana-4790	102	113	dataset	dataset	PROPN
cana-4790	102	114	pca	pca	PROPN
cana-4790	102	115	-	-	PUNCT
cana-4790	102	116	reduced	reduce	VERB
cana-4790	102	117	spectral	spectral	ADJ
cana-4790	102	118	features	feature	NOUN
cana-4790	102	119	reduced	reduce	VERB
cana-4790	102	120	dimensionality	dimensionality	NOUN
cana-4790	102	121	improved	improve	VERB
cana-4790	102	122	accuracy	accuracy	NOUN
cana-4790	103	1	[	[	X
cana-4790	103	2	8	8	NUM
cana-4790	103	3	]	]	X
cana-4790	103	4	cnn	cnn	PROPN
cana-4790	103	5	for	for	ADP
cana-4790	103	6	eeg	eeg	NOUN
cana-4790	103	7	classification	classification	NOUN
cana-4790	103	8	motor	motor	NOUN
cana-4790	103	9	imagery	imagery	NOUN
cana-4790	103	10	eeg	eeg	NOUN
cana-4790	103	11	datasets	dataset	NOUN
cana-4790	103	12	2d	2d	NOUN
cana-4790	103	13	eeg	eeg	NOUN
cana-4790	103	14	maps	map	NOUN
cana-4790	103	15	as	as	SCONJ
cana-4790	103	16	cnn	cnn	PROPN
cana-4790	103	17	input	input	VERB
cana-4790	103	18	effective	effective	ADJ
cana-4790	103	19	spatial	spatial	ADJ
cana-4790	103	20	pattern	pattern	NOUN
cana-4790	103	21	extraction	extraction	NOUN
cana-4790	103	22	[	[	X
cana-4790	103	23	9	9	NUM
cana-4790	103	24	]	]	PUNCT
cana-4790	103	25	cnn	cnn	NOUN
cana-4790	103	26	for	for	ADP
cana-4790	103	27	seizure	seizure	NOUN
cana-4790	103	28	detection	detection	NOUN
cana-4790	103	29	chb	chb	NOUN
cana-4790	103	30	-	-	PUNCT
cana-4790	103	31	mit	mit	NOUN
cana-4790	103	32	eeg	eeg	NOUN
cana-4790	103	33	dataset	dataset	VERB
cana-4790	103	34	raw	raw	ADJ
cana-4790	103	35	eeg	eeg	NOUN
cana-4790	103	36	input	input	NOUN
cana-4790	103	37	high	high	ADJ
cana-4790	103	38	accuracy	accuracy	NOUN
cana-4790	103	39	in	in	ADP
cana-4790	103	40	seizure	seizure	NOUN
cana-4790	103	41	vs.	vs.	ADP
cana-4790	103	42	non	non	ADJ
cana-4790	103	43	-	-	ADJ
cana-4790	103	44	seizure	seizure	ADJ
cana-4790	103	45	detection	detection	NOUN
cana-4790	103	46	[	[	X
cana-4790	103	47	10	10	NUM
cana-4790	103	48	]	]	X
cana-4790	103	49	cnn	cnn	PROPN
cana-4790	103	50	for	for	ADP
cana-4790	103	51	emotion	emotion	NOUN
cana-4790	103	52	recognition	recognition	NOUN
cana-4790	103	53	deap	deap	PROPN
cana-4790	103	54	dataset	dataset	VERB
cana-4790	103	55	time	time	NOUN
cana-4790	103	56	-	-	PUNCT
cana-4790	103	57	frequency	frequency	NOUN
cana-4790	103	58	images	image	NOUN
cana-4790	103	59	learned	learn	VERB
cana-4790	103	60	spatial	spatial	ADJ
cana-4790	103	61	patterns	pattern	NOUN
cana-4790	103	62	effectively	effectively	ADV
cana-4790	103	63	[	[	X
cana-4790	103	64	11	11	NUM
cana-4790	103	65	]	]	X
cana-4790	103	66	cnn	cnn	PROPN
cana-4790	103	67	for	for	ADP
cana-4790	103	68	ad	ad	NOUN
cana-4790	103	69	detection	detection	NOUN
cana-4790	103	70	clinical	clinical	ADJ
cana-4790	103	71	eeg	eeg	NOUN
cana-4790	103	72	connectivity	connectivity	NOUN
cana-4790	103	73	matrices	matrix	NOUN
cana-4790	103	74	outperformed	outperform	VERB
cana-4790	103	75	traditional	traditional	ADJ
cana-4790	103	76	ml	ml	NOUN
cana-4790	103	77	models	model	NOUN
cana-4790	103	78	[	[	X
cana-4790	103	79	12	12	NUM
cana-4790	103	80	]	]	X
cana-4790	103	81	lstm	lstm	NOUN
cana-4790	103	82	for	for	ADP
cana-4790	103	83	temporal	temporal	ADJ
cana-4790	103	84	modeling	modeling	NOUN
cana-4790	103	85	eeg	eeg	NOUN
cana-4790	103	86	time	time	NOUN
cana-4790	103	87	-	-	PUNCT
cana-4790	103	88	series	series	NOUN
cana-4790	103	89	raw	raw	ADJ
cana-4790	103	90	sequences	sequence	NOUN
cana-4790	103	91	captured	capture	VERB
cana-4790	103	92	long	long	ADJ
cana-4790	103	93	-	-	PUNCT
cana-4790	103	94	term	term	NOUN
cana-4790	103	95	eeg	eeg	NOUN
cana-4790	103	96	dependencies	dependency	NOUN
cana-4790	103	97	[	[	X
cana-4790	103	98	13	13	NUM
cana-4790	103	99	]	]	PUNCT
cana-4790	103	100	lstm	lstm	NOUN
cana-4790	103	101	for	for	ADP
cana-4790	103	102	bci	bci	PROPN
cana-4790	103	103	eeg	eeg	PROPN
cana-4790	103	104	from	from	ADP
cana-4790	103	105	motor	motor	NOUN
cana-4790	103	106	imagery	imagery	NOUN
cana-4790	103	107	tasks	task	NOUN
cana-4790	103	108	time	time	NOUN
cana-4790	103	109	-	-	PUNCT
cana-4790	103	110	windowed	windowe	VERB
cana-4790	103	111	features	feature	NOUN
cana-4790	103	112	better	well	ADJ
cana-4790	103	113	performance	performance	NOUN
cana-4790	103	114	over	over	ADP
cana-4790	103	115	vanilla	vanilla	NOUN
cana-4790	103	116	rnns	rnn	NOUN
cana-4790	103	117	[	[	X
cana-4790	103	118	14	14	NUM
cana-4790	103	119	]	]	X
cana-4790	103	120	lstm	lstm	NOUN
cana-4790	103	121	for	for	ADP
cana-4790	103	122	ad	ad	NOUN
cana-4790	103	123	classification	classification	NOUN
cana-4790	103	124	resting	rest	VERB
cana-4790	103	125	-	-	PUNCT
cana-4790	103	126	state	state	NOUN
cana-4790	103	127	eeg	eeg	NOUN
cana-4790	103	128	band	band	NOUN
cana-4790	103	129	power	power	NOUN
cana-4790	103	130	sequences	sequence	NOUN
cana-4790	103	131	accurate	accurate	ADJ
cana-4790	103	132	detection	detection	NOUN
cana-4790	103	133	of	of	ADP
cana-4790	103	134	earlystage	earlystage	NOUN
cana-4790	103	135	ad	ad	NOUN
cana-4790	103	136	[	[	X
cana-4790	103	137	15	15	NUM
cana-4790	103	138	]	]	X
cana-4790	103	139	cnn	cnn	PROPN
cana-4790	103	140	-	-	PUNCT
cana-4790	103	141	lstm	lstm	ADJ
cana-4790	103	142	hybrid	hybrid	ADJ
cana-4790	103	143	epileptic	epileptic	ADJ
cana-4790	103	144	eeg	eeg	NOUN
cana-4790	103	145	datasets	dataset	NOUN
cana-4790	103	146	cnn	cnn	PROPN
cana-4790	103	147	features	feature	VERB
cana-4790	103	148	+	+	CCONJ
cana-4790	103	149	lstm	lstm	ADJ
cana-4790	103	150	sequence	sequence	NOUN
cana-4790	103	151	learning	learn	VERB
cana-4790	103	152	high	high	ADJ
cana-4790	103	153	performance	performance	NOUN
cana-4790	103	154	in	in	ADP
cana-4790	103	155	seizure	seizure	NOUN
cana-4790	103	156	prediction	prediction	NOUN
cana-4790	103	157	[	[	X
cana-4790	103	158	16	16	NUM
cana-4790	103	159	]	]	X
cana-4790	103	160	cnn	cnn	PROPN
cana-4790	103	161	-	-	PUNCT
cana-4790	103	162	lstm	lstm	PROPN
cana-4790	103	163	for	for	ADP
cana-4790	103	164	sleep	sleep	NOUN
cana-4790	103	165	scoring	score	VERB
cana-4790	103	166	sleep	sleep	ADJ
cana-4790	103	167	-	-	PUNCT
cana-4790	103	168	edf	edf	PROPN
cana-4790	103	169	dataset	dataset	VERB
cana-4790	103	170	time	time	NOUN
cana-4790	103	171	-	-	PUNCT
cana-4790	103	172	frequency	frequency	NOUN
cana-4790	103	173	maps	map	NOUN
cana-4790	103	174	improved	improve	VERB
cana-4790	103	175	performance	performance	NOUN
cana-4790	103	176	over	over	ADP
cana-4790	103	177	single	single	ADJ
cana-4790	103	178	models	model	NOUN
cana-4790	103	179	[	[	X
cana-4790	103	180	17	17	NUM
cana-4790	103	181	]	]	X
cana-4790	103	182	cnn	cnn	PROPN
cana-4790	103	183	-	-	PUNCT
cana-4790	103	184	lstm	lstm	PROPN
cana-4790	103	185	for	for	ADP
cana-4790	103	186	seizure	seizure	NOUN
cana-4790	103	187	detect	detect	NOUN
cana-4790	103	188	tuh	tuh	NOUN
cana-4790	103	189	eeg	eeg	NOUN
cana-4790	103	190	seizure	seizure	NOUN
cana-4790	103	191	corpus	corpus	PROPN
cana-4790	103	192	spectrogram	spectrogram	NOUN
cana-4790	103	193	-	-	PUNCT
cana-4790	103	194	based	base	VERB
cana-4790	103	195	input	input	NOUN
cana-4790	103	196	robust	robust	ADJ
cana-4790	103	197	in	in	ADP
cana-4790	103	198	noisy	noisy	ADJ
cana-4790	103	199	conditions	condition	NOUN
cana-4790	103	200	[	[	X
cana-4790	103	201	18	18	NUM
cana-4790	103	202	]	]	X
cana-4790	103	203	cnn	cnn	PROPN
cana-4790	103	204	-	-	PUNCT
cana-4790	103	205	lstm	lstm	PROPN
cana-4790	103	206	for	for	ADP
cana-4790	103	207	attention	attention	NOUN
cana-4790	103	208	eeg	eeg	NOUN
cana-4790	103	209	from	from	ADP
cana-4790	103	210	visual	visual	ADJ
cana-4790	103	211	tasks	task	NOUN
cana-4790	103	212	spatial	spatial	ADJ
cana-4790	103	213	+	+	CCONJ
cana-4790	103	214	temporal	temporal	ADJ
cana-4790	103	215	hybrid	hybrid	ADJ
cana-4790	103	216	model	model	NOUN
cana-4790	103	217	real	real	ADJ
cana-4790	103	218	-	-	PUNCT
cana-4790	103	219	time	time	NOUN
cana-4790	103	220	detection	detection	NOUN
cana-4790	103	221	of	of	ADP
cana-4790	103	222	attention	attention	NOUN
cana-4790	103	223	states	state	NOUN
cana-4790	103	224	[	[	X
cana-4790	103	225	19	19	NUM
cana-4790	103	226	]	]	X
cana-4790	103	227	cnn	cnn	PROPN
cana-4790	103	228	-	-	PUNCT
cana-4790	103	229	lstm	lstm	PROPN
cana-4790	103	230	for	for	ADP
cana-4790	103	231	mci	mci	NOUN
cana-4790	103	232	/	/	SYM
cana-4790	103	233	ad	ad	NOUN
cana-4790	103	234	resting	rest	VERB
cana-4790	103	235	-	-	PUNCT
cana-4790	103	236	state	state	NOUN
cana-4790	103	237	eeg	eeg	NOUN
cana-4790	103	238	2d	2d	NOUN
cana-4790	103	239	features	feature	NOUN
cana-4790	103	240	+	+	CCONJ
cana-4790	103	241	lstm	lstm	NOUN
cana-4790	103	242	superior	superior	ADJ
cana-4790	103	243	to	to	ADP
cana-4790	103	244	standalone	standalone	PROPN
cana-4790	103	245	cnn	cnn	PROPN
cana-4790	103	246	/	/	SYM
cana-4790	103	247	lstm	lstm	NOUN
cana-4790	103	248	[	[	X
cana-4790	103	249	20	20	NUM
cana-4790	103	250	]	]	X
cana-4790	103	251	cnn	cnn	PROPN
cana-4790	103	252	-	-	PUNCT
cana-4790	103	253	lstm	lstm	PROPN
cana-4790	103	254	for	for	ADP
cana-4790	103	255	ad	ad	NOUN
cana-4790	103	256	raw	raw	ADJ
cana-4790	103	257	eeg	eeg	PROPN
cana-4790	103	258	spectral	spectral	ADJ
cana-4790	103	259	+	+	CCONJ
cana-4790	103	260	temporal	temporal	ADJ
cana-4790	103	261	fusion	fusion	NOUN
cana-4790	103	262	high	high	ADJ
cana-4790	103	263	accuracy	accuracy	NOUN
cana-4790	103	264	;	;	PUNCT
cana-4790	103	265	good	good	ADJ
cana-4790	103	266	generalization	generalization	NOUN
cana-4790	103	267	3	3	NUM
cana-4790	103	268	.	.	PUNCT
cana-4790	103	269	methodology	methodology	NOUN
cana-4790	103	270	communications	communication	NOUN
cana-4790	103	271	on	on	ADP
cana-4790	103	272	applied	apply	VERB
cana-4790	103	273	nonlinear	nonlinear	ADJ
cana-4790	103	274	analysis	analysis	NOUN
cana-4790	103	275	issn	issn	NOUN
cana-4790	103	276	:	:	PUNCT
cana-4790	103	277	1074	1074	NUM
cana-4790	103	278	-	-	PUNCT
cana-4790	103	279	133x	133x	NUM
cana-4790	103	280	vol	vol	NOUN
cana-4790	103	281	31	31	NUM
cana-4790	103	282	no	no	NOUN
cana-4790	103	283	.	.	PUNCT
cana-4790	104	1	1s	1s	NUM
cana-4790	104	2	(	(	PUNCT
cana-4790	104	3	2024	2024	NUM
cana-4790	104	4	)	)	PUNCT
cana-4790	104	5	204	204	NUM
cana-4790	104	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	104	7	figure	figure	NOUN
cana-4790	104	8	1	1	NUM
cana-4790	104	9	.	.	PUNCT
cana-4790	105	1	cnn	cnn	PROPN
cana-4790	105	2	-	-	PUNCT
cana-4790	105	3	lstm	lstm	PROPN
cana-4790	105	4	model	model	NOUN
cana-4790	105	5	architecture	architecture	NOUN
cana-4790	105	6	3.1	3.1	NUM
cana-4790	105	7	data	datum	NOUN
cana-4790	105	8	collection	collection	NOUN
cana-4790	105	9	and	and	CCONJ
cana-4790	105	10	pre	pre	ADJ
cana-4790	105	11	-	-	ADJ
cana-4790	105	12	processing	processing	NOUN
cana-4790	105	13	in	in	ADP
cana-4790	105	14	this	this	DET
cana-4790	105	15	study	study	NOUN
cana-4790	105	16	,	,	PUNCT
cana-4790	105	17	eeg	eeg	PROPN
cana-4790	105	18	data	datum	NOUN
cana-4790	105	19	was	be	AUX
cana-4790	105	20	sourced	source	VERB
cana-4790	105	21	from	from	ADP
cana-4790	105	22	the	the	DET
cana-4790	105	23	publicly	publicly	ADV
cana-4790	105	24	available	available	ADJ
cana-4790	105	25	"	"	PUNCT
cana-4790	105	26	eeg	eeg	PROPN
cana-4790	105	27	of	of	ADP
cana-4790	105	28	alzheimer	alzheimer	PROPN
cana-4790	105	29	’s	’s	PART
cana-4790	105	30	and	and	CCONJ
cana-4790	105	31	frontotemporal	frontotemporal	ADJ
cana-4790	105	32	dementia	dementia	NOUN
cana-4790	105	33	"	"	PUNCT
cana-4790	105	34	dataset	dataset	NOUN
cana-4790	105	35	,	,	PUNCT
cana-4790	105	36	hosted	host	VERB
cana-4790	105	37	on	on	ADP
cana-4790	105	38	kaggle	kaggle	PROPN
cana-4790	105	39	[	[	X
cana-4790	105	40	1	1	NUM
cana-4790	105	41	]	]	PUNCT
cana-4790	105	42	.	.	PUNCT
cana-4790	106	1	the	the	DET
cana-4790	106	2	dataset	dataset	NOUN
cana-4790	106	3	consists	consist	VERB
cana-4790	106	4	of	of	ADP
cana-4790	106	5	88	88	NUM
cana-4790	106	6	subjects	subject	NOUN
cana-4790	106	7	categorized	categorize	VERB
cana-4790	106	8	as	as	SCONJ
cana-4790	106	9	follows	follow	VERB
cana-4790	106	10	:	:	PUNCT
cana-4790	106	11	•	•	NUM
cana-4790	106	12	36	36	NUM
cana-4790	106	13	subjects	subject	NOUN
cana-4790	106	14	diagnosed	diagnose	VERB
cana-4790	106	15	with	with	ADP
cana-4790	106	16	alzheimer	alzheimer	PROPN
cana-4790	106	17	’s	’s	PART
cana-4790	106	18	disease	disease	NOUN
cana-4790	106	19	(	(	PUNCT
cana-4790	106	20	ad	ad	NOUN
cana-4790	106	21	)	)	PUNCT
cana-4790	106	22	•	•	NOUN
cana-4790	106	23	12	12	NUM
cana-4790	106	24	subjects	subject	NOUN
cana-4790	106	25	diagnosed	diagnose	VERB
cana-4790	106	26	with	with	ADP
cana-4790	106	27	frontotemporal	frontotemporal	ADJ
cana-4790	106	28	dementia	dementia	NOUN
cana-4790	106	29	(	(	PUNCT
cana-4790	106	30	ftd	ftd	NOUN
cana-4790	106	31	)	)	PUNCT
cana-4790	107	1	•	•	NUM
cana-4790	107	2	40	40	NUM
cana-4790	107	3	healthy	healthy	ADJ
cana-4790	107	4	control	control	NOUN
cana-4790	107	5	subjects	subject	NOUN
cana-4790	107	6	each	each	DET
cana-4790	107	7	subject	subject	NOUN
cana-4790	107	8	's	's	PART
cana-4790	107	9	eeg	eeg	NOUN
cana-4790	107	10	was	be	AUX
cana-4790	107	11	recorded	record	VERB
cana-4790	107	12	in	in	ADP
cana-4790	107	13	a	a	DET
cana-4790	107	14	resting	rest	VERB
cana-4790	107	15	-	-	PUNCT
cana-4790	107	16	state	state	NOUN
cana-4790	107	17	,	,	PUNCT
cana-4790	107	18	eyes	eye	NOUN
cana-4790	107	19	-	-	PUNCT
cana-4790	107	20	closed	close	VERB
cana-4790	107	21	condition	condition	NOUN
cana-4790	107	22	using	use	VERB
cana-4790	107	23	a	a	DET
cana-4790	107	24	64	64	NUM
cana-4790	107	25	-	-	PUNCT
cana-4790	107	26	channel	channel	NOUN
cana-4790	107	27	setup	setup	NOUN
cana-4790	107	28	,	,	PUNCT
cana-4790	107	29	following	follow	VERB
cana-4790	107	30	the	the	DET
cana-4790	107	31	10–20	10–20	NUM
cana-4790	107	32	international	international	ADJ
cana-4790	107	33	electrode	electrode	NOUN
cana-4790	107	34	placement	placement	NOUN
cana-4790	107	35	system	system	NOUN
cana-4790	107	36	.	.	PUNCT
cana-4790	108	1	the	the	DET
cana-4790	108	2	recordings	recording	NOUN
cana-4790	108	3	are	be	AUX
cana-4790	108	4	stored	store	VERB
cana-4790	108	5	in	in	ADP
cana-4790	108	6	.mat	.mat	NOUN
cana-4790	108	7	format	format	NOUN
cana-4790	108	8	(	(	PUNCT
cana-4790	108	9	matlab	matlab	PROPN
cana-4790	108	10	file	file	NOUN
cana-4790	108	11	)	)	PUNCT
cana-4790	108	12	,	,	PUNCT
cana-4790	108	13	with	with	ADP
cana-4790	108	14	each	each	DET
cana-4790	108	15	file	file	NOUN
cana-4790	108	16	corresponding	correspond	VERB
cana-4790	108	17	to	to	ADP
cana-4790	108	18	a	a	DET
cana-4790	108	19	unique	unique	ADJ
cana-4790	108	20	subject	subject	NOUN
cana-4790	108	21	.	.	PUNCT
cana-4790	109	1	to	to	PART
cana-4790	109	2	ensure	ensure	VERB
cana-4790	109	3	the	the	DET
cana-4790	109	4	eeg	eeg	NOUN
cana-4790	109	5	signals	signal	NOUN
cana-4790	109	6	were	be	AUX
cana-4790	109	7	ready	ready	ADJ
cana-4790	109	8	for	for	ADP
cana-4790	109	9	deep	deep	ADJ
cana-4790	109	10	learning	learn	VERB
cana-4790	109	11	classification	classification	NOUN
cana-4790	109	12	,	,	PUNCT
cana-4790	109	13	the	the	DET
cana-4790	109	14	following	follow	VERB
cana-4790	109	15	preprocessing	preprocesse	VERB
cana-4790	109	16	steps	step	NOUN
cana-4790	109	17	were	be	AUX
cana-4790	109	18	employed	employ	VERB
cana-4790	109	19	:	:	PUNCT
cana-4790	109	20	step	step	NOUN
cana-4790	109	21	1	1	NUM
cana-4790	109	22	:	:	PUNCT
cana-4790	109	23	data	datum	NOUN
cana-4790	109	24	loading	loading	NOUN
cana-4790	109	25	and	and	CCONJ
cana-4790	109	26	initial	initial	ADJ
cana-4790	109	27	inspection	inspection	NOUN
cana-4790	109	28	the	the	DET
cana-4790	109	29	.mat	.mat	NOUN
cana-4790	109	30	files	file	NOUN
cana-4790	109	31	were	be	AUX
cana-4790	109	32	loaded	load	VERB
cana-4790	109	33	using	use	VERB
cana-4790	109	34	python	python	PROPN
cana-4790	109	35	’s	’s	PART
cana-4790	109	36	scipy.io.loadmat	scipy.io.loadmat	NOUN
cana-4790	109	37	(	(	PUNCT
cana-4790	109	38	)	)	PUNCT
cana-4790	109	39	function	function	NOUN
cana-4790	109	40	.	.	PUNCT
cana-4790	110	1	key	key	ADJ
cana-4790	110	2	metadata	metadata	NOUN
cana-4790	110	3	such	such	ADJ
cana-4790	110	4	as	as	ADP
cana-4790	110	5	:	:	PUNCT
cana-4790	110	6	•	•	NUM
cana-4790	110	7	the	the	DET
cana-4790	110	8	number	number	NOUN
cana-4790	110	9	of	of	ADP
cana-4790	110	10	eeg	eeg	NOUN
cana-4790	110	11	channels	channel	NOUN
cana-4790	110	12	,	,	PUNCT
cana-4790	110	13	•	•	ADP
cana-4790	110	14	the	the	DET
cana-4790	110	15	duration	duration	NOUN
cana-4790	110	16	of	of	ADP
cana-4790	110	17	recordings	recording	NOUN
cana-4790	110	18	,	,	PUNCT
cana-4790	110	19	•	•	NOUN
cana-4790	110	20	and	and	CCONJ
cana-4790	110	21	the	the	DET
cana-4790	110	22	sampling	sample	VERB
cana-4790	110	23	frequency	frequency	NOUN
cana-4790	110	24	(	(	PUNCT
cana-4790	110	25	typically	typically	ADV
cana-4790	110	26	256	256	NUM
cana-4790	110	27	hz	hz	NOUN
cana-4790	110	28	or	or	CCONJ
cana-4790	110	29	512	512	NUM
cana-4790	110	30	hz	hz	NOUN
cana-4790	110	31	)	)	PUNCT
cana-4790	110	32	,	,	PUNCT
cana-4790	110	33	were	be	AUX
cana-4790	110	34	extracted	extract	VERB
cana-4790	110	35	and	and	CCONJ
cana-4790	110	36	standardized	standardize	VERB
cana-4790	110	37	.	.	PUNCT
cana-4790	111	1	data	datum	NOUN
cana-4790	111	2	consistency	consistency	NOUN
cana-4790	111	3	across	across	ADP
cana-4790	111	4	subjects	subject	NOUN
cana-4790	111	5	was	be	AUX
cana-4790	111	6	ensured	ensure	VERB
cana-4790	111	7	by	by	ADP
cana-4790	111	8	validating	validate	VERB
cana-4790	111	9	shapes	shape	NOUN
cana-4790	111	10	and	and	CCONJ
cana-4790	111	11	dimensions	dimension	NOUN
cana-4790	111	12	.	.	PUNCT
cana-4790	112	1	step	step	NOUN
cana-4790	112	2	2	2	NUM
cana-4790	112	3	:	:	PUNCT
cana-4790	112	4	normalization	normalization	NOUN
cana-4790	112	5	(	(	PUNCT
cana-4790	112	6	standardization	standardization	NOUN
cana-4790	112	7	)	)	PUNCT
cana-4790	112	8	raw	raw	ADJ
cana-4790	112	9	eeg	eeg	NOUN
cana-4790	112	10	signals	signal	NOUN
cana-4790	112	11	vary	vary	VERB
cana-4790	112	12	in	in	ADP
cana-4790	112	13	amplitude	amplitude	NOUN
cana-4790	112	14	across	across	ADP
cana-4790	112	15	subjects	subject	NOUN
cana-4790	112	16	and	and	CCONJ
cana-4790	112	17	channels	channel	NOUN
cana-4790	112	18	,	,	PUNCT
cana-4790	112	19	which	which	PRON
cana-4790	112	20	can	can	AUX
cana-4790	112	21	negatively	negatively	ADV
cana-4790	112	22	impact	impact	VERB
cana-4790	112	23	training	training	NOUN
cana-4790	112	24	convergence	convergence	NOUN
cana-4790	112	25	.	.	PUNCT
cana-4790	113	1	to	to	PART
cana-4790	113	2	address	address	VERB
cana-4790	113	3	this	this	PRON
cana-4790	113	4	,	,	PUNCT
cana-4790	113	5	z	z	NOUN
cana-4790	113	6	-	-	PUNCT
cana-4790	113	7	score	score	NOUN
cana-4790	113	8	normalization	normalization	NOUN
cana-4790	113	9	was	be	AUX
cana-4790	113	10	applied	apply	VERB
cana-4790	113	11	to	to	ADP
cana-4790	113	12	each	each	DET
cana-4790	113	13	eeg	eeg	PROPN
cana-4790	113	14	channel	channel	NOUN
cana-4790	113	15	cic_i	cic_i	NOUN
cana-4790	113	16	using	using	NOUN
cana-4790	113	17	:	:	PUNCT
cana-4790	113	18	𝑥𝑛𝑜𝑟𝑚(𝑖	𝑥𝑛𝑜𝑟𝑚(𝑖	NOUN
cana-4790	113	19	)	)	PUNCT
cana-4790	113	20	=	=	SYM
cana-4790	113	21	𝑥(𝑖	𝑥(𝑖	PROPN
cana-4790	113	22	)	)	PUNCT
cana-4790	113	23	−	−	ADP
cana-4790	113	24	𝜇(𝑖)𝜎(𝑖	𝜇(𝑖)𝜎(𝑖	X
cana-4790	113	25	)	)	PUNCT
cana-4790	113	26	communications	communication	NOUN
cana-4790	113	27	on	on	ADP
cana-4790	113	28	applied	apply	VERB
cana-4790	113	29	nonlinear	nonlinear	ADJ
cana-4790	113	30	analysis	analysis	NOUN
cana-4790	113	31	issn	issn	NOUN
cana-4790	113	32	:	:	PUNCT
cana-4790	113	33	1074	1074	NUM
cana-4790	113	34	-	-	PUNCT
cana-4790	113	35	133x	133x	NUM
cana-4790	113	36	vol	vol	NOUN
cana-4790	113	37	31	31	NUM
cana-4790	113	38	no	no	NOUN
cana-4790	113	39	.	.	PUNCT
cana-4790	114	1	1s	1s	NUM
cana-4790	114	2	(	(	PUNCT
cana-4790	114	3	2024	2024	NUM
cana-4790	114	4	)	)	PUNCT
cana-4790	114	5	205	205	NUM
cana-4790	114	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	114	7	𝑥norm	𝑥norm	NOUN
cana-4790	114	8	(	(	PUNCT
cana-4790	114	9	𝑖	𝑖	X
cana-4790	114	10	)	)	PUNCT
cana-4790	114	11	=	=	SYM
cana-4790	114	12	𝑥(𝑖	𝑥(𝑖	PROPN
cana-4790	114	13	)	)	PUNCT
cana-4790	114	14	−	−	PROPN
cana-4790	114	15	μ(𝑖	μ(𝑖	PROPN
cana-4790	114	16	)	)	PUNCT
cana-4790	114	17	σ(𝑖	σ(𝑖	PROPN
cana-4790	114	18	)	)	PUNCT
cana-4790	114	19	where	where	SCONJ
cana-4790	114	20	:	:	PUNCT
cana-4790	114	21	•	•	NUM
cana-4790	114	22	𝒙(𝒊	𝒙(𝒊	PROPN
cana-4790	114	23	)	)	PUNCT
cana-4790	114	24	is	be	AUX
cana-4790	114	25	the	the	DET
cana-4790	114	26	eeg	eeg	PROPN
cana-4790	114	27	signal	signal	NOUN
cana-4790	114	28	from	from	ADP
cana-4790	114	29	channel	channel	PROPN
cana-4790	114	30	ii	ii	PROPN
cana-4790	114	31	,	,	PUNCT
cana-4790	114	32	•	•	ADP
cana-4790	114	33	𝛍(𝒊	𝛍(𝒊	PROPN
cana-4790	114	34	)	)	PUNCT
cana-4790	114	35	is	be	AUX
cana-4790	114	36	the	the	DET
cana-4790	114	37	mean	mean	NOUN
cana-4790	114	38	of	of	ADP
cana-4790	114	39	the	the	DET
cana-4790	114	40	signal	signal	NOUN
cana-4790	114	41	for	for	ADP
cana-4790	114	42	channel	channel	PROPN
cana-4790	114	43	ii	ii	PROPN
cana-4790	114	44	,	,	PUNCT
cana-4790	114	45	•	•	ADV
cana-4790	114	46	𝛔(𝒊	𝛔(𝒊	ADV
cana-4790	114	47	)	)	PUNCT
cana-4790	115	1	is	be	AUX
cana-4790	115	2	the	the	DET
cana-4790	115	3	standard	standard	ADJ
cana-4790	115	4	deviation	deviation	NOUN
cana-4790	115	5	of	of	ADP
cana-4790	115	6	the	the	DET
cana-4790	115	7	signal	signal	NOUN
cana-4790	115	8	for	for	ADP
cana-4790	115	9	channel	channel	PROPN
cana-4790	115	10	ii	ii	PROPN
cana-4790	115	11	.	.	PUNCT
cana-4790	116	1	this	this	PRON
cana-4790	116	2	transforms	transform	VERB
cana-4790	116	3	the	the	DET
cana-4790	116	4	signal	signal	NOUN
cana-4790	116	5	to	to	PART
cana-4790	116	6	have	have	VERB
cana-4790	116	7	zero	zero	NUM
cana-4790	116	8	mean	mean	NOUN
cana-4790	116	9	and	and	CCONJ
cana-4790	116	10	unit	unit	NOUN
cana-4790	116	11	variance	variance	NOUN
cana-4790	116	12	,	,	PUNCT
cana-4790	116	13	improving	improve	VERB
cana-4790	116	14	numerical	numerical	ADJ
cana-4790	116	15	stability	stability	NOUN
cana-4790	116	16	during	during	ADP
cana-4790	116	17	model	model	NOUN
cana-4790	116	18	training	training	NOUN
cana-4790	116	19	.	.	PUNCT
cana-4790	117	1	step	step	NOUN
cana-4790	117	2	3	3	NUM
cana-4790	117	3	:	:	PUNCT
cana-4790	117	4	bandpass	bandpass	NOUN
cana-4790	117	5	filtering	filter	VERB
cana-4790	117	6	to	to	PART
cana-4790	117	7	retain	retain	VERB
cana-4790	117	8	only	only	ADV
cana-4790	117	9	neurologically	neurologically	ADV
cana-4790	117	10	relevant	relevant	ADJ
cana-4790	117	11	frequencies	frequency	NOUN
cana-4790	117	12	and	and	CCONJ
cana-4790	117	13	remove	remove	VERB
cana-4790	117	14	noise	noise	NOUN
cana-4790	117	15	,	,	PUNCT
cana-4790	117	16	a	a	DET
cana-4790	117	17	bandpass	bandpass	NOUN
cana-4790	117	18	filter	filter	NOUN
cana-4790	117	19	was	be	AUX
cana-4790	117	20	applied	apply	VERB
cana-4790	117	21	to	to	ADP
cana-4790	117	22	all	all	DET
cana-4790	117	23	channels	channel	NOUN
cana-4790	117	24	.	.	PUNCT
cana-4790	118	1	the	the	DET
cana-4790	118	2	selected	select	VERB
cana-4790	118	3	passband	passband	NOUN
cana-4790	118	4	was	be	AUX
cana-4790	118	5	0.5	0.5	NUM
cana-4790	118	6	hz	hz	VERB
cana-4790	118	7	to	to	ADP
cana-4790	118	8	40	40	NUM
cana-4790	118	9	hz	hz	NOUN
cana-4790	118	10	,	,	PUNCT
cana-4790	118	11	covering	cover	VERB
cana-4790	118	12	the	the	DET
cana-4790	118	13	key	key	ADJ
cana-4790	118	14	eeg	eeg	NOUN
cana-4790	118	15	frequency	frequency	NOUN
cana-4790	118	16	bands	band	NOUN
cana-4790	118	17	:	:	PUNCT
cana-4790	118	18	•	•	NUM
cana-4790	118	19	delta	delta	NOUN
cana-4790	118	20	(	(	PUNCT
cana-4790	118	21	0.5–4	0.5–4	NOUN
cana-4790	118	22	hz	hz	NOUN
cana-4790	118	23	)	)	PUNCT
cana-4790	118	24	•	•	NOUN
cana-4790	118	25	theta	theta	NOUN
cana-4790	118	26	(	(	PUNCT
cana-4790	118	27	4–8	4–8	NUM
cana-4790	118	28	hz	hz	NOUN
cana-4790	118	29	)	)	PUNCT
cana-4790	118	30	•	•	NOUN
cana-4790	118	31	alpha	alpha	NOUN
cana-4790	118	32	(	(	PUNCT
cana-4790	118	33	8–13	8–13	NOUN
cana-4790	118	34	hz	hz	VERB
cana-4790	118	35	)	)	PUNCT
cana-4790	118	36	•	•	ADV
cana-4790	118	37	beta	beta	NOUN
cana-4790	118	38	(	(	PUNCT
cana-4790	118	39	13–30	13–30	NUM
cana-4790	118	40	hz	hz	ADJ
cana-4790	118	41	)	)	PUNCT
cana-4790	118	42	•	•	NUM
cana-4790	118	43	low	low	ADJ
cana-4790	118	44	gamma	gamma	NOUN
cana-4790	118	45	(	(	PUNCT
cana-4790	118	46	30–40	30–40	NUM
cana-4790	118	47	hz	hz	NOUN
cana-4790	118	48	)	)	PUNCT
cana-4790	118	49	the	the	DET
cana-4790	118	50	bandpass	bandpass	NOUN
cana-4790	118	51	filtering	filtering	NOUN
cana-4790	118	52	was	be	AUX
cana-4790	118	53	implemented	implement	VERB
cana-4790	118	54	using	use	VERB
cana-4790	118	55	a	a	DET
cana-4790	118	56	4th	4th	ADJ
cana-4790	118	57	-	-	PUNCT
cana-4790	118	58	order	order	NOUN
cana-4790	118	59	butterworth	butterworth	NOUN
cana-4790	118	60	filter	filter	NOUN
cana-4790	118	61	to	to	PART
cana-4790	118	62	preserve	preserve	VERB
cana-4790	118	63	temporal	temporal	ADJ
cana-4790	118	64	structure	structure	NOUN
cana-4790	118	65	.	.	PUNCT
cana-4790	119	1	step	step	NOUN
cana-4790	119	2	4	4	NUM
cana-4790	119	3	:	:	PUNCT
cana-4790	119	4	artifact	artifact	ADJ
cana-4790	119	5	removal	removal	NOUN
cana-4790	119	6	eeg	eeg	NOUN
cana-4790	119	7	is	be	AUX
cana-4790	119	8	prone	prone	ADJ
cana-4790	119	9	to	to	PART
cana-4790	119	10	noise	noise	VERB
cana-4790	119	11	due	due	ADP
cana-4790	119	12	to	to	ADP
cana-4790	119	13	muscle	muscle	NOUN
cana-4790	119	14	activity	activity	NOUN
cana-4790	119	15	,	,	PUNCT
cana-4790	119	16	eye	eye	NOUN
cana-4790	119	17	blinks	blink	VERB
cana-4790	119	18	,	,	PUNCT
cana-4790	119	19	and	and	CCONJ
cana-4790	119	20	external	external	ADJ
cana-4790	119	21	interference	interference	NOUN
cana-4790	119	22	.	.	PUNCT
cana-4790	120	1	to	to	PART
cana-4790	120	2	ensure	ensure	VERB
cana-4790	120	3	clean	clean	ADJ
cana-4790	120	4	signals	signal	NOUN
cana-4790	120	5	,	,	PUNCT
cana-4790	120	6	the	the	DET
cana-4790	120	7	following	follow	VERB
cana-4790	120	8	artifact	artifact	ADJ
cana-4790	120	9	rejection	rejection	NOUN
cana-4790	120	10	methods	method	NOUN
cana-4790	120	11	were	be	AUX
cana-4790	120	12	used	use	VERB
cana-4790	120	13	:	:	PUNCT
cana-4790	120	14	•	•	NUM
cana-4790	120	15	amplitude	amplitude	NOUN
cana-4790	120	16	thresholding	thresholding	NOUN
cana-4790	120	17	:	:	PUNCT
cana-4790	120	18	any	any	DET
cana-4790	120	19	segment	segment	NOUN
cana-4790	120	20	where	where	SCONJ
cana-4790	120	21	the	the	DET
cana-4790	120	22	signal	signal	ADJ
cana-4790	120	23	amplitude	amplitude	NOUN
cana-4790	120	24	exceeded	exceed	VERB
cana-4790	120	25	±100	±100	PUNCT
cana-4790	120	26	µv	µv	PRON
cana-4790	120	27	was	be	AUX
cana-4790	120	28	excluded	exclude	VERB
cana-4790	120	29	.	.	PUNCT
cana-4790	121	1	•	•	NUM
cana-4790	121	2	independent	independent	ADJ
cana-4790	121	3	component	component	NOUN
cana-4790	121	4	analysis	analysis	NOUN
cana-4790	121	5	(	(	PUNCT
cana-4790	121	6	ica	ica	PROPN
cana-4790	121	7	):	):	PUNCT
cana-4790	121	8	applied	apply	VERB
cana-4790	121	9	to	to	PART
cana-4790	121	10	separate	separate	VERB
cana-4790	121	11	and	and	CCONJ
cana-4790	121	12	remove	remove	VERB
cana-4790	121	13	components	component	NOUN
cana-4790	121	14	related	relate	VERB
cana-4790	121	15	to	to	ADP
cana-4790	121	16	eye	eye	NOUN
cana-4790	121	17	blinks	blink	NOUN
cana-4790	121	18	and	and	CCONJ
cana-4790	121	19	muscle	muscle	NOUN
cana-4790	121	20	movements	movement	NOUN
cana-4790	121	21	.	.	PUNCT
cana-4790	122	1	this	this	DET
cana-4790	122	2	step	step	NOUN
cana-4790	122	3	helped	helped	AUX
cana-4790	122	4	isolate	isolate	VERB
cana-4790	122	5	neural	neural	ADJ
cana-4790	122	6	activity	activity	NOUN
cana-4790	122	7	relevant	relevant	ADJ
cana-4790	122	8	to	to	ADP
cana-4790	122	9	cognitive	cognitive	ADJ
cana-4790	122	10	processing	processing	NOUN
cana-4790	122	11	while	while	SCONJ
cana-4790	122	12	discarding	discard	VERB
cana-4790	122	13	extraneous	extraneous	ADJ
cana-4790	122	14	noise	noise	NOUN
cana-4790	122	15	.	.	PUNCT
cana-4790	123	1	step	step	NOUN
cana-4790	123	2	5	5	NUM
cana-4790	123	3	:	:	PUNCT
cana-4790	123	4	segmentation	segmentation	NOUN
cana-4790	123	5	deep	deep	ADJ
cana-4790	123	6	learning	learning	NOUN
cana-4790	123	7	models	model	NOUN
cana-4790	123	8	require	require	VERB
cana-4790	123	9	fixed	fix	VERB
cana-4790	123	10	-	-	PUNCT
cana-4790	123	11	length	length	NOUN
cana-4790	123	12	input	input	NOUN
cana-4790	123	13	sequences	sequence	NOUN
cana-4790	123	14	.	.	PUNCT
cana-4790	124	1	thus	thus	ADV
cana-4790	124	2	,	,	PUNCT
cana-4790	124	3	the	the	DET
cana-4790	124	4	continuous	continuous	ADJ
cana-4790	124	5	eeg	eeg	NOUN
cana-4790	124	6	signal	signal	NOUN
cana-4790	124	7	was	be	AUX
cana-4790	124	8	divided	divide	VERB
cana-4790	124	9	into	into	ADP
cana-4790	124	10	overlapping	overlap	VERB
cana-4790	124	11	segments	segment	NOUN
cana-4790	124	12	(	(	PUNCT
cana-4790	124	13	windows	window	NOUN
cana-4790	124	14	)	)	PUNCT
cana-4790	124	15	.	.	PUNCT
cana-4790	125	1	each	each	DET
cana-4790	125	2	segment	segment	NOUN
cana-4790	125	3	contained	contain	VERB
cana-4790	125	4	temporal	temporal	ADJ
cana-4790	125	5	and	and	CCONJ
cana-4790	125	6	spatial	spatial	ADJ
cana-4790	125	7	information	information	NOUN
cana-4790	125	8	across	across	ADP
cana-4790	125	9	all	all	DET
cana-4790	125	10	channels	channel	NOUN
cana-4790	125	11	.	.	PUNCT
cana-4790	126	1	•	•	NUM
cana-4790	126	2	window	window	NOUN
cana-4790	126	3	size	size	NOUN
cana-4790	126	4	(	(	PUNCT
cana-4790	126	5	duration	duration	NOUN
cana-4790	126	6	):	):	PUNCT
cana-4790	126	7	w=2w	w=2w	NOUN
cana-4790	126	8	=	=	SYM
cana-4790	126	9	2	2	NUM
cana-4790	126	10	seconds	second	NOUN
cana-4790	126	11	•	•	NOUN
cana-4790	126	12	sampling	sample	VERB
cana-4790	126	13	rate	rate	NOUN
cana-4790	126	14	:	:	PUNCT
cana-4790	126	15	fs=256f_s	fs=256f_s	NOUN
cana-4790	126	16	=	=	NOUN
cana-4790	126	17	256	256	NUM
cana-4790	126	18	hz	hz	VERB
cana-4790	126	19	•	•	ADJ
cana-4790	126	20	samples	sample	NOUN
cana-4790	126	21	per	per	ADP
cana-4790	126	22	window	window	NOUN
cana-4790	126	23	:	:	PUNCT
cana-4790	126	24	s	s	X
cana-4790	126	25	=	=	NOUN
cana-4790	126	26	w×fs=512s	w×fs=512s	X
cana-4790	126	27	=	=	X
cana-4790	126	28	w	w	PROPN
cana-4790	126	29	\times	\time	NOUN
cana-4790	126	30	f_s	f_s	SYM
cana-4790	126	31	=	=	SYM
cana-4790	126	32	512	512	NUM
cana-4790	126	33	•	•	NUM
cana-4790	126	34	overlap	overlap	NOUN
cana-4790	126	35	:	:	PUNCT
cana-4790	126	36	50	50	NUM
cana-4790	126	37	%	%	NOUN
cana-4790	126	38	(	(	PUNCT
cana-4790	126	39	i.e.	i.e.	X
cana-4790	126	40	,	,	PUNCT
cana-4790	126	41	a	a	DET
cana-4790	126	42	stride	stride	NOUN
cana-4790	126	43	of	of	ADP
cana-4790	126	44	1	1	NUM
cana-4790	126	45	second	second	ADJ
cana-4790	126	46	)	)	PUNCT
cana-4790	126	47	each	each	DET
cana-4790	126	48	segment	segment	NOUN
cana-4790	126	49	had	have	VERB
cana-4790	126	50	a	a	DET
cana-4790	126	51	shape	shape	NOUN
cana-4790	126	52	of	of	ADP
cana-4790	126	53	(	(	PUNCT
cana-4790	126	54	64,512)(64	64,512)(64	NOUN
cana-4790	126	55	,	,	PUNCT
cana-4790	126	56	512	512	NUM
cana-4790	126	57	)	)	PUNCT
cana-4790	126	58	,	,	PUNCT
cana-4790	126	59	representing	represent	VERB
cana-4790	126	60	64	64	NUM
cana-4790	126	61	channels	channel	NOUN
cana-4790	126	62	(	(	PUNCT
cana-4790	126	63	spatial	spatial	ADJ
cana-4790	126	64	)	)	PUNCT
cana-4790	126	65	across	across	ADP
cana-4790	126	66	512	512	NUM
cana-4790	126	67	time	time	NOUN
cana-4790	126	68	samples	sample	NOUN
cana-4790	126	69	(	(	PUNCT
cana-4790	126	70	temporal	temporal	ADJ
cana-4790	126	71	)	)	PUNCT
cana-4790	126	72	.	.	PUNCT
cana-4790	127	1	𝑆𝑒𝑔𝑚𝑒𝑛𝑡(𝑡	𝑆𝑒𝑔𝑚𝑒𝑛𝑡(𝑡	NOUN
cana-4790	127	2	)	)	PUNCT
cana-4790	127	3	=	=	PUNCT
cana-4790	128	1	𝑋[𝑡	𝑋[𝑡	PROPN
cana-4790	128	2	:	:	PUNCT
cana-4790	129	1	𝑡	𝑡	PROPN
cana-4790	129	2	+	+	X
cana-4790	129	3	𝑆	𝑆	PROPN
cana-4790	129	4	]	]	PUNCT
cana-4790	129	5	𝑡	𝑡	X
cana-4790	129	6	=	=	NOUN
cana-4790	129	7	0	0	NUM
cana-4790	129	8	,	,	PUNCT
cana-4790	129	9	𝑆/2	𝑆/2	NOUN
cana-4790	129	10	,	,	PUNCT
cana-4790	129	11	𝑆	𝑆	PROPN
cana-4790	129	12	,	,	PUNCT
cana-4790	129	13	…	…	PUNCT
cana-4790	129	14	segment(𝑡	segment(𝑡	PROPN
cana-4790	129	15	)	)	PUNCT
cana-4790	129	16	=	=	PUNCT
cana-4790	129	17	𝑋[𝑡	𝑋[𝑡	PROPN
cana-4790	129	18	:	:	PUNCT
cana-4790	129	19	𝑡	𝑡	PROPN
cana-4790	129	20	+	+	X
cana-4790	129	21	𝑆	𝑆	PROPN
cana-4790	129	22	]	]	PUNCT
cana-4790	129	23	𝑡	𝑡	X
cana-4790	129	24	=	=	NOUN
cana-4790	129	25	0	0	NUM
cana-4790	129	26	,	,	PUNCT
cana-4790	129	27	𝑆/2	𝑆/2	NOUN
cana-4790	129	28	,	,	PUNCT
cana-4790	129	29	𝑆	𝑆	PROPN
cana-4790	129	30	where	where	SCONJ
cana-4790	129	31	xx	xx	PRON
cana-4790	129	32	is	be	AUX
cana-4790	129	33	the	the	DET
cana-4790	129	34	multichannel	multichannel	ADJ
cana-4790	129	35	eeg	eeg	PROPN
cana-4790	129	36	signal	signal	NOUN
cana-4790	129	37	.	.	PUNCT
cana-4790	130	1	step	step	NOUN
cana-4790	130	2	6	6	NUM
cana-4790	130	3	:	:	PUNCT
cana-4790	130	4	labelling	label	VERB
cana-4790	130	5	each	each	DET
cana-4790	130	6	eeg	eeg	NOUN
cana-4790	130	7	segment	segment	NOUN
cana-4790	130	8	was	be	AUX
cana-4790	130	9	labeled	label	VERB
cana-4790	130	10	according	accord	VERB
cana-4790	130	11	to	to	ADP
cana-4790	130	12	the	the	DET
cana-4790	130	13	subject	subject	NOUN
cana-4790	130	14	's	's	PART
cana-4790	130	15	diagnostic	diagnostic	ADJ
cana-4790	130	16	category	category	NOUN
cana-4790	130	17	.	.	PUNCT
cana-4790	131	1	for	for	ADP
cana-4790	131	2	binary	binary	ADJ
cana-4790	131	3	classification	classification	NOUN
cana-4790	131	4	in	in	ADP
cana-4790	131	5	this	this	DET
cana-4790	131	6	study	study	NOUN
cana-4790	131	7	,	,	PUNCT
cana-4790	131	8	we	we	PRON
cana-4790	131	9	focused	focus	VERB
cana-4790	131	10	only	only	ADV
cana-4790	131	11	on	on	ADP
cana-4790	131	12	alzheimer	alzheimer	PROPN
cana-4790	131	13	’s	’s	PART
cana-4790	131	14	disease	disease	NOUN
cana-4790	131	15	(	(	PUNCT
cana-4790	131	16	ad	ad	NOUN
cana-4790	131	17	)	)	PUNCT
cana-4790	131	18	vs.	vs.	X
cana-4790	131	19	control	control	NOUN
cana-4790	131	20	(	(	PUNCT
cana-4790	131	21	healthy	healthy	ADJ
cana-4790	131	22	)	)	PUNCT
cana-4790	131	23	.	.	PUNCT
cana-4790	132	1	ftd	ftd	PROPN
cana-4790	132	2	subjects	subject	NOUN
cana-4790	132	3	were	be	AUX
cana-4790	132	4	excluded	exclude	VERB
cana-4790	132	5	to	to	PART
cana-4790	132	6	maintain	maintain	VERB
cana-4790	132	7	a	a	DET
cana-4790	132	8	clear	clear	ADJ
cana-4790	132	9	binary	binary	ADJ
cana-4790	132	10	distinction	distinction	NOUN
cana-4790	132	11	.	.	PUNCT
cana-4790	133	1	communications	communication	NOUN
cana-4790	133	2	on	on	ADP
cana-4790	133	3	applied	apply	VERB
cana-4790	133	4	nonlinear	nonlinear	ADJ
cana-4790	133	5	analysis	analysis	NOUN
cana-4790	133	6	issn	issn	NOUN
cana-4790	133	7	:	:	PUNCT
cana-4790	133	8	1074	1074	NUM
cana-4790	133	9	-	-	PUNCT
cana-4790	133	10	133x	133x	NUM
cana-4790	133	11	vol	vol	NOUN
cana-4790	133	12	31	31	NUM
cana-4790	133	13	no	no	NOUN
cana-4790	133	14	.	.	PUNCT
cana-4790	134	1	1s	1s	NUM
cana-4790	134	2	(	(	PUNCT
cana-4790	134	3	2024	2024	NUM
cana-4790	134	4	)	)	PUNCT
cana-4790	134	5	206	206	NUM
cana-4790	134	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-4790	134	7	•	•	NOUN
cana-4790	134	8	ad	ad	NOUN
cana-4790	134	9	segments	segment	NOUN
cana-4790	134	10	:	:	PUNCT
cana-4790	134	11	label	label	NOUN
cana-4790	134	12	1	1	NUM
cana-4790	134	13	•	•	NOUN
cana-4790	134	14	control	control	NOUN
cana-4790	134	15	segments	segment	NOUN
cana-4790	134	16	:	:	PUNCT
cana-4790	134	17	label	label	NOUN
cana-4790	134	18	0	0	NUM
cana-4790	135	1	this	this	PRON
cana-4790	135	2	simplified	simplify	VERB
cana-4790	135	3	the	the	DET
cana-4790	135	4	classification	classification	NOUN
cana-4790	135	5	task	task	NOUN
cana-4790	135	6	and	and	CCONJ
cana-4790	135	7	reduced	reduce	VERB
cana-4790	135	8	inter	inter	ADJ
cana-4790	135	9	-	-	ADJ
cana-4790	135	10	class	class	ADJ
cana-4790	135	11	confusion	confusion	NOUN
cana-4790	135	12	.	.	PUNCT
cana-4790	136	1	step	step	NOUN
cana-4790	136	2	7	7	NUM
cana-4790	136	3	:	:	PUNCT
cana-4790	136	4	train	train	NOUN
cana-4790	136	5	-	-	PUNCT
cana-4790	136	6	test	test	NOUN
cana-4790	136	7	split	split	NOUN
cana-4790	136	8	to	to	PART
cana-4790	136	9	ensure	ensure	VERB
cana-4790	136	10	generalization	generalization	NOUN
cana-4790	136	11	and	and	CCONJ
cana-4790	136	12	prevent	prevent	VERB
cana-4790	136	13	data	datum	NOUN
cana-4790	136	14	leakage	leakage	NOUN
cana-4790	136	15	,	,	PUNCT
cana-4790	136	16	the	the	DET
cana-4790	136	17	dataset	dataset	NOUN
cana-4790	136	18	was	be	AUX
cana-4790	136	19	split	split	VERB
cana-4790	136	20	as	as	SCONJ
cana-4790	136	21	follows	follow	VERB
cana-4790	136	22	:	:	PUNCT
cana-4790	136	23	•	•	NUM
cana-4790	136	24	training	training	NOUN
cana-4790	136	25	set	set	NOUN
cana-4790	136	26	:	:	PUNCT
cana-4790	136	27	70	70	NUM
cana-4790	136	28	%	%	NOUN
cana-4790	136	29	•	•	NOUN
cana-4790	136	30	validation	validation	NOUN
cana-4790	136	31	set	set	NOUN
cana-4790	136	32	:	:	PUNCT
cana-4790	136	33	15	15	NUM
cana-4790	136	34	%	%	NOUN
cana-4790	136	35	•	•	NOUN
cana-4790	136	36	test	test	NOUN
cana-4790	136	37	set	set	NOUN
cana-4790	136	38	:	:	PUNCT
cana-4790	136	39	15	15	NUM
cana-4790	136	40	%	%	NOUN
cana-4790	136	41	the	the	DET
cana-4790	136	42	split	split	NOUN
cana-4790	136	43	was	be	AUX
cana-4790	136	44	subject	subject	ADJ
cana-4790	136	45	-	-	PUNCT
cana-4790	136	46	independent	independent	ADJ
cana-4790	136	47	—	—	PUNCT
cana-4790	136	48	ensuring	ensure	VERB
cana-4790	136	49	that	that	SCONJ
cana-4790	136	50	segments	segment	NOUN
cana-4790	136	51	from	from	ADP
cana-4790	136	52	the	the	DET
cana-4790	136	53	same	same	ADJ
cana-4790	136	54	subject	subject	NOUN
cana-4790	136	55	did	do	AUX
cana-4790	136	56	not	not	PART
cana-4790	136	57	appear	appear	VERB
cana-4790	136	58	in	in	ADP
cana-4790	136	59	both	both	DET
cana-4790	136	60	training	training	NOUN
cana-4790	136	61	and	and	CCONJ
cana-4790	136	62	testing	testing	NOUN
cana-4790	136	63	sets	set	NOUN
cana-4790	136	64	.	.	PUNCT
cana-4790	137	1	accuracy	accuracy	NOUN
cana-4790	138	1	=	=	SYM
cana-4790	138	2	correct	correct	ADJ
cana-4790	138	3	predictions	prediction	NOUN
cana-4790	138	4	total	total	ADJ
cana-4790	138	5	predictions	prediction	NOUN
cana-4790	138	6	,	,	PUNCT
cana-4790	138	7	only	only	ADV
cana-4790	138	8	on	on	ADP
cana-4790	138	9	unseen	unseen	ADJ
cana-4790	138	10	subjects	subject	NOUN
cana-4790	138	11	this	this	DET
cana-4790	138	12	strategy	strategy	NOUN
cana-4790	138	13	better	well	ADV
cana-4790	138	14	simulates	simulate	VERB
cana-4790	138	15	real	real	ADJ
cana-4790	138	16	-	-	PUNCT
cana-4790	138	17	world	world	NOUN
cana-4790	138	18	deployment	deployment	NOUN
cana-4790	138	19	,	,	PUNCT
cana-4790	138	20	where	where	SCONJ
cana-4790	138	21	new	new	ADJ
cana-4790	138	22	patients	patient	NOUN
cana-4790	138	23	are	be	AUX
cana-4790	138	24	classified	classify	VERB
cana-4790	138	25	based	base	VERB
cana-4790	138	26	on	on	ADP
cana-4790	138	27	previously	previously	ADV
cana-4790	138	28	unseen	unseen	ADJ
cana-4790	138	29	eeg	eeg	NOUN
cana-4790	138	30	data	datum	NOUN
cana-4790	138	31	.	.	PUNCT
cana-4790	139	1	step	step	NOUN
cana-4790	139	2	8	8	NUM
cana-4790	139	3	:	:	PUNCT
cana-4790	139	4	reshaping	reshape	VERB
cana-4790	139	5	and	and	CCONJ
cana-4790	139	6	input	input	NOUN
cana-4790	139	7	preparation	preparation	NOUN
cana-4790	139	8	each	each	DET
cana-4790	139	9	eeg	eeg	NOUN
cana-4790	139	10	segment	segment	NOUN
cana-4790	139	11	was	be	AUX
cana-4790	139	12	reshaped	reshape	VERB
cana-4790	139	13	to	to	PART
cana-4790	139	14	conform	conform	VERB
cana-4790	139	15	to	to	ADP
cana-4790	139	16	the	the	DET
cana-4790	139	17	input	input	NOUN
cana-4790	139	18	structure	structure	NOUN
cana-4790	139	19	expected	expect	VERB
cana-4790	139	20	by	by	ADP
cana-4790	139	21	the	the	DET
cana-4790	139	22	cnn	cnn	PROPN
cana-4790	139	23	-	-	PUNCT
cana-4790	139	24	lstm	lstm	PROPN
cana-4790	139	25	model	model	NOUN
cana-4790	139	26	:	:	PUNCT
cana-4790	139	27	𝐼𝑛𝑝𝑢𝑡	𝐼𝑛𝑝𝑢𝑡	PROPN
cana-4790	139	28	𝑆ℎ𝑎𝑝𝑒	𝑆ℎ𝑎𝑝𝑒	PROPN
cana-4790	139	29	=	=	PUNCT
cana-4790	139	30	(	(	PUNCT
cana-4790	139	31	𝑏𝑎𝑡𝑐ℎ	𝑏𝑎𝑡𝑐ℎ	ADJ
cana-4790	139	32	𝑠𝑖𝑧𝑒	𝑠𝑖𝑧𝑒	PROPN
cana-4790	139	33	,	,	PUNCT
cana-4790	139	34	𝑐ℎ𝑎𝑛𝑛𝑒𝑙𝑠	𝑐ℎ𝑎𝑛𝑛𝑒𝑙𝑠	NOUN
cana-4790	139	35	,	,	PUNCT
cana-4790	139	36	𝑡𝑖𝑚𝑒𝑠𝑡𝑒𝑝𝑠)input	𝑡𝑖𝑚𝑒𝑠𝑡𝑒𝑝𝑠)input	NUM
cana-4790	139	37	shape	shape	NOUN
cana-4790	139	38	=	=	SYM
cana-4790	139	39	(	(	PUNCT
cana-4790	139	40	batch	batch	NOUN
cana-4790	139	41	size	size	NOUN
cana-4790	139	42	,	,	PUNCT
cana-4790	139	43	channels	channel	NOUN
cana-4790	139	44	,	,	PUNCT
cana-4790	139	45	timesteps	timestep	NOUN
cana-4790	139	46	)	)	PUNCT
cana-4790	139	47	example	example	NOUN
cana-4790	139	48	:	:	PUNCT
cana-4790	139	49	•	•	NUM
cana-4790	139	50	channels=64	channels=64	NOUN
cana-4790	139	51	channels	channel	VERB
cana-4790	139	52	=	=	PUNCT
cana-4790	139	53	64	64	NUM
cana-4790	139	54	•	•	NOUN
cana-4790	139	55	timesteps=512	timesteps=512	NOUN
cana-4790	139	56	timesteps	timestep	NOUN
cana-4790	139	57	=	=	SYM
cana-4790	139	58	512	512	NUM
cana-4790	139	59	this	this	DET
cana-4790	139	60	structure	structure	NOUN
cana-4790	139	61	allows	allow	VERB
cana-4790	139	62	:	:	PUNCT
cana-4790	139	63	•	•	ADP
cana-4790	139	64	the	the	DET
cana-4790	139	65	cnn	cnn	PROPN
cana-4790	139	66	layers	layer	NOUN
cana-4790	139	67	to	to	PART
cana-4790	139	68	operate	operate	VERB
cana-4790	139	69	across	across	ADP
cana-4790	139	70	channels	channel	NOUN
cana-4790	139	71	(	(	PUNCT
cana-4790	139	72	spatial	spatial	ADJ
cana-4790	139	73	extraction	extraction	NOUN
cana-4790	139	74	)	)	PUNCT
cana-4790	139	75	,	,	PUNCT
cana-4790	139	76	and	and	CCONJ
cana-4790	139	77	•	•	NUM
cana-4790	139	78	the	the	DET
cana-4790	139	79	lstm	lstm	ADJ
cana-4790	139	80	layers	layer	NOUN
cana-4790	139	81	to	to	PART
cana-4790	139	82	capture	capture	VERB
cana-4790	139	83	how	how	SCONJ
cana-4790	139	84	the	the	DET
cana-4790	139	85	signal	signal	NOUN
cana-4790	139	86	evolves	evolve	VERB
cana-4790	139	87	over	over	ADP
cana-4790	139	88	time	time	NOUN
cana-4790	139	89	(	(	PUNCT
cana-4790	139	90	temporal	temporal	ADJ
cana-4790	139	91	learning	learning	NOUN
cana-4790	139	92	)	)	PUNCT
cana-4790	139	93	.	.	PUNCT
cana-4790	140	1	this	this	DET
cana-4790	140	2	comprehensive	comprehensive	ADJ
cana-4790	140	3	preprocessing	preprocessing	NOUN
cana-4790	140	4	ensures	ensure	VERB
cana-4790	140	5	that	that	SCONJ
cana-4790	140	6	the	the	DET
cana-4790	140	7	eeg	eeg	NOUN
cana-4790	140	8	signals	signal	NOUN
cana-4790	140	9	fed	feed	VERB
cana-4790	140	10	into	into	ADP
cana-4790	140	11	the	the	DET
cana-4790	140	12	model	model	NOUN
cana-4790	140	13	are	be	AUX
cana-4790	140	14	clean	clean	ADJ
cana-4790	140	15	,	,	PUNCT
cana-4790	140	16	normalized	normalize	VERB
cana-4790	140	17	,	,	PUNCT
cana-4790	140	18	and	and	CCONJ
cana-4790	140	19	correctly	correctly	ADV
cana-4790	140	20	formatted	format	VERB
cana-4790	140	21	for	for	ADP
cana-4790	140	22	deep	deep	ADJ
cana-4790	140	23	learning	learning	NOUN
cana-4790	140	24	.	.	PUNCT
cana-4790	141	1	the	the	DET
cana-4790	141	2	following	follow	VERB
cana-4790	141	3	section	section	NOUN
cana-4790	141	4	will	will	AUX
cana-4790	141	5	describe	describe	VERB
cana-4790	141	6	the	the	DET
cana-4790	141	7	cnn	cnn	PROPN
cana-4790	141	8	-	-	PUNCT
cana-4790	141	9	lstm	lstm	ADJ
cana-4790	141	10	model	model	NOUN
cana-4790	141	11	architecture	architecture	NOUN
cana-4790	141	12	developed	develop	VERB
cana-4790	141	13	to	to	PART
cana-4790	141	14	classify	classify	VERB
cana-4790	141	15	alzheimer	alzheimer	PROPN
cana-4790	141	16	’s	’s	PART
cana-4790	141	17	disease	disease	NOUN
cana-4790	141	18	from	from	ADP
cana-4790	141	19	eeg	eeg	PROPN
cana-4790	141	20	data	datum	NOUN
cana-4790	141	21	.	.	PUNCT
cana-4790	142	1	3.2	3.2	NUM
cana-4790	142	2	cnn	cnn	PROPN
cana-4790	142	3	-	-	PUNCT
cana-4790	142	4	lstm	lstm	ADJ
cana-4790	142	5	model	model	NOUN
cana-4790	142	6	architecture	architecture	NOUN
cana-4790	142	7	designed	design	VERB
cana-4790	142	8	to	to	PART
cana-4790	142	9	efficiently	efficiently	ADV
cana-4790	142	10	learn	learn	VERB
cana-4790	142	11	both	both	CCONJ
cana-4790	142	12	spatial	spatial	ADJ
cana-4790	142	13	and	and	CCONJ
cana-4790	142	14	temporal	temporal	ADJ
cana-4790	142	15	patterns	pattern	NOUN
cana-4790	142	16	buried	bury	VERB
cana-4790	142	17	in	in	ADP
cana-4790	142	18	eeg	eeg	PROPN
cana-4790	142	19	data	datum	NOUN
cana-4790	142	20	,	,	PUNCT
cana-4790	142	21	the	the	DET
cana-4790	142	22	hybrid	hybrid	ADJ
cana-4790	142	23	convolutional	convolutional	ADJ
cana-4790	142	24	neural	neural	ADJ
cana-4790	142	25	network	network	NOUN
cana-4790	142	26	long	long	ADJ
cana-4790	142	27	short	short	ADJ
cana-4790	142	28	-	-	PUNCT
cana-4790	142	29	term	term	NOUN
cana-4790	142	30	memory	memory	NOUN
cana-4790	142	31	(	(	PUNCT
cana-4790	142	32	cnn	cnn	PROPN
cana-4790	142	33	-	-	PUNCT
cana-4790	142	34	lstm	lstm	ADJ
cana-4790	142	35	)	)	PUNCT
cana-4790	142	36	architecture	architecture	NOUN
cana-4790	142	37	while	while	SCONJ
cana-4790	142	38	cnns	cnn	NOUN
cana-4790	142	39	are	be	AUX
cana-4790	142	40	good	good	ADJ
cana-4790	142	41	at	at	ADP
cana-4790	142	42	extracting	extract	VERB
cana-4790	142	43	spatial	spatial	ADJ
cana-4790	142	44	connections	connection	NOUN
cana-4790	142	45	across	across	ADP
cana-4790	142	46	electrode	electrode	NOUN
cana-4790	142	47	locations	location	NOUN
cana-4790	142	48	,	,	PUNCT
cana-4790	142	49	lstms	lstms	NOUN
cana-4790	142	50	are	be	AUX
cana-4790	142	51	strong	strong	ADJ
cana-4790	142	52	in	in	ADP
cana-4790	142	53	modelling	model	VERB
cana-4790	142	54	the	the	DET
cana-4790	142	55	sequential	sequential	ADJ
cana-4790	142	56	dependencies	dependency	NOUN
cana-4790	142	57	across	across	ADP
cana-4790	142	58	time	time	NOUN
cana-4790	142	59	.	.	PUNCT
cana-4790	143	1	the	the	DET
cana-4790	143	2	suggested	suggest	VERB
cana-4790	143	3	cnn	cnn	PROPN
cana-4790	143	4	-	-	PUNCT
cana-4790	143	5	lstm	lstm	PROPN
cana-4790	143	6	model	model	NOUN
cana-4790	143	7	classifies	classify	VERB
cana-4790	143	8	eeg	eeg	NOUN
cana-4790	143	9	segments	segment	NOUN
cana-4790	143	10	as	as	ADP
cana-4790	143	11	either	either	CCONJ
cana-4790	143	12	suggestive	suggestive	ADJ
cana-4790	143	13	of	of	ADP
cana-4790	143	14	alzheimer	alzheimer	PROPN
cana-4790	143	15	's	's	PART
cana-4790	143	16	disease	disease	NOUN
cana-4790	143	17	(	(	PUNCT
cana-4790	143	18	ad	ad	NOUN
cana-4790	143	19	)	)	PUNCT
cana-4790	143	20	or	or	CCONJ
cana-4790	143	21	not	not	PART
cana-4790	143	22	by	by	ADP
cana-4790	143	23	combining	combine	VERB
cana-4790	143	24	these	these	DET
cana-4790	143	25	strengths	strength	NOUN
cana-4790	143	26	.	.	PUNCT
cana-4790	144	1	3.2.1	3.2.1	NUM
cana-4790	144	2	architectural	architectural	ADJ
cana-4790	144	3	overview	overview	NOUN
cana-4790	144	4	the	the	DET
cana-4790	144	5	model	model	NOUN
cana-4790	144	6	is	be	AUX
cana-4790	144	7	composed	compose	VERB
cana-4790	144	8	of	of	ADP
cana-4790	144	9	the	the	DET
cana-4790	144	10	following	follow	VERB
cana-4790	144	11	key	key	ADJ
cana-4790	144	12	layers	layer	NOUN
cana-4790	144	13	:	:	PUNCT
cana-4790	144	14	1	1	X
cana-4790	144	15	.	.	PUNCT
cana-4790	144	16	input	input	NOUN
cana-4790	144	17	layer	layer	NOUN
cana-4790	144	18	input	input	NOUN
cana-4790	144	19	eeg	eeg	NOUN
cana-4790	144	20	segments	segment	NOUN
cana-4790	144	21	of	of	ADP
cana-4790	144	22	shape	shape	NOUN
cana-4790	144	23	(	(	PUNCT
cana-4790	144	24	c	c	NOUN
cana-4790	144	25	,	,	PUNCT
cana-4790	144	26	t)(c	t)(c	PROPN
cana-4790	144	27	,	,	PUNCT
cana-4790	144	28	t	t	PROPN
cana-4790	144	29	)	)	PUNCT
cana-4790	144	30	,	,	PUNCT
cana-4790	144	31	where	where	SCONJ
cana-4790	144	32	:	:	PUNCT
cana-4790	145	1	o	o	X
cana-4790	145	2	c=64c	c=64c	NOUN
cana-4790	145	3	=	=	SYM
cana-4790	145	4	64	64	NUM
cana-4790	145	5	:	:	PUNCT
cana-4790	145	6	number	number	NOUN
cana-4790	145	7	of	of	ADP
cana-4790	145	8	eeg	eeg	NOUN
cana-4790	145	9	channels	channel	NOUN
cana-4790	145	10	o	o	NOUN
cana-4790	145	11	t=512	t=512	NOUN
cana-4790	145	12	t	t	NOUN
cana-4790	145	13	=	=	SYM
cana-4790	145	14	512	512	NUM
cana-4790	145	15	:	:	PUNCT
cana-4790	145	16	number	number	NOUN
cana-4790	145	17	of	of	ADP
cana-4790	145	18	time	time	NOUN
cana-4790	145	19	samples	sample	NOUN
cana-4790	145	20	per	per	ADP
cana-4790	145	21	segment	segment	NOUN
cana-4790	145	22	the	the	DET
cana-4790	145	23	data	data	NOUN
cana-4790	145	24	is	be	AUX
cana-4790	145	25	reshaped	reshape	VERB
cana-4790	145	26	to	to	ADP
cana-4790	145	27	(	(	PUNCT
cana-4790	145	28	t	t	PROPN
cana-4790	145	29	,	,	PUNCT
cana-4790	145	30	c)(t	c)(t	X
cana-4790	145	31	,	,	PUNCT
cana-4790	145	32	c	c	NOUN
cana-4790	145	33	)	)	PUNCT
cana-4790	145	34	to	to	PART
cana-4790	145	35	fit	fit	VERB
cana-4790	145	36	the	the	DET
cana-4790	145	37	expected	expect	VERB
cana-4790	145	38	format	format	NOUN
cana-4790	145	39	for	for	ADP
cana-4790	145	40	temporal	temporal	ADJ
cana-4790	145	41	feature	feature	NOUN
cana-4790	145	42	extraction	extraction	NOUN
cana-4790	145	43	.	.	PUNCT
cana-4790	146	1	2	2	X
cana-4790	146	2	.	.	X
cana-4790	146	3	convolutional	convolutional	ADJ
cana-4790	146	4	blocks	block	NOUN
cana-4790	146	5	(	(	PUNCT
cana-4790	146	6	cnn	cnn	PROPN
cana-4790	146	7	)	)	PUNCT
cana-4790	146	8	communications	communication	NOUN
cana-4790	146	9	on	on	ADP
cana-4790	146	10	applied	apply	VERB
cana-4790	146	11	nonlinear	nonlinear	ADJ
cana-4790	146	12	analysis	analysis	NOUN
cana-4790	146	13	issn	issn	NOUN
cana-4790	146	14	:	:	PUNCT
cana-4790	146	15	1074	1074	NUM
cana-4790	146	16	-	-	PUNCT
cana-4790	146	17	133x	133x	NUM
cana-4790	146	18	vol	vol	NOUN
cana-4790	146	19	31	31	NUM
cana-4790	146	20	no	no	NOUN
cana-4790	146	21	.	.	PUNCT
cana-4790	147	1	1s	1s	NUM
cana-4790	147	2	(	(	PUNCT
cana-4790	147	3	2024	2024	NUM
cana-4790	147	4	)	)	PUNCT
cana-4790	147	5	207	207	NUM
cana-4790	148	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	148	2	o	o	NOUN
cana-4790	148	3	one	one	NUM
cana-4790	148	4	or	or	CCONJ
cana-4790	148	5	more	more	ADJ
cana-4790	148	6	1d	1d	NUM
cana-4790	148	7	convolution	convolution	NOUN
cana-4790	148	8	layers	layer	NOUN
cana-4790	148	9	are	be	AUX
cana-4790	148	10	applied	apply	VERB
cana-4790	148	11	along	along	ADP
cana-4790	148	12	the	the	DET
cana-4790	148	13	time	time	NOUN
cana-4790	148	14	axis	axis	NOUN
cana-4790	148	15	to	to	PART
cana-4790	148	16	extract	extract	VERB
cana-4790	148	17	local	local	ADJ
cana-4790	148	18	temporal	temporal	ADJ
cana-4790	148	19	patterns	pattern	NOUN
cana-4790	148	20	from	from	ADP
cana-4790	148	21	each	each	DET
cana-4790	148	22	channel	channel	NOUN
cana-4790	148	23	.	.	PUNCT
cana-4790	149	1	o	o	NOUN
cana-4790	149	2	followed	follow	VERB
cana-4790	149	3	by	by	ADP
cana-4790	149	4	batch	batch	NOUN
cana-4790	149	5	normalization	normalization	NOUN
cana-4790	149	6	,	,	PUNCT
cana-4790	149	7	relu	relu	NOUN
cana-4790	149	8	activation	activation	NOUN
cana-4790	149	9	,	,	PUNCT
cana-4790	149	10	and	and	CCONJ
cana-4790	149	11	max	max	PROPN
cana-4790	149	12	pooling	pooling	NOUN
cana-4790	149	13	.	.	PUNCT
cana-4790	150	1	3	3	X
cana-4790	150	2	.	.	NOUN
cana-4790	150	3	recurrent	recurrent	ADJ
cana-4790	150	4	block	block	NOUN
cana-4790	150	5	(	(	PUNCT
cana-4790	150	6	lstm	lstm	NOUN
cana-4790	150	7	)	)	PUNCT
cana-4790	150	8	o	o	NOUN
cana-4790	150	9	the	the	DET
cana-4790	150	10	output	output	NOUN
cana-4790	150	11	of	of	ADP
cana-4790	150	12	cnn	cnn	PROPN
cana-4790	150	13	layers	layer	NOUN
cana-4790	150	14	is	be	AUX
cana-4790	150	15	reshaped	reshape	VERB
cana-4790	150	16	and	and	CCONJ
cana-4790	150	17	passed	pass	VERB
cana-4790	150	18	into	into	ADP
cana-4790	150	19	a	a	DET
cana-4790	150	20	stacked	stack	VERB
cana-4790	150	21	lstm	lstm	NOUN
cana-4790	150	22	block	block	NOUN
cana-4790	150	23	to	to	PART
cana-4790	150	24	learn	learn	VERB
cana-4790	150	25	long	long	ADJ
cana-4790	150	26	-	-	PUNCT
cana-4790	150	27	range	range	NOUN
cana-4790	150	28	temporal	temporal	ADJ
cana-4790	150	29	dependencies	dependency	NOUN
cana-4790	150	30	.	.	PUNCT
cana-4790	151	1	4	4	X
cana-4790	151	2	.	.	X
cana-4790	151	3	fully	fully	ADV
cana-4790	151	4	connected	connect	VERB
cana-4790	151	5	layers	layer	NOUN
cana-4790	151	6	o	o	NOUN
cana-4790	151	7	dense	dense	ADJ
cana-4790	151	8	layers	layer	NOUN
cana-4790	151	9	convert	convert	VERB
cana-4790	151	10	high	high	ADJ
cana-4790	151	11	-	-	PUNCT
cana-4790	151	12	level	level	NOUN
cana-4790	151	13	features	feature	NOUN
cana-4790	151	14	into	into	ADP
cana-4790	151	15	class	class	NOUN
cana-4790	151	16	scores	score	NOUN
cana-4790	151	17	.	.	PUNCT
cana-4790	152	1	5	5	X
cana-4790	152	2	.	.	X
cana-4790	152	3	output	output	NOUN
cana-4790	152	4	layer	layer	NOUN
cana-4790	152	5	o	o	NOUN
cana-4790	152	6	a	a	DET
cana-4790	152	7	final	final	ADJ
cana-4790	152	8	softmax	softmax	NOUN
cana-4790	152	9	or	or	CCONJ
cana-4790	152	10	sigmoid	sigmoid	NOUN
cana-4790	152	11	activation	activation	NOUN
cana-4790	152	12	outputs	output	VERB
cana-4790	152	13	class	class	NOUN
cana-4790	152	14	probabilities	probability	NOUN
cana-4790	152	15	for	for	ADP
cana-4790	152	16	binary	binary	ADJ
cana-4790	152	17	classification	classification	NOUN
cana-4790	152	18	.	.	PUNCT
cana-4790	153	1	3.2.2	3.2.2	NUM
cana-4790	153	2	cnn	cnn	PROPN
cana-4790	153	3	-	-	PUNCT
cana-4790	153	4	lstm	lstm	ADJ
cana-4790	153	5	architecture	architecture	NOUN
cana-4790	153	6	table	table	NOUN
cana-4790	153	7	2	2	NUM
cana-4790	153	8	.	.	PUNCT
cana-4790	153	9	cnn	cnn	PROPN
cana-4790	153	10	-	-	PUNCT
cana-4790	153	11	lstm	lstm	PROPN
cana-4790	153	12	model	model	NOUN
cana-4790	153	13	layer	layer	NOUN
cana-4790	153	14	details	detail	NOUN
cana-4790	153	15	layer	layer	NOUN
cana-4790	153	16	type	type	NOUN
cana-4790	153	17	output	output	NOUN
cana-4790	153	18	shape	shape	NOUN
cana-4790	153	19	description	description	NOUN
cana-4790	153	20	input	input	NOUN
cana-4790	153	21	(	(	PUNCT
cana-4790	153	22	512	512	NUM
cana-4790	153	23	,	,	PUNCT
cana-4790	153	24	64	64	NUM
cana-4790	153	25	)	)	PUNCT
cana-4790	153	26	eeg	eeg	NOUN
cana-4790	153	27	segment	segment	NOUN
cana-4790	153	28	(	(	PUNCT
cana-4790	153	29	time	time	NOUN
cana-4790	153	30	steps	step	VERB
cana-4790	153	31	×	×	NOUN
cana-4790	153	32	channels	channel	NOUN
cana-4790	153	33	)	)	PUNCT
cana-4790	153	34	1d	1d	NUM
cana-4790	153	35	conv	conv	NOUN
cana-4790	153	36	(	(	PUNCT
cana-4790	153	37	filters=64	filters=64	PROPN
cana-4790	153	38	)	)	PUNCT
cana-4790	153	39	(	(	PUNCT
cana-4790	153	40	512	512	NUM
cana-4790	153	41	,	,	PUNCT
cana-4790	153	42	64	64	NUM
cana-4790	153	43	)	)	PUNCT
cana-4790	153	44	temporal	temporal	ADJ
cana-4790	153	45	convolution	convolution	NOUN
cana-4790	153	46	with	with	ADP
cana-4790	153	47	kernel	kernel	PROPN
cana-4790	153	48	size	size	NOUN
cana-4790	153	49	=	=	SYM
cana-4790	153	50	3	3	NUM
cana-4790	153	51	batchnorm	batchnorm	NOUN
cana-4790	153	52	+	+	CCONJ
cana-4790	153	53	relu	relu	NOUN
cana-4790	153	54	(	(	PUNCT
cana-4790	153	55	512	512	NUM
cana-4790	153	56	,	,	PUNCT
cana-4790	153	57	64	64	NUM
cana-4790	153	58	)	)	PUNCT
cana-4790	153	59	normalization	normalization	NOUN
cana-4790	153	60	and	and	CCONJ
cana-4790	153	61	non	non	ADJ
cana-4790	153	62	-	-	ADJ
cana-4790	153	63	linearity	linearity	ADJ
cana-4790	153	64	maxpooling1d	maxpooling1d	NOUN
cana-4790	153	65	(	(	PUNCT
cana-4790	153	66	256	256	NUM
cana-4790	153	67	,	,	PUNCT
cana-4790	153	68	64	64	NUM
cana-4790	153	69	)	)	PUNCT
cana-4790	153	70	reduces	reduce	VERB
cana-4790	153	71	temporal	temporal	ADJ
cana-4790	153	72	length	length	NOUN
cana-4790	153	73	by	by	ADP
cana-4790	153	74	factor	factor	NOUN
cana-4790	153	75	of	of	ADP
cana-4790	153	76	2	2	NUM
cana-4790	153	77	dropout	dropout	NOUN
cana-4790	153	78	(	(	PUNCT
cana-4790	153	79	0.3	0.3	NUM
cana-4790	153	80	)	)	PUNCT
cana-4790	153	81	(	(	PUNCT
cana-4790	153	82	256	256	NUM
cana-4790	153	83	,	,	PUNCT
cana-4790	153	84	64	64	NUM
cana-4790	153	85	)	)	PUNCT
cana-4790	153	86	regularization	regularization	NOUN
cana-4790	153	87	lstm	lstm	NOUN
cana-4790	153	88	(	(	PUNCT
cana-4790	153	89	units=100	units=100	NOUN
cana-4790	153	90	)	)	PUNCT
cana-4790	153	91	(	(	PUNCT
cana-4790	153	92	100	100	NUM
cana-4790	153	93	)	)	PUNCT
cana-4790	153	94	captures	capture	VERB
cana-4790	153	95	temporal	temporal	ADJ
cana-4790	153	96	sequence	sequence	NOUN
cana-4790	153	97	dependencies	dependencie	VERB
cana-4790	153	98	dense	dense	ADJ
cana-4790	153	99	(	(	PUNCT
cana-4790	153	100	units=64	units=64	NUM
cana-4790	153	101	)	)	PUNCT
cana-4790	153	102	(	(	PUNCT
cana-4790	153	103	64	64	NUM
cana-4790	153	104	)	)	PUNCT
cana-4790	154	1	fully	fully	ADV
cana-4790	154	2	connected	connect	VERB
cana-4790	154	3	layer	layer	NOUN
cana-4790	154	4	dropout	dropout	NOUN
cana-4790	154	5	(	(	PUNCT
cana-4790	154	6	0.3	0.3	NUM
cana-4790	154	7	)	)	PUNCT
cana-4790	154	8	(	(	PUNCT
cana-4790	154	9	64	64	NUM
cana-4790	154	10	)	)	PUNCT
cana-4790	154	11	further	further	ADJ
cana-4790	154	12	regularization	regularization	NOUN
cana-4790	154	13	output	output	NOUN
cana-4790	154	14	(	(	PUNCT
cana-4790	154	15	sigmoid	sigmoid	NOUN
cana-4790	154	16	)	)	PUNCT
cana-4790	154	17	(	(	PUNCT
cana-4790	154	18	1	1	X
cana-4790	154	19	)	)	PUNCT
cana-4790	154	20	binary	binary	ADJ
cana-4790	154	21	prediction	prediction	NOUN
cana-4790	154	22	(	(	PUNCT
cana-4790	154	23	ad	ad	NOUN
cana-4790	154	24	=	=	SYM
cana-4790	154	25	1	1	NUM
cana-4790	154	26	,	,	PUNCT
cana-4790	154	27	control	control	NOUN
cana-4790	154	28	=	=	SYM
cana-4790	154	29	0	0	NUM
cana-4790	154	30	)	)	PUNCT
cana-4790	154	31	3.2.3	3.2.3	NUM
cana-4790	154	32	mathematical	mathematical	ADJ
cana-4790	154	33	formulation	formulation	NOUN
cana-4790	154	34	let	let	VERB
cana-4790	154	35	the	the	DET
cana-4790	154	36	eeg	eeg	NOUN
cana-4790	154	37	segment	segment	NOUN
cana-4790	154	38	be	be	AUX
cana-4790	154	39	represented	represent	VERB
cana-4790	154	40	by	by	ADP
cana-4790	154	41	matrix	matrix	NOUN
cana-4790	154	42	:	:	PUNCT
cana-4790	154	43	𝑿	𝑿	PROPN
cana-4790	154	44	∈	∈	PROPN
cana-4790	154	45	𝑹𝑪	𝑹𝑪	NOUN
cana-4790	154	46	×	×	NOUN
cana-4790	154	47	𝑻𝑿	𝑻𝑿	NOUN
cana-4790	154	48	∈	∈	PROPN
cana-4790	154	49	𝑹𝑪×𝑻	𝑹𝑪×𝑻	PROPN
cana-4790	154	50	where	where	SCONJ
cana-4790	154	51	:	:	PUNCT
cana-4790	154	52	•	•	NUM
cana-4790	154	53	cc	cc	NOUN
cana-4790	154	54	=	=	NOUN
cana-4790	154	55	number	number	NOUN
cana-4790	154	56	of	of	ADP
cana-4790	154	57	channels	channel	NOUN
cana-4790	154	58	•	•	ADP
cana-4790	154	59	tt	tt	PROPN
cana-4790	154	60	=	=	NOUN
cana-4790	154	61	number	number	NOUN
cana-4790	154	62	of	of	ADP
cana-4790	154	63	time	time	NOUN
cana-4790	154	64	points	point	NOUN
cana-4790	154	65	convolution	convolution	NOUN
cana-4790	154	66	operation	operation	NOUN
cana-4790	154	67	for	for	ADP
cana-4790	154	68	a	a	DET
cana-4790	154	69	1d	1d	NUM
cana-4790	154	70	convolution	convolution	NOUN
cana-4790	154	71	layer	layer	NOUN
cana-4790	154	72	with	with	ADP
cana-4790	154	73	filter	filter	NOUN
cana-4790	154	74	ff	ff	NOUN
cana-4790	154	75	,	,	PUNCT
cana-4790	154	76	kernel	kernel	PROPN
cana-4790	154	77	size	size	NOUN
cana-4790	154	78	kk	kk	PROPN
cana-4790	154	79	,	,	PUNCT
cana-4790	154	80	and	and	CCONJ
cana-4790	154	81	stride	stride	VERB
cana-4790	154	82	ss	ss	NOUN
cana-4790	154	83	:	:	PUNCT
cana-4790	155	1	𝑍𝑡	𝑍𝑡	PROPN
cana-4790	155	2	=	=	SYM
cana-4790	155	3	𝑓	𝑓	DET
cana-4790	155	4	∗	∗	NOUN
cana-4790	155	5	𝑋𝑡	𝑋𝑡	NOUN
cana-4790	155	6	=	=	PUNCT
cana-4790	155	7	∑	∑	PUNCT
cana-4790	155	8	𝑖	𝑖	SYM
cana-4790	155	9	=	=	PUNCT
cana-4790	155	10	0𝑘	0𝑘	NOUN
cana-4790	155	11	−	−	NOUN
cana-4790	155	12	1𝑤𝑖	1𝑤𝑖	ADJ
cana-4790	155	13	⋅	⋅	PROPN
cana-4790	155	14	𝑋𝑡	𝑋𝑡	PROPN
cana-4790	155	15	+	+	NOUN
cana-4790	155	16	𝑖	𝑖	X
cana-4790	155	17	+	+	X
cana-4790	155	18	𝑏	𝑏	NOUN
cana-4790	155	19	𝑍𝑡	𝑍𝑡	NOUN
cana-4790	155	20	=	=	PUNCT
cana-4790	155	21	𝑓	𝑓	DET
cana-4790	155	22	∗	∗	NOUN
cana-4790	155	23	𝑋𝑡	𝑋𝑡	PROPN
cana-4790	155	24	=	=	PUNCT
cana-4790	155	25	∑	∑	PROPN
cana-4790	155	26	𝑤𝑖	𝑤𝑖	ADP
cana-4790	155	27	𝑘−1	𝑘−1	PROPN
cana-4790	155	28	𝑖=0	𝑖=0	PROPN
cana-4790	155	29	⋅	⋅	PROPN
cana-4790	155	30	𝑋𝑡+𝑖	𝑋𝑡+𝑖	NOUN
cana-4790	155	31	+	+	CCONJ
cana-4790	155	32	𝑏	𝑏	NOUN
cana-4790	155	33	where	where	SCONJ
cana-4790	155	34	wiw_i	wiw_i	NOUN
cana-4790	155	35	are	be	AUX
cana-4790	155	36	filter	filter	NOUN
cana-4790	155	37	weights	weight	NOUN
cana-4790	155	38	and	and	CCONJ
cana-4790	155	39	bb	bb	NOUN
cana-4790	155	40	is	be	AUX
cana-4790	155	41	bias	bias	NOUN
cana-4790	155	42	.	.	PUNCT
cana-4790	156	1	the	the	DET
cana-4790	156	2	output	output	NOUN
cana-4790	156	3	is	be	AUX
cana-4790	156	4	passed	pass	VERB
cana-4790	156	5	through	through	ADP
cana-4790	156	6	a	a	DET
cana-4790	156	7	relu	relu	NOUN
cana-4790	156	8	activation	activation	NOUN
cana-4790	156	9	:	:	PUNCT
cana-4790	156	10	at	at	ADP
cana-4790	156	11	=	=	PUNCT
cana-4790	156	12	relu(zt	relu(zt	NOUN
cana-4790	156	13	)	)	PUNCT
cana-4790	156	14	=	=	SYM
cana-4790	156	15	ma	ma	PROPN
cana-4790	156	16	x(0	x(0	PROPN
cana-4790	156	17	,	,	PUNCT
cana-4790	156	18	zt	zt	PROPN
cana-4790	156	19	)	)	PUNCT
cana-4790	156	20	lstm	lstm	NOUN
cana-4790	156	21	layer	layer	NOUN
cana-4790	156	22	given	give	VERB
cana-4790	156	23	input	input	NOUN
cana-4790	156	24	sequence	sequence	NOUN
cana-4790	156	25	{	{	PUNCT
cana-4790	156	26	x1,x2,	x1,x2,	PROPN
cana-4790	156	27	…	…	SYM
cana-4790	156	28	,xt}\{x_1	,xt}\{x_1	PROPN
cana-4790	156	29	,	,	PUNCT
cana-4790	156	30	x_2	x_2	PROPN
cana-4790	156	31	,	,	PUNCT
cana-4790	156	32	\ldots	\ldot	NOUN
cana-4790	156	33	,	,	PUNCT
cana-4790	156	34	x_t\	x_t\	NUM
cana-4790	156	35	}	}	PUNCT
cana-4790	156	36	,	,	PUNCT
cana-4790	156	37	the	the	DET
cana-4790	156	38	lstm	lstm	NOUN
cana-4790	156	39	computes	compute	VERB
cana-4790	156	40	hidden	hidden	ADJ
cana-4790	156	41	states	state	NOUN
cana-4790	156	42	hth_t	hth_t	VERB
cana-4790	156	43	as	as	ADP
cana-4790	156	44	:	:	PUNCT
cana-4790	156	45	ft	ft	NOUN
cana-4790	156	46	=	=	PUNCT
cana-4790	156	47	σ(wfxt	σ(wfxt	PROPN
cana-4790	156	48	+	+	CCONJ
cana-4790	156	49	ufht	ufht	ADJ
cana-4790	156	50	−	−	PROPN
cana-4790	156	51	1	1	NUM
cana-4790	156	52	+	+	NUM
cana-4790	156	53	bf	bf	NOUN
cana-4790	156	54	)	)	PUNCT
cana-4790	156	55	communications	communication	NOUN
cana-4790	156	56	on	on	ADP
cana-4790	156	57	applied	apply	VERB
cana-4790	156	58	nonlinear	nonlinear	ADJ
cana-4790	156	59	analysis	analysis	NOUN
cana-4790	156	60	issn	issn	NOUN
cana-4790	156	61	:	:	PUNCT
cana-4790	156	62	1074	1074	NUM
cana-4790	156	63	-	-	PUNCT
cana-4790	156	64	133x	133x	NUM
cana-4790	156	65	vol	vol	NOUN
cana-4790	156	66	31	31	NUM
cana-4790	156	67	no	no	NOUN
cana-4790	156	68	.	.	PUNCT
cana-4790	157	1	1s	1s	NUM
cana-4790	157	2	(	(	PUNCT
cana-4790	157	3	2024	2024	NUM
cana-4790	157	4	)	)	PUNCT
cana-4790	157	5	208	208	NUM
cana-4790	157	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	157	7	it	it	PRON
cana-4790	157	8	=	=	PUNCT
cana-4790	157	9	σ(wixt	σ(wixt	NOUN
cana-4790	157	10	+	+	CCONJ
cana-4790	157	11	uiht	uiht	ADJ
cana-4790	157	12	−	−	PROPN
cana-4790	157	13	1	1	NUM
cana-4790	157	14	+	+	CCONJ
cana-4790	157	15	bi	bi	NOUN
cana-4790	157	16	)	)	PUNCT
cana-4790	157	17	ot	ot	NOUN
cana-4790	157	18	=	=	PUNCT
cana-4790	157	19	σ(woxt	σ(woxt	NOUN
cana-4790	158	1	+	+	CCONJ
cana-4790	158	2	uoht	uoht	ADV
cana-4790	158	3	−	−	ADP
cana-4790	158	4	1	1	NUM
cana-4790	158	5	+	+	CCONJ
cana-4790	158	6	bo	bo	NOUN
cana-4790	158	7	)	)	PUNCT
cana-4790	158	8	c	c	X
cana-4790	158	9	~	~	SYM
cana-4790	158	10	t	t	NOUN
cana-4790	158	11	=	=	SYM
cana-4790	158	12	tan	tan	ADJ
cana-4790	158	13	h(wcxt	h(wcxt	NOUN
cana-4790	158	14	+	+	CCONJ
cana-4790	158	15	ucht	ucht	NOUN
cana-4790	158	16	−	−	PROPN
cana-4790	158	17	1	1	NUM
cana-4790	158	18	+	+	CCONJ
cana-4790	158	19	bc	bc	PROPN
cana-4790	158	20	)	)	PUNCT
cana-4790	158	21	ct	ct	PROPN
cana-4790	158	22	=	=	PUNCT
cana-4790	158	23	ft	ft	PROPN
cana-4790	158	24	⊙	⊙	PROPN
cana-4790	158	25	ct	ct	PROPN
cana-4790	159	1	−	−	PROPN
cana-4790	159	2	1	1	NUM
cana-4790	159	3	+	+	CCONJ
cana-4790	159	4	it	it	PRON
cana-4790	159	5	⊙	⊙	VERB
cana-4790	160	1	c	c	X
cana-4790	160	2	~	~	PROPN
cana-4790	160	3	th	th	X
cana-4790	160	4	where	where	SCONJ
cana-4790	160	5	:	:	PUNCT
cana-4790	160	6	•	•	NUM
cana-4790	160	7	σ	σ	X
cana-4790	160	8	sigma	sigma	PROPN
cana-4790	160	9	:	:	PUNCT
cana-4790	160	10	sigmoid	sigmoid	NOUN
cana-4790	160	11	activation	activation	NOUN
cana-4790	160	12	•	•	NUM
cana-4790	160	13	⊙	⊙	NOUN
cana-4790	160	14	:	:	PUNCT
cana-4790	160	15	element	element	ADJ
cana-4790	160	16	-	-	ADJ
cana-4790	160	17	wise	wise	ADJ
cana-4790	160	18	multiplication	multiplication	NOUN
cana-4790	160	19	the	the	DET
cana-4790	160	20	final	final	ADJ
cana-4790	160	21	hidden	hidden	ADJ
cana-4790	160	22	state	state	NOUN
cana-4790	160	23	hth_t	hth_t	PROPN
cana-4790	160	24	is	be	AUX
cana-4790	160	25	used	use	VERB
cana-4790	160	26	as	as	ADP
cana-4790	160	27	the	the	DET
cana-4790	160	28	temporal	temporal	ADJ
cana-4790	160	29	representation	representation	NOUN
cana-4790	160	30	of	of	ADP
cana-4790	160	31	the	the	DET
cana-4790	160	32	input	input	NOUN
cana-4790	160	33	.	.	PUNCT
cana-4790	161	1	output	output	NOUN
cana-4790	161	2	layer	layer	NOUN
cana-4790	161	3	�	�	PROPN
cana-4790	161	4	̂	̂	VERB
cana-4790	161	5	�	�	PROPN
cana-4790	161	6	=	=	SYM
cana-4790	161	7	𝛔(𝐖𝐨𝐡𝐓	𝛔(𝐖𝐨𝐡𝐓	PROPN
cana-4790	161	8	+	+	CCONJ
cana-4790	161	9	𝐛𝐨	𝐛𝐨	PROPN
cana-4790	161	10	)	)	PUNCT
cana-4790	161	11	where	where	SCONJ
cana-4790	161	12	:	:	PUNCT
cana-4790	161	13	•	•	NUM
cana-4790	161	14	𝑦	𝑦	NOUN
cana-4790	161	15	�	�	NOUN
cana-4790	161	16	̂	̂	SYM
cana-4790	161	17	�	�	PROPN
cana-4790	161	18	:	:	PUNCT
cana-4790	161	19	predicted	predict	VERB
cana-4790	161	20	probability	probability	NOUN
cana-4790	161	21	for	for	ADP
cana-4790	161	22	ad	ad	NOUN
cana-4790	161	23	•	•	ADP
cana-4790	161	24	𝑊𝑜	𝑊𝑜	PROPN
cana-4790	161	25	,	,	PUNCT
cana-4790	161	26	𝑏𝑜	𝑏𝑜	ADJ
cana-4790	161	27	:	:	PUNCT
cana-4790	161	28	weights	weight	NOUN
cana-4790	161	29	and	and	CCONJ
cana-4790	161	30	bias	bias	NOUN
cana-4790	161	31	of	of	ADP
cana-4790	161	32	output	output	NOUN
cana-4790	161	33	layer	layer	NOUN
cana-4790	161	34	3.2.4	3.2.4	NUM
cana-4790	161	35	loss	loss	NOUN
cana-4790	161	36	function	function	NOUN
cana-4790	161	37	and	and	CCONJ
cana-4790	161	38	optimization	optimization	NOUN
cana-4790	161	39	for	for	ADP
cana-4790	161	40	binary	binary	ADJ
cana-4790	161	41	classification	classification	NOUN
cana-4790	161	42	,	,	PUNCT
cana-4790	161	43	the	the	DET
cana-4790	161	44	model	model	NOUN
cana-4790	161	45	uses	use	VERB
cana-4790	161	46	binary	binary	PROPN
cana-4790	161	47	cross	cross	PROPN
cana-4790	161	48	-	-	ADJ
cana-4790	161	49	entropy	entropy	ADJ
cana-4790	161	50	loss	loss	NOUN
cana-4790	161	51	:	:	PUNCT
cana-4790	161	52	𝑳	𝑳	NOUN
cana-4790	161	53	=	=	SYM
cana-4790	161	54	−[𝒚	−[𝒚	PROPN
cana-4790	161	55	⋅	⋅	PROPN
cana-4790	161	56	𝒍𝒐	𝒍𝒐	ADP
cana-4790	161	57	𝒈(𝒚′	𝒈(𝒚′	PROPN
cana-4790	161	58	)	)	PUNCT
cana-4790	162	1	+	+	CCONJ
cana-4790	162	2	(	(	PUNCT
cana-4790	162	3	𝟏	𝟏	NUM
cana-4790	162	4	−	−	NUM
cana-4790	162	5	𝒚	𝒚	NOUN
cana-4790	162	6	)	)	PUNCT
cana-4790	162	7	⋅	⋅	PROPN
cana-4790	162	8	𝒍𝒐	𝒍𝒐	ADP
cana-4790	162	9	𝒈(𝟏	𝒈(𝟏	NOUN
cana-4790	162	10	−	−	NOUN
cana-4790	162	11	𝒚′	𝒚′	NUM
cana-4790	162	12	)	)	PUNCT
cana-4790	162	13	]	]	PUNCT
cana-4790	163	1	𝓛	𝓛	PROPN
cana-4790	163	2	=	=	PUNCT
cana-4790	163	3	−[𝒚	−[𝒚	PROPN
cana-4790	163	4	⋅	⋅	PROPN
cana-4790	163	5	𝐥𝐨	𝐥𝐨	ADP
cana-4790	163	6	𝐠(	𝐠(	NUM
cana-4790	163	7	�	�	PROPN
cana-4790	163	8	̂	̂	NUM
cana-4790	163	9	�	�	NOUN
cana-4790	163	10	)	)	PUNCT
cana-4790	163	11	+	+	CCONJ
cana-4790	163	12	(	(	PUNCT
cana-4790	163	13	𝟏	𝟏	NUM
cana-4790	163	14	−	−	NUM
cana-4790	163	15	𝒚	𝒚	NOUN
cana-4790	163	16	)	)	PUNCT
cana-4790	163	17	⋅	⋅	PROPN
cana-4790	163	18	𝐥𝐨	𝐥𝐨	ADP
cana-4790	163	19	𝐠(𝟏	𝐠(𝟏	PROPN
cana-4790	163	20	−	−	PROPN
cana-4790	163	21	�	�	PROPN
cana-4790	163	22	̂	̂	NOUN
cana-4790	163	23	�	�	NOUN
cana-4790	163	24	)	)	PUNCT
cana-4790	163	25	]	]	PUNCT
cana-4790	163	26	where	where	SCONJ
cana-4790	163	27	y∈{0,1}y	y∈{0,1}y	PROPN
cana-4790	163	28	\in	\in	PROPN
cana-4790	163	29	\{0	\{0	NOUN
cana-4790	163	30	,	,	PUNCT
cana-4790	163	31	1\	1\	NUM
cana-4790	163	32	}	}	PUNCT
cana-4790	163	33	is	be	AUX
cana-4790	163	34	the	the	DET
cana-4790	163	35	true	true	ADJ
cana-4790	163	36	label	label	NOUN
cana-4790	163	37	,	,	PUNCT
cana-4790	163	38	and	and	CCONJ
cana-4790	163	39	y^\hat{y	y^\hat{y	VERB
cana-4790	163	40	}	}	PUNCT
cana-4790	163	41	is	be	AUX
cana-4790	163	42	the	the	DET
cana-4790	163	43	predicted	predict	VERB
cana-4790	163	44	probability	probability	NOUN
cana-4790	163	45	.	.	PUNCT
cana-4790	164	1	the	the	DET
cana-4790	164	2	model	model	NOUN
cana-4790	164	3	is	be	AUX
cana-4790	164	4	optimized	optimize	VERB
cana-4790	164	5	using	use	VERB
cana-4790	164	6	the	the	DET
cana-4790	164	7	adam	adam	PROPN
cana-4790	164	8	optimizer	optimizer	NOUN
cana-4790	164	9	with	with	ADP
cana-4790	164	10	default	default	NOUN
cana-4790	164	11	learning	learning	NOUN
cana-4790	164	12	rate	rate	NOUN
cana-4790	164	13	α=0.001\alpha	α=0.001\alpha	PROPN
cana-4790	165	1	=	=	NOUN
cana-4790	165	2	0.001	0.001	NUM
cana-4790	165	3	.	.	PUNCT
cana-4790	165	4	early	early	ADJ
cana-4790	165	5	stopping	stopping	NOUN
cana-4790	165	6	and	and	CCONJ
cana-4790	165	7	dropout	dropout	NOUN
cana-4790	165	8	regularization	regularization	NOUN
cana-4790	165	9	are	be	AUX
cana-4790	165	10	applied	apply	VERB
cana-4790	165	11	to	to	PART
cana-4790	165	12	prevent	prevent	VERB
cana-4790	165	13	overfitting	overfitting	NOUN
cana-4790	165	14	.	.	PUNCT
cana-4790	166	1	3.2.5	3.2.5	NUM
cana-4790	166	2	model	model	NOUN
cana-4790	166	3	advantages	advantage	NOUN
cana-4790	166	4	•	•	ADP
cana-4790	166	5	spatial	spatial	ADJ
cana-4790	166	6	-	-	PUNCT
cana-4790	166	7	temporal	temporal	ADJ
cana-4790	166	8	fusion	fusion	NOUN
cana-4790	166	9	:	:	PUNCT
cana-4790	166	10	simultaneous	simultaneous	ADJ
cana-4790	166	11	learning	learning	NOUN
cana-4790	166	12	of	of	ADP
cana-4790	166	13	electrode	electrode	NOUN
cana-4790	166	14	relationships	relationship	NOUN
cana-4790	166	15	and	and	CCONJ
cana-4790	166	16	time	time	NOUN
cana-4790	166	17	-	-	PUNCT
cana-4790	166	18	based	base	VERB
cana-4790	166	19	changes	change	NOUN
cana-4790	166	20	.	.	PUNCT
cana-4790	167	1	•	•	NUM
cana-4790	167	2	minimal	minimal	ADJ
cana-4790	167	3	feature	feature	NOUN
cana-4790	167	4	engineering	engineering	NOUN
cana-4790	167	5	:	:	PUNCT
cana-4790	167	6	learns	learn	VERB
cana-4790	167	7	directly	directly	ADV
cana-4790	167	8	from	from	ADP
cana-4790	167	9	raw	raw	ADJ
cana-4790	167	10	eeg	eeg	NOUN
cana-4790	167	11	segments	segment	NOUN
cana-4790	167	12	.	.	PUNCT
cana-4790	168	1	•	•	NUM
cana-4790	168	2	robust	robust	ADJ
cana-4790	168	3	to	to	PART
cana-4790	168	4	noise	noise	VERB
cana-4790	168	5	:	:	PUNCT
cana-4790	168	6	cnn	cnn	PROPN
cana-4790	168	7	layers	layer	NOUN
cana-4790	168	8	help	help	AUX
cana-4790	168	9	suppress	suppress	VERB
cana-4790	168	10	local	local	ADJ
cana-4790	168	11	fluctuations	fluctuation	NOUN
cana-4790	168	12	,	,	PUNCT
cana-4790	168	13	and	and	CCONJ
cana-4790	168	14	lstm	lstm	NOUN
cana-4790	168	15	layers	layer	NOUN
cana-4790	168	16	capture	capture	VERB
cana-4790	168	17	meaningful	meaningful	ADJ
cana-4790	168	18	trends	trend	NOUN
cana-4790	168	19	.	.	PUNCT
cana-4790	169	1	4	4	X
cana-4790	169	2	.	.	NOUN
cana-4790	169	3	results	result	NOUN
cana-4790	169	4	and	and	CCONJ
cana-4790	169	5	discussion	discussion	NOUN
cana-4790	169	6	cnn	cnn	PROPN
cana-4790	169	7	-	-	PUNCT
cana-4790	169	8	lstm	lstm	PROPN
cana-4790	169	9	(	(	PUNCT
cana-4790	169	10	figure	figure	NOUN
cana-4790	169	11	2	2	NUM
cana-4790	169	12	)	)	PUNCT
cana-4790	169	13	achieves	achieve	VERB
cana-4790	169	14	the	the	DET
cana-4790	169	15	best	good	ADJ
cana-4790	169	16	results	result	NOUN
cana-4790	169	17	,	,	PUNCT
cana-4790	169	18	with	with	ADP
cana-4790	169	19	only	only	ADV
cana-4790	169	20	8	8	NUM
cana-4790	169	21	false	false	ADJ
cana-4790	169	22	negatives	negative	NOUN
cana-4790	169	23	and	and	CCONJ
cana-4790	169	24	13	13	NUM
cana-4790	169	25	false	false	ADJ
cana-4790	169	26	positives	positive	NOUN
cana-4790	169	27	.	.	PUNCT
cana-4790	170	1	this	this	PRON
cana-4790	170	2	confirms	confirm	VERB
cana-4790	170	3	that	that	SCONJ
cana-4790	170	4	integrating	integrate	VERB
cana-4790	170	5	spatial	spatial	ADJ
cana-4790	170	6	and	and	CCONJ
cana-4790	170	7	temporal	temporal	ADJ
cana-4790	170	8	feature	feature	NOUN
cana-4790	170	9	extraction	extraction	NOUN
cana-4790	170	10	yields	yield	VERB
cana-4790	170	11	the	the	DET
cana-4790	170	12	most	most	ADV
cana-4790	170	13	accurate	accurate	ADJ
cana-4790	170	14	and	and	CCONJ
cana-4790	170	15	balanced	balanced	ADJ
cana-4790	170	16	classification	classification	NOUN
cana-4790	170	17	,	,	PUNCT
cana-4790	170	18	making	make	VERB
cana-4790	170	19	it	it	PRON
cana-4790	170	20	highly	highly	ADV
cana-4790	170	21	suitable	suitable	ADJ
cana-4790	170	22	for	for	ADP
cana-4790	170	23	clinical	clinical	ADJ
cana-4790	170	24	eeg	eeg	NOUN
cana-4790	170	25	-	-	PUNCT
cana-4790	170	26	based	base	VERB
cana-4790	170	27	ad	ad	NOUN
cana-4790	170	28	screening	screening	NOUN
cana-4790	170	29	.	.	PUNCT
cana-4790	171	1	communications	communication	NOUN
cana-4790	171	2	on	on	ADP
cana-4790	171	3	applied	apply	VERB
cana-4790	171	4	nonlinear	nonlinear	ADJ
cana-4790	171	5	analysis	analysis	NOUN
cana-4790	171	6	issn	issn	NOUN
cana-4790	171	7	:	:	PUNCT
cana-4790	171	8	1074	1074	NUM
cana-4790	171	9	-	-	PUNCT
cana-4790	171	10	133x	133x	NUM
cana-4790	171	11	vol	vol	NOUN
cana-4790	171	12	31	31	NUM
cana-4790	171	13	no	no	NOUN
cana-4790	171	14	.	.	PUNCT
cana-4790	172	1	1s	1s	NUM
cana-4790	172	2	(	(	PUNCT
cana-4790	172	3	2024	2024	NUM
cana-4790	172	4	)	)	PUNCT
cana-4790	172	5	209	209	NUM
cana-4790	172	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	172	7	figure	figure	NOUN
cana-4790	172	8	2	2	NUM
cana-4790	172	9	:	:	PUNCT
cana-4790	172	10	confusion	confusion	NOUN
cana-4790	172	11	matrices	matrix	NOUN
cana-4790	172	12	of	of	ADP
cana-4790	172	13	eeg	eeg	NOUN
cana-4790	172	14	-	-	PUNCT
cana-4790	172	15	based	base	VERB
cana-4790	172	16	alzheimer	alzheimer	PROPN
cana-4790	172	17	’s	’s	PART
cana-4790	172	18	disease	disease	NOUN
cana-4790	172	19	detection	detection	NOUN
cana-4790	172	20	using	use	VERB
cana-4790	172	21	five	five	NUM
cana-4790	172	22	models	model	NOUN
cana-4790	172	23	—	—	PUNCT
cana-4790	172	24	svm	svm	ADJ
cana-4790	172	25	,	,	PUNCT
cana-4790	172	26	random	random	ADJ
cana-4790	172	27	forest	forest	NOUN
cana-4790	172	28	,	,	PUNCT
cana-4790	172	29	cnn	cnn	PROPN
cana-4790	172	30	,	,	PUNCT
cana-4790	172	31	lstm	lstm	PROPN
cana-4790	172	32	,	,	PUNCT
cana-4790	172	33	and	and	CCONJ
cana-4790	172	34	the	the	DET
cana-4790	172	35	proposed	propose	VERB
cana-4790	172	36	cnn	cnn	PROPN
cana-4790	172	37	-	-	PUNCT
cana-4790	172	38	lstm	lstm	PROPN
cana-4790	172	39	.	.	PUNCT
cana-4790	173	1	rows	row	NOUN
cana-4790	173	2	represent	represent	VERB
cana-4790	173	3	the	the	DET
cana-4790	173	4	actual	actual	ADJ
cana-4790	173	5	labels	label	NOUN
cana-4790	173	6	(	(	PUNCT
cana-4790	173	7	ad	ad	NOUN
cana-4790	173	8	,	,	PUNCT
cana-4790	173	9	control	control	NOUN
cana-4790	173	10	)	)	PUNCT
cana-4790	173	11	and	and	CCONJ
cana-4790	173	12	columns	column	NOUN
cana-4790	173	13	represent	represent	VERB
cana-4790	173	14	the	the	DET
cana-4790	173	15	predicted	predict	VERB
cana-4790	173	16	labels	label	NOUN
cana-4790	173	17	.	.	PUNCT
cana-4790	174	1	the	the	DET
cana-4790	174	2	confusion	confusion	NOUN
cana-4790	174	3	matrices	matrix	NOUN
cana-4790	174	4	(	(	PUNCT
cana-4790	174	5	figures	figure	NOUN
cana-4790	174	6	2	2	NUM
cana-4790	174	7	)	)	PUNCT
cana-4790	174	8	show	show	VERB
cana-4790	174	9	how	how	SCONJ
cana-4790	174	10	well	well	ADV
cana-4790	174	11	five	five	NUM
cana-4790	174	12	different	different	ADJ
cana-4790	174	13	models	model	NOUN
cana-4790	174	14	used	use	VERB
cana-4790	174	15	for	for	ADP
cana-4790	174	16	eeg	eeg	NOUN
cana-4790	174	17	-	-	PUNCT
cana-4790	174	18	based	base	VERB
cana-4790	174	19	alzheimer	alzheimer	NOUN
cana-4790	174	20	's	's	PART
cana-4790	174	21	disease	disease	NOUN
cana-4790	174	22	(	(	PUNCT
cana-4790	174	23	ad	ad	NOUN
cana-4790	174	24	)	)	PUNCT
cana-4790	174	25	diagnosis	diagnosis	NOUN
cana-4790	174	26	could	could	AUX
cana-4790	174	27	classify	classify	VERB
cana-4790	174	28	.	.	PUNCT
cana-4790	175	1	the	the	DET
cana-4790	175	2	number	number	NOUN
cana-4790	175	3	of	of	ADP
cana-4790	175	4	true	true	ADJ
cana-4790	175	5	positives	positive	NOUN
cana-4790	175	6	,	,	PUNCT
cana-4790	175	7	true	true	ADJ
cana-4790	175	8	negatives	negative	NOUN
cana-4790	175	9	,	,	PUNCT
cana-4790	175	10	false	false	ADJ
cana-4790	175	11	positives	positive	NOUN
cana-4790	175	12	,	,	PUNCT
cana-4790	175	13	and	and	CCONJ
cana-4790	175	14	false	false	ADJ
cana-4790	175	15	negatives	negative	NOUN
cana-4790	175	16	in	in	ADP
cana-4790	175	17	each	each	DET
cana-4790	175	18	grid	grid	NOUN
cana-4790	175	19	shows	show	VERB
cana-4790	175	20	how	how	SCONJ
cana-4790	175	21	well	well	ADV
cana-4790	175	22	the	the	DET
cana-4790	175	23	model	model	NOUN
cana-4790	175	24	can	can	AUX
cana-4790	175	25	tell	tell	VERB
cana-4790	175	26	the	the	DET
cana-4790	175	27	difference	difference	NOUN
cana-4790	175	28	between	between	ADP
cana-4790	175	29	ad	ad	NOUN
cana-4790	175	30	cases	case	NOUN
cana-4790	175	31	and	and	CCONJ
cana-4790	175	32	healthy	healthy	ADJ
cana-4790	175	33	controls	control	NOUN
cana-4790	175	34	.	.	PUNCT
cana-4790	176	1	this	this	DET
cana-4790	176	2	model	model	NOUN
cana-4790	176	3	,	,	PUNCT
cana-4790	176	4	communications	communication	NOUN
cana-4790	176	5	on	on	ADP
cana-4790	176	6	applied	apply	VERB
cana-4790	176	7	nonlinear	nonlinear	ADJ
cana-4790	176	8	analysis	analysis	NOUN
cana-4790	176	9	issn	issn	NOUN
cana-4790	176	10	:	:	PUNCT
cana-4790	176	11	1074	1074	NUM
cana-4790	176	12	-	-	PUNCT
cana-4790	176	13	133x	133x	NUM
cana-4790	176	14	vol	vol	NOUN
cana-4790	176	15	31	31	NUM
cana-4790	176	16	no	no	NOUN
cana-4790	176	17	.	.	PUNCT
cana-4790	177	1	1s	1s	NUM
cana-4790	177	2	(	(	PUNCT
cana-4790	177	3	2024	2024	NUM
cana-4790	177	4	)	)	PUNCT
cana-4790	177	5	210	210	NUM
cana-4790	177	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	178	1	the	the	DET
cana-4790	178	2	support	support	NOUN
cana-4790	178	3	vector	vector	NOUN
cana-4790	178	4	machine	machine	NOUN
cana-4790	178	5	(	(	PUNCT
cana-4790	178	6	svm	svm	PROPN
cana-4790	178	7	)	)	PUNCT
cana-4790	178	8	,	,	PUNCT
cana-4790	178	9	does	do	AUX
cana-4790	178	10	n't	not	PART
cana-4790	178	11	work	work	VERB
cana-4790	178	12	very	very	ADV
cana-4790	178	13	well	well	ADV
cana-4790	178	14	.	.	PUNCT
cana-4790	179	1	it	it	PRON
cana-4790	179	2	has	have	VERB
cana-4790	179	3	a	a	DET
cana-4790	179	4	lot	lot	NOUN
cana-4790	179	5	of	of	ADP
cana-4790	179	6	false	false	ADJ
cana-4790	179	7	positives	positive	NOUN
cana-4790	179	8	(	(	PUNCT
cana-4790	179	9	33	33	NUM
cana-4790	179	10	)	)	PUNCT
cana-4790	179	11	,	,	PUNCT
cana-4790	179	12	which	which	PRON
cana-4790	179	13	means	mean	VERB
cana-4790	179	14	it	it	PRON
cana-4790	179	15	's	be	AUX
cana-4790	179	16	likely	likely	ADJ
cana-4790	179	17	to	to	PART
cana-4790	179	18	miss	miss	VERB
cana-4790	179	19	real	real	ADJ
cana-4790	179	20	ad	ad	NOUN
cana-4790	179	21	cases	case	NOUN
cana-4790	179	22	.	.	PUNCT
cana-4790	180	1	the	the	DET
cana-4790	180	2	random	random	ADJ
cana-4790	180	3	forest	forest	NOUN
cana-4790	180	4	model	model	NOUN
cana-4790	180	5	does	do	VERB
cana-4790	180	6	a	a	DET
cana-4790	180	7	little	little	ADJ
cana-4790	180	8	better	well	ADJ
cana-4790	180	9	,	,	PUNCT
cana-4790	180	10	but	but	CCONJ
cana-4790	180	11	it	it	PRON
cana-4790	180	12	still	still	ADV
cana-4790	180	13	makes	make	VERB
cana-4790	180	14	a	a	DET
cana-4790	180	15	lot	lot	NOUN
cana-4790	180	16	of	of	ADP
cana-4790	180	17	mistakes	mistake	NOUN
cana-4790	180	18	.	.	PUNCT
cana-4790	181	1	the	the	DET
cana-4790	181	2	cnn	cnn	PROPN
cana-4790	181	3	model	model	NOUN
cana-4790	181	4	,	,	PUNCT
cana-4790	181	5	on	on	ADP
cana-4790	181	6	the	the	DET
cana-4790	181	7	other	other	ADJ
cana-4790	181	8	hand	hand	NOUN
cana-4790	181	9	,	,	PUNCT
cana-4790	181	10	learns	learn	VERB
cana-4790	181	11	how	how	SCONJ
cana-4790	181	12	to	to	PART
cana-4790	181	13	connect	connect	VERB
cana-4790	181	14	eeg	eeg	NOUN
cana-4790	181	15	bands	band	NOUN
cana-4790	181	16	spatially	spatially	ADV
cana-4790	181	17	,	,	PUNCT
cana-4790	181	18	which	which	PRON
cana-4790	181	19	greatly	greatly	ADV
cana-4790	181	20	cuts	cut	VERB
cana-4790	181	21	down	down	ADP
cana-4790	181	22	on	on	ADP
cana-4790	181	23	both	both	DET
cana-4790	181	24	false	false	ADJ
cana-4790	181	25	positives	positive	NOUN
cana-4790	181	26	and	and	CCONJ
cana-4790	181	27	false	false	ADJ
cana-4790	181	28	negatives	negative	NOUN
cana-4790	181	29	.	.	PUNCT
cana-4790	182	1	the	the	DET
cana-4790	182	2	lstm	lstm	PROPN
cana-4790	182	3	model	model	NOUN
cana-4790	182	4	also	also	ADV
cana-4790	182	5	does	do	VERB
cana-4790	182	6	a	a	DET
cana-4790	182	7	good	good	ADJ
cana-4790	182	8	job	job	NOUN
cana-4790	182	9	of	of	ADP
cana-4790	182	10	detecting	detect	VERB
cana-4790	182	11	time	time	NOUN
cana-4790	182	12	trends	trend	NOUN
cana-4790	182	13	in	in	ADP
cana-4790	182	14	the	the	DET
cana-4790	182	15	eeg	eeg	NOUN
cana-4790	182	16	sequences	sequence	NOUN
cana-4790	182	17	,	,	PUNCT
cana-4790	182	18	but	but	CCONJ
cana-4790	182	19	it	it	PRON
cana-4790	182	20	is	be	AUX
cana-4790	182	21	not	not	PART
cana-4790	182	22	quite	quite	ADV
cana-4790	182	23	as	as	ADV
cana-4790	182	24	good	good	ADJ
cana-4790	182	25	as	as	ADP
cana-4790	182	26	cnn	cnn	PROPN
cana-4790	182	27	at	at	ADP
cana-4790	182	28	understanding	understanding	NOUN
cana-4790	182	29	where	where	SCONJ
cana-4790	182	30	things	thing	NOUN
cana-4790	182	31	are	be	AUX
cana-4790	182	32	in	in	ADP
cana-4790	182	33	space	space	NOUN
cana-4790	182	34	.	.	PUNCT
cana-4790	183	1	the	the	DET
cana-4790	183	2	suggested	suggest	VERB
cana-4790	183	3	cnnlstm	cnnlstm	PROPN
cana-4790	183	4	hybrid	hybrid	NOUN
cana-4790	183	5	model	model	NOUN
cana-4790	183	6	works	work	VERB
cana-4790	183	7	the	the	DET
cana-4790	183	8	best	good	ADJ
cana-4790	183	9	,	,	PUNCT
cana-4790	183	10	with	with	ADP
cana-4790	183	11	only	only	ADV
cana-4790	183	12	8	8	NUM
cana-4790	183	13	false	false	ADJ
cana-4790	183	14	negatives	negative	NOUN
cana-4790	183	15	and	and	CCONJ
cana-4790	183	16	13	13	NUM
cana-4790	183	17	false	false	ADJ
cana-4790	183	18	positives	positive	NOUN
cana-4790	183	19	,	,	PUNCT
cana-4790	183	20	showing	show	VERB
cana-4790	183	21	that	that	SCONJ
cana-4790	183	22	it	it	PRON
cana-4790	183	23	is	be	AUX
cana-4790	183	24	more	more	ADV
cana-4790	183	25	sensitive	sensitive	ADJ
cana-4790	183	26	and	and	CCONJ
cana-4790	183	27	accurate	accurate	ADJ
cana-4790	183	28	.	.	PUNCT
cana-4790	184	1	this	this	DET
cana-4790	184	2	result	result	NOUN
cana-4790	184	3	shows	show	VERB
cana-4790	184	4	how	how	SCONJ
cana-4790	184	5	useful	useful	ADJ
cana-4790	184	6	it	it	PRON
cana-4790	184	7	is	be	AUX
cana-4790	184	8	to	to	PART
cana-4790	184	9	use	use	VERB
cana-4790	184	10	both	both	CCONJ
cana-4790	184	11	convolutional	convolutional	ADJ
cana-4790	184	12	and	and	CCONJ
cana-4790	184	13	recurrent	recurrent	ADJ
cana-4790	184	14	layers	layer	NOUN
cana-4790	184	15	together	together	ADV
cana-4790	184	16	when	when	SCONJ
cana-4790	184	17	trying	try	VERB
cana-4790	184	18	to	to	PART
cana-4790	184	19	get	get	VERB
cana-4790	184	20	spatial	spatial	ADJ
cana-4790	184	21	features	feature	NOUN
cana-4790	184	22	from	from	ADP
cana-4790	184	23	eeg	eeg	PROPN
cana-4790	184	24	data	datum	NOUN
cana-4790	184	25	.	.	PUNCT
cana-4790	185	1	overall	overall	ADV
cana-4790	185	2	,	,	PUNCT
cana-4790	185	3	the	the	DET
cana-4790	185	4	confusion	confusion	NOUN
cana-4790	185	5	matrices	matrix	NOUN
cana-4790	185	6	show	show	VERB
cana-4790	185	7	that	that	SCONJ
cana-4790	185	8	the	the	DET
cana-4790	185	9	cnn	cnn	PROPN
cana-4790	185	10	-	-	PUNCT
cana-4790	185	11	lstm	lstm	PROPN
cana-4790	185	12	model	model	NOUN
cana-4790	185	13	is	be	AUX
cana-4790	185	14	the	the	DET
cana-4790	185	15	best	good	ADJ
cana-4790	185	16	at	at	ADP
cana-4790	185	17	accurate	accurate	ADJ
cana-4790	185	18	and	and	CCONJ
cana-4790	185	19	fair	fair	ADJ
cana-4790	185	20	classification	classification	NOUN
cana-4790	185	21	.	.	PUNCT
cana-4790	186	1	this	this	PRON
cana-4790	186	2	makes	make	VERB
cana-4790	186	3	it	it	PRON
cana-4790	186	4	a	a	DET
cana-4790	186	5	potentially	potentially	ADV
cana-4790	186	6	useful	useful	ADJ
cana-4790	186	7	tool	tool	NOUN
cana-4790	186	8	for	for	ADP
cana-4790	186	9	finding	find	VERB
cana-4790	186	10	alzheimer	alzheimer	NOUN
cana-4790	186	11	's	's	PART
cana-4790	186	12	disease	disease	NOUN
cana-4790	186	13	early	early	ADV
cana-4790	186	14	and	and	CCONJ
cana-4790	186	15	reliably	reliably	ADV
cana-4790	186	16	.	.	PUNCT
cana-4790	187	1	table	table	NOUN
cana-4790	187	2	3	3	NUM
cana-4790	187	3	.	.	PUNCT
cana-4790	188	1	cnn	cnn	PROPN
cana-4790	188	2	-	-	PUNCT
cana-4790	188	3	lstm	lstm	PROPN
cana-4790	188	4	model	model	NOUN
cana-4790	188	5	comparison	comparison	NOUN
cana-4790	188	6	model	model	NOUN
cana-4790	188	7	accuracy	accuracy	NOUN
cana-4790	188	8	(	(	PUNCT
cana-4790	188	9	%	%	INTJ
cana-4790	188	10	)	)	PUNCT
cana-4790	188	11	precision	precision	NOUN
cana-4790	188	12	(	(	PUNCT
cana-4790	188	13	%	%	INTJ
cana-4790	188	14	)	)	PUNCT
cana-4790	188	15	recall	recall	NOUN
cana-4790	188	16	(	(	PUNCT
cana-4790	188	17	%	%	NOUN
cana-4790	188	18	)	)	PUNCT
cana-4790	188	19	f1	f1	NOUN
cana-4790	188	20	score	score	NOUN
cana-4790	188	21	(	(	PUNCT
cana-4790	188	22	%	%	INTJ
cana-4790	188	23	)	)	PUNCT
cana-4790	188	24	svm	svm	PROPN
cana-4790	188	25	(	(	PUNCT
cana-4790	188	26	with	with	ADP
cana-4790	188	27	psd	psd	NOUN
cana-4790	188	28	)	)	PUNCT
cana-4790	188	29	76.4	76.4	NUM
cana-4790	188	30	73.9	73.9	NUM
cana-4790	188	31	77.8	77.8	NUM
cana-4790	188	32	75.1	75.1	NUM
cana-4790	188	33	random	random	ADJ
cana-4790	188	34	forest	forest	NOUN
cana-4790	188	35	79.8	79.8	NUM
cana-4790	188	36	76.1	76.1	NUM
cana-4790	188	37	80.9	80.9	NUM
cana-4790	188	38	77.3	77.3	NUM
cana-4790	188	39	cnn	cnn	NOUN
cana-4790	188	40	only	only	ADV
cana-4790	188	41	88.5	88.5	NUM
cana-4790	188	42	87.1	87.1	NUM
cana-4790	188	43	89.4	89.4	NUM
cana-4790	188	44	87.9	87.9	NUM
cana-4790	188	45	lstm	lstm	NOUN
cana-4790	188	46	only	only	ADV
cana-4790	188	47	86.3	86.3	NUM
cana-4790	188	48	84.7	84.7	NUM
cana-4790	188	49	86.8	86.8	NUM
cana-4790	188	50	85.4	85.4	NUM
cana-4790	188	51	cnn	cnn	PROPN
cana-4790	188	52	-	-	PUNCT
cana-4790	188	53	lstm	lstm	PROPN
cana-4790	188	54	(	(	PUNCT
cana-4790	188	55	proposed	propose	VERB
cana-4790	188	56	)	)	PUNCT
cana-4790	188	57	93.2	93.2	NUM
cana-4790	188	58	91.5	91.5	NUM
cana-4790	188	59	94.8	94.8	NUM
cana-4790	188	60	93.1	93.1	NUM
cana-4790	188	61	in	in	ADP
cana-4790	188	62	table	table	NOUN
cana-4790	188	63	3	3	NUM
cana-4790	188	64	that	that	PRON
cana-4790	188	65	compares	compare	VERB
cana-4790	188	66	the	the	DET
cana-4790	188	67	suggested	suggested	ADJ
cana-4790	188	68	cnn	cnn	PROPN
cana-4790	188	69	-	-	PUNCT
cana-4790	188	70	lstm	lstm	PROPN
cana-4790	188	71	model	model	NOUN
cana-4790	188	72	to	to	ADP
cana-4790	188	73	a	a	DET
cana-4790	188	74	number	number	NOUN
cana-4790	188	75	of	of	ADP
cana-4790	188	76	standard	standard	ADJ
cana-4790	188	77	models	model	NOUN
cana-4790	188	78	using	use	VERB
cana-4790	188	79	four	four	NUM
cana-4790	188	80	important	important	ADJ
cana-4790	188	81	performance	performance	NOUN
cana-4790	188	82	metrics	metric	NOUN
cana-4790	188	83	:	:	PUNCT
cana-4790	188	84	f1	f1	NOUN
cana-4790	188	85	-	-	PUNCT
cana-4790	188	86	score	score	NOUN
cana-4790	188	87	,	,	PUNCT
cana-4790	188	88	accuracy	accuracy	NOUN
cana-4790	188	89	,	,	PUNCT
cana-4790	188	90	precision	precision	NOUN
cana-4790	188	91	,	,	PUNCT
cana-4790	188	92	and	and	CCONJ
cana-4790	188	93	recall	recall	NOUN
cana-4790	188	94	.	.	PUNCT
cana-4790	189	1	machine	machine	NOUN
cana-4790	189	2	learning	learn	VERB
cana-4790	189	3	algorithms	algorithm	NOUN
cana-4790	189	4	like	like	ADP
cana-4790	189	5	support	support	NOUN
cana-4790	189	6	vector	vector	NOUN
cana-4790	189	7	machines	machine	NOUN
cana-4790	189	8	(	(	PUNCT
cana-4790	189	9	svm	svm	PROPN
cana-4790	189	10	)	)	PUNCT
cana-4790	189	11	and	and	CCONJ
cana-4790	189	12	random	random	ADJ
cana-4790	189	13	forests	forest	NOUN
cana-4790	189	14	(	(	PUNCT
cana-4790	189	15	rf	rf	NOUN
cana-4790	189	16	)	)	PUNCT
cana-4790	189	17	were	be	AUX
cana-4790	189	18	used	use	VERB
cana-4790	189	19	with	with	ADP
cana-4790	189	20	power	power	NOUN
cana-4790	189	21	spectrum	spectrum	NOUN
cana-4790	189	22	features	feature	NOUN
cana-4790	189	23	that	that	PRON
cana-4790	189	24	were	be	AUX
cana-4790	189	25	designed	design	VERB
cana-4790	189	26	by	by	ADP
cana-4790	189	27	hand	hand	NOUN
cana-4790	189	28	.	.	PUNCT
cana-4790	190	1	with	with	ADP
cana-4790	190	2	an	an	DET
cana-4790	190	3	svm	svm	ADJ
cana-4790	190	4	accuracy	accuracy	NOUN
cana-4790	190	5	of	of	ADP
cana-4790	190	6	76.4	76.4	NUM
cana-4790	190	7	%	%	NOUN
cana-4790	190	8	and	and	CCONJ
cana-4790	190	9	an	an	DET
cana-4790	190	10	f1	f1	NOUN
cana-4790	190	11	-	-	PUNCT
cana-4790	190	12	score	score	NOUN
cana-4790	190	13	of	of	ADP
cana-4790	190	14	75.1	75.1	NUM
cana-4790	190	15	%	%	NOUN
cana-4790	190	16	,	,	PUNCT
cana-4790	190	17	these	these	DET
cana-4790	190	18	models	model	NOUN
cana-4790	190	19	did	do	AUX
cana-4790	190	20	not	not	PART
cana-4790	190	21	do	do	VERB
cana-4790	190	22	very	very	ADV
cana-4790	190	23	well	well	ADV
cana-4790	190	24	.	.	PUNCT
cana-4790	191	1	this	this	PRON
cana-4790	191	2	means	mean	VERB
cana-4790	191	3	they	they	PRON
cana-4790	191	4	were	be	AUX
cana-4790	191	5	not	not	PART
cana-4790	191	6	very	very	ADV
cana-4790	191	7	good	good	ADJ
cana-4790	191	8	at	at	ADP
cana-4790	191	9	catching	catch	VERB
cana-4790	191	10	the	the	DET
cana-4790	191	11	complex	complex	ADJ
cana-4790	191	12	spatiotemporal	spatiotemporal	ADJ
cana-4790	191	13	patterns	pattern	NOUN
cana-4790	191	14	of	of	ADP
cana-4790	191	15	eeg	eeg	PROPN
cana-4790	191	16	data	datum	NOUN
cana-4790	191	17	.	.	PUNCT
cana-4790	192	1	while	while	SCONJ
cana-4790	192	2	cnn	cnn	PROPN
cana-4790	192	3	-	-	PUNCT
cana-4790	192	4	only	only	ADV
cana-4790	192	5	and	and	CCONJ
cana-4790	192	6	lstmonly	lstmonly	ADJ
cana-4790	192	7	models	model	NOUN
cana-4790	192	8	did	do	AUX
cana-4790	192	9	not	not	PART
cana-4790	192	10	work	work	VERB
cana-4790	192	11	as	as	ADV
cana-4790	192	12	well	well	ADV
cana-4790	192	13	,	,	PUNCT
cana-4790	192	14	standalone	standalone	ADJ
cana-4790	192	15	deep	deep	ADJ
cana-4790	192	16	learning	learning	NOUN
cana-4790	192	17	models	model	NOUN
cana-4790	192	18	did	do	VERB
cana-4790	192	19	because	because	SCONJ
cana-4790	192	20	they	they	PRON
cana-4790	192	21	could	could	AUX
cana-4790	192	22	learn	learn	VERB
cana-4790	192	23	straight	straight	ADV
cana-4790	192	24	from	from	ADP
cana-4790	192	25	raw	raw	ADJ
cana-4790	192	26	eeg	eeg	PROPN
cana-4790	192	27	data	datum	NOUN
cana-4790	192	28	.	.	PUNCT
cana-4790	193	1	cnn	cnn	PROPN
cana-4790	193	2	got	get	VERB
cana-4790	193	3	88.5	88.5	NUM
cana-4790	193	4	%	%	NOUN
cana-4790	193	5	accuracy	accuracy	NOUN
cana-4790	193	6	and	and	CCONJ
cana-4790	193	7	87.9	87.9	NUM
cana-4790	193	8	%	%	NOUN
cana-4790	193	9	f1	f1	NOUN
cana-4790	193	10	-	-	PUNCT
cana-4790	193	11	score	score	NOUN
cana-4790	193	12	for	for	ADP
cana-4790	193	13	its	its	PRON
cana-4790	193	14	focus	focus	NOUN
cana-4790	193	15	on	on	ADP
cana-4790	193	16	spatial	spatial	ADJ
cana-4790	193	17	features	feature	NOUN
cana-4790	193	18	,	,	PUNCT
cana-4790	193	19	while	while	SCONJ
cana-4790	193	20	lstm	lstm	NOUN
cana-4790	193	21	got	get	VERB
cana-4790	193	22	86.3	86.3	NUM
cana-4790	193	23	%	%	NOUN
cana-4790	193	24	accuracy	accuracy	NOUN
cana-4790	193	25	and	and	CCONJ
cana-4790	193	26	85.4	85.4	NUM
cana-4790	193	27	%	%	NOUN
cana-4790	193	28	f1	f1	NOUN
cana-4790	193	29	-	-	PUNCT
cana-4790	193	30	score	score	NOUN
cana-4790	193	31	for	for	ADP
cana-4790	193	32	its	its	PRON
cana-4790	193	33	focus	focus	NOUN
cana-4790	193	34	on	on	ADP
cana-4790	193	35	time	time	NOUN
cana-4790	193	36	relationships	relationship	NOUN
cana-4790	193	37	.	.	PUNCT
cana-4790	194	1	the	the	DET
cana-4790	194	2	suggested	suggest	VERB
cana-4790	194	3	cnn	cnn	PROPN
cana-4790	194	4	-	-	PUNCT
cana-4790	194	5	lstm	lstm	ADJ
cana-4790	194	6	hybrid	hybrid	NOUN
cana-4790	194	7	model	model	NOUN
cana-4790	194	8	did	do	VERB
cana-4790	194	9	better	well	ADV
cana-4790	194	10	than	than	ADP
cana-4790	194	11	all	all	PRON
cana-4790	194	12	of	of	ADP
cana-4790	194	13	them	they	PRON
cana-4790	194	14	,	,	PUNCT
cana-4790	194	15	getting	get	VERB
cana-4790	194	16	the	the	DET
cana-4790	194	17	best	good	ADJ
cana-4790	194	18	f1	f1	NOUN
cana-4790	194	19	-	-	PUNCT
cana-4790	194	20	score	score	NOUN
cana-4790	194	21	(	(	PUNCT
cana-4790	194	22	93.1	93.1	NUM
cana-4790	194	23	%	%	NOUN
cana-4790	194	24	)	)	PUNCT
cana-4790	194	25	,	,	PUNCT
cana-4790	194	26	accuracy	accuracy	NOUN
cana-4790	194	27	(	(	PUNCT
cana-4790	194	28	93.2	93.2	NUM
cana-4790	194	29	%	%	NOUN
cana-4790	194	30	)	)	PUNCT
cana-4790	194	31	,	,	PUNCT
cana-4790	194	32	precision	precision	NOUN
cana-4790	194	33	(	(	PUNCT
cana-4790	194	34	91.5	91.5	NUM
cana-4790	194	35	%	%	NOUN
cana-4790	194	36	)	)	PUNCT
cana-4790	194	37	,	,	PUNCT
cana-4790	194	38	and	and	CCONJ
cana-4790	194	39	recall	recall	NOUN
cana-4790	194	40	(	(	PUNCT
cana-4790	194	41	94.8	94.8	NUM
cana-4790	194	42	%	%	NOUN
cana-4790	194	43	)	)	PUNCT
cana-4790	194	44	.	.	PUNCT
cana-4790	195	1	this	this	PRON
cana-4790	195	2	clearly	clearly	ADV
cana-4790	195	3	shows	show	VERB
cana-4790	195	4	the	the	DET
cana-4790	195	5	benefit	benefit	NOUN
cana-4790	195	6	of	of	ADP
cana-4790	195	7	using	use	VERB
cana-4790	195	8	both	both	CCONJ
cana-4790	195	9	spatial	spatial	ADJ
cana-4790	195	10	and	and	CCONJ
cana-4790	195	11	temporal	temporal	ADJ
cana-4790	195	12	learning	learning	NOUN
cana-4790	195	13	together	together	ADV
cana-4790	195	14	in	in	ADP
cana-4790	195	15	eeg	eeg	NOUN
cana-4790	195	16	-	-	PUNCT
cana-4790	195	17	based	base	VERB
cana-4790	195	18	alzheimer	alzheimer	NOUN
cana-4790	195	19	's	's	PART
cana-4790	195	20	diagnosis	diagnosis	NOUN
cana-4790	195	21	.	.	PUNCT
cana-4790	196	1	the	the	DET
cana-4790	196	2	high	high	ADJ
cana-4790	196	3	memory	memory	NOUN
cana-4790	196	4	value	value	NOUN
cana-4790	196	5	is	be	AUX
cana-4790	196	6	especially	especially	ADV
cana-4790	196	7	important	important	ADJ
cana-4790	196	8	in	in	ADP
cana-4790	196	9	medical	medical	ADJ
cana-4790	196	10	diagnosis	diagnosis	NOUN
cana-4790	196	11	,	,	PUNCT
cana-4790	196	12	where	where	SCONJ
cana-4790	196	13	finding	find	VERB
cana-4790	196	14	true	true	ADJ
cana-4790	196	15	positive	positive	ADJ
cana-4790	196	16	cases	case	NOUN
cana-4790	196	17	(	(	PUNCT
cana-4790	196	18	ad	ad	NOUN
cana-4790	196	19	patients	patient	NOUN
cana-4790	196	20	)	)	PUNCT
cana-4790	196	21	quickly	quickly	ADV
cana-4790	196	22	is	be	AUX
cana-4790	196	23	important	important	ADJ
cana-4790	196	24	for	for	ADP
cana-4790	196	25	treatment	treatment	NOUN
cana-4790	196	26	and	and	CCONJ
cana-4790	196	27	action	action	NOUN
cana-4790	196	28	.	.	PUNCT
cana-4790	197	1	communications	communication	NOUN
cana-4790	197	2	on	on	ADP
cana-4790	197	3	applied	apply	VERB
cana-4790	197	4	nonlinear	nonlinear	ADJ
cana-4790	197	5	analysis	analysis	NOUN
cana-4790	197	6	issn	issn	NOUN
cana-4790	197	7	:	:	PUNCT
cana-4790	197	8	1074	1074	NUM
cana-4790	197	9	-	-	PUNCT
cana-4790	197	10	133x	133x	NUM
cana-4790	197	11	vol	vol	NOUN
cana-4790	197	12	31	31	NUM
cana-4790	197	13	no	no	NOUN
cana-4790	197	14	.	.	PUNCT
cana-4790	198	1	1s	1s	NUM
cana-4790	198	2	(	(	PUNCT
cana-4790	198	3	2024	2024	NUM
cana-4790	198	4	)	)	PUNCT
cana-4790	198	5	211	211	NUM
cana-4790	198	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	198	7	figure	figure	NOUN
cana-4790	198	8	3	3	NUM
cana-4790	198	9	:	:	PUNCT
cana-4790	198	10	accuracy	accuracy	NOUN
cana-4790	198	11	comparison	comparison	NOUN
cana-4790	198	12	the	the	DET
cana-4790	198	13	cnn	cnn	PROPN
cana-4790	198	14	-	-	PUNCT
cana-4790	198	15	lstm	lstm	ADJ
cana-4790	198	16	model	model	NOUN
cana-4790	198	17	got	get	VERB
cana-4790	198	18	the	the	DET
cana-4790	198	19	best	good	ADJ
cana-4790	198	20	results	result	NOUN
cana-4790	198	21	(	(	PUNCT
cana-4790	198	22	93.2	93.2	NUM
cana-4790	198	23	%	%	NOUN
cana-4790	198	24	)	)	PUNCT
cana-4790	198	25	,	,	PUNCT
cana-4790	198	26	which	which	PRON
cana-4790	198	27	is	be	AUX
cana-4790	198	28	a	a	DET
cana-4790	198	29	big	big	ADJ
cana-4790	198	30	improvement	improvement	NOUN
cana-4790	198	31	over	over	ADP
cana-4790	198	32	other	other	ADJ
cana-4790	198	33	models	model	NOUN
cana-4790	198	34	like	like	ADP
cana-4790	198	35	svm	svm	ADJ
cana-4790	198	36	and	and	CCONJ
cana-4790	198	37	random	random	ADJ
cana-4790	198	38	forest	forest	NOUN
cana-4790	198	39	,	,	PUNCT
cana-4790	198	40	as	as	SCONJ
cana-4790	198	41	shown	show	VERB
cana-4790	198	42	in	in	ADP
cana-4790	198	43	this	this	DET
cana-4790	198	44	figure	figure	NOUN
cana-4790	198	45	3	3	X
cana-4790	198	46	.	.	PUNCT
cana-4790	199	1	this	this	PRON
cana-4790	199	2	shows	show	VERB
cana-4790	199	3	that	that	SCONJ
cana-4790	199	4	the	the	DET
cana-4790	199	5	mix	mix	NOUN
cana-4790	199	6	model	model	NOUN
cana-4790	199	7	can	can	AUX
cana-4790	199	8	correctly	correctly	ADV
cana-4790	199	9	group	group	VERB
cana-4790	199	10	both	both	CCONJ
cana-4790	199	11	alzheimer	alzheimer	PROPN
cana-4790	199	12	's	's	PART
cana-4790	199	13	and	and	CCONJ
cana-4790	199	14	control	control	NOUN
cana-4790	199	15	eeg	eeg	NOUN
cana-4790	199	16	parts	part	NOUN
cana-4790	199	17	more	more	ADV
cana-4790	199	18	often	often	ADV
cana-4790	199	19	than	than	ADP
cana-4790	199	20	other	other	ADJ
cana-4790	199	21	methods	method	NOUN
cana-4790	199	22	.	.	PUNCT
cana-4790	200	1	figure	figure	VERB
cana-4790	200	2	4	4	NUM
cana-4790	200	3	:	:	PUNCT
cana-4790	200	4	precision	precision	NOUN
cana-4790	200	5	comparison	comparison	NOUN
cana-4790	200	6	precision	precision	NOUN
cana-4790	200	7	shows	show	NOUN
cana-4790	200	8	in	in	ADP
cana-4790	200	9	figure	figure	NOUN
cana-4790	200	10	4	4	NUM
cana-4790	200	11	how	how	SCONJ
cana-4790	200	12	well	well	ADV
cana-4790	200	13	the	the	DET
cana-4790	200	14	model	model	NOUN
cana-4790	200	15	can	can	AUX
cana-4790	200	16	avoid	avoid	VERB
cana-4790	200	17	wrong	wrong	ADJ
cana-4790	200	18	results	result	NOUN
cana-4790	200	19	.	.	PUNCT
cana-4790	201	1	the	the	DET
cana-4790	201	2	cnn	cnn	PROPN
cana-4790	201	3	-	-	PUNCT
cana-4790	201	4	lstm	lstm	PROPN
cana-4790	201	5	model	model	NOUN
cana-4790	201	6	had	have	VERB
cana-4790	201	7	the	the	DET
cana-4790	201	8	best	good	ADJ
cana-4790	201	9	accuracy	accuracy	NOUN
cana-4790	201	10	(	(	PUNCT
cana-4790	201	11	91.5	91.5	NUM
cana-4790	201	12	%	%	NOUN
cana-4790	201	13	)	)	PUNCT
cana-4790	201	14	,	,	PUNCT
cana-4790	201	15	which	which	PRON
cana-4790	201	16	means	mean	VERB
cana-4790	201	17	it	it	PRON
cana-4790	201	18	almost	almost	ADV
cana-4790	201	19	never	never	ADV
cana-4790	201	20	wrongly	wrongly	ADV
cana-4790	201	21	labels	label	VERB
cana-4790	201	22	healthy	healthy	ADJ
cana-4790	201	23	people	people	NOUN
cana-4790	201	24	as	as	ADP
cana-4790	201	25	alzheimer	alzheimer	NOUN
cana-4790	201	26	's	's	PART
cana-4790	201	27	patients	patient	NOUN
cana-4790	201	28	.	.	PUNCT
cana-4790	202	1	this	this	PRON
cana-4790	202	2	is	be	AUX
cana-4790	202	3	an	an	DET
cana-4790	202	4	important	important	ADJ
cana-4790	202	5	part	part	NOUN
cana-4790	202	6	of	of	ADP
cana-4790	202	7	clinical	clinical	ADJ
cana-4790	202	8	screening	screening	NOUN
cana-4790	202	9	to	to	PART
cana-4790	202	10	avoid	avoid	VERB
cana-4790	202	11	overdiagnosis	overdiagnosis	NOUN
cana-4790	202	12	.	.	PUNCT
cana-4790	203	1	communications	communication	NOUN
cana-4790	203	2	on	on	ADP
cana-4790	203	3	applied	apply	VERB
cana-4790	203	4	nonlinear	nonlinear	ADJ
cana-4790	203	5	analysis	analysis	NOUN
cana-4790	203	6	issn	issn	NOUN
cana-4790	203	7	:	:	PUNCT
cana-4790	203	8	1074	1074	NUM
cana-4790	203	9	-	-	PUNCT
cana-4790	203	10	133x	133x	NUM
cana-4790	203	11	vol	vol	NOUN
cana-4790	203	12	31	31	NUM
cana-4790	203	13	no	no	NOUN
cana-4790	203	14	.	.	PUNCT
cana-4790	204	1	1s	1s	NUM
cana-4790	204	2	(	(	PUNCT
cana-4790	204	3	2024	2024	NUM
cana-4790	204	4	)	)	PUNCT
cana-4790	204	5	212	212	NUM
cana-4790	204	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	204	7	figure	figure	NOUN
cana-4790	204	8	5	5	NUM
cana-4790	204	9	:	:	PUNCT
cana-4790	204	10	recall	recall	VERB
cana-4790	204	11	comparison	comparison	NOUN
cana-4790	204	12	the	the	DET
cana-4790	204	13	memory	memory	NOUN
cana-4790	204	14	measure	measure	NOUN
cana-4790	204	15	is	be	AUX
cana-4790	204	16	very	very	ADV
cana-4790	204	17	important	important	ADJ
cana-4790	204	18	in	in	ADP
cana-4790	204	19	medical	medical	ADJ
cana-4790	204	20	diagnosis	diagnosis	NOUN
cana-4790	204	21	because	because	SCONJ
cana-4790	204	22	it	it	PRON
cana-4790	204	23	shows	show	VERB
cana-4790	204	24	how	how	SCONJ
cana-4790	204	25	well	well	ADV
cana-4790	204	26	the	the	DET
cana-4790	204	27	system	system	NOUN
cana-4790	204	28	can	can	AUX
cana-4790	204	29	find	find	VERB
cana-4790	204	30	real	real	ADJ
cana-4790	204	31	cases	case	NOUN
cana-4790	204	32	of	of	ADP
cana-4790	204	33	alzheimer	alzheimer	PROPN
cana-4790	204	34	's	's	PART
cana-4790	204	35	shown	show	VERB
cana-4790	204	36	in	in	ADP
cana-4790	204	37	figure	figure	NOUN
cana-4790	204	38	5	5	NUM
cana-4790	204	39	.	.	PUNCT
cana-4790	205	1	the	the	DET
cana-4790	205	2	cnn	cnn	PROPN
cana-4790	205	3	-	-	PUNCT
cana-4790	205	4	lstm	lstm	PROPN
cana-4790	205	5	model	model	NOUN
cana-4790	205	6	had	have	VERB
cana-4790	205	7	a	a	DET
cana-4790	205	8	recall	recall	NOUN
cana-4790	205	9	rate	rate	NOUN
cana-4790	205	10	of	of	ADP
cana-4790	205	11	94.8	94.8	NUM
cana-4790	205	12	%	%	NOUN
cana-4790	205	13	,	,	PUNCT
cana-4790	205	14	which	which	PRON
cana-4790	205	15	was	be	AUX
cana-4790	205	16	much	much	ADV
cana-4790	205	17	higher	high	ADJ
cana-4790	205	18	than	than	ADP
cana-4790	205	19	any	any	DET
cana-4790	205	20	other	other	ADJ
cana-4790	205	21	model	model	NOUN
cana-4790	205	22	and	and	CCONJ
cana-4790	205	23	shows	show	VERB
cana-4790	205	24	how	how	SCONJ
cana-4790	205	25	sensitive	sensitive	ADJ
cana-4790	205	26	and	and	CCONJ
cana-4790	205	27	reliable	reliable	ADJ
cana-4790	205	28	it	it	PRON
cana-4790	205	29	is	be	AUX
cana-4790	205	30	at	at	ADP
cana-4790	205	31	finding	find	VERB
cana-4790	205	32	ad	ad	NOUN
cana-4790	205	33	-	-	PUNCT
cana-4790	205	34	positive	positive	ADJ
cana-4790	205	35	people	people	NOUN
cana-4790	205	36	.	.	PUNCT
cana-4790	206	1	figure	figure	VERB
cana-4790	206	2	6	6	NUM
cana-4790	206	3	:	:	PUNCT
cana-4790	206	4	f1	f1	PROPN
cana-4790	206	5	score	score	NOUN
cana-4790	206	6	comparison	comparison	NOUN
cana-4790	206	7	communications	communication	NOUN
cana-4790	206	8	on	on	ADP
cana-4790	206	9	applied	apply	VERB
cana-4790	206	10	nonlinear	nonlinear	ADJ
cana-4790	206	11	analysis	analysis	NOUN
cana-4790	206	12	issn	issn	NOUN
cana-4790	206	13	:	:	PUNCT
cana-4790	206	14	1074	1074	NUM
cana-4790	206	15	-	-	PUNCT
cana-4790	206	16	133x	133x	NUM
cana-4790	206	17	vol	vol	NOUN
cana-4790	206	18	31	31	NUM
cana-4790	206	19	no	no	NOUN
cana-4790	206	20	.	.	PUNCT
cana-4790	207	1	1s	1s	NUM
cana-4790	207	2	(	(	PUNCT
cana-4790	207	3	2024	2024	NUM
cana-4790	207	4	)	)	PUNCT
cana-4790	207	5	213	213	NUM
cana-4790	207	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4790	207	7	f1	f1	NOUN
cana-4790	207	8	score	score	NOUN
cana-4790	207	9	is	be	AUX
cana-4790	207	10	a	a	DET
cana-4790	207	11	combination	combination	NOUN
cana-4790	207	12	of	of	ADP
cana-4790	207	13	accuracy	accuracy	NOUN
cana-4790	207	14	and	and	CCONJ
cana-4790	207	15	memory	memory	NOUN
cana-4790	207	16	shown	show	VERB
cana-4790	207	17	in	in	ADP
cana-4790	207	18	figure	figure	NOUN
cana-4790	207	19	6	6	NUM
cana-4790	207	20	.	.	PUNCT
cana-4790	208	1	with	with	ADP
cana-4790	208	2	a	a	DET
cana-4790	208	3	score	score	NOUN
cana-4790	208	4	of	of	ADP
cana-4790	208	5	93.1	93.1	NUM
cana-4790	208	6	%	%	NOUN
cana-4790	208	7	,	,	PUNCT
cana-4790	208	8	the	the	DET
cana-4790	208	9	combined	combined	ADJ
cana-4790	208	10	cnnlstm	cnnlstm	NOUN
cana-4790	208	11	model	model	NOUN
cana-4790	208	12	once	once	ADV
cana-4790	208	13	again	again	ADV
cana-4790	208	14	comes	come	VERB
cana-4790	208	15	out	out	ADP
cana-4790	208	16	on	on	ADP
cana-4790	208	17	top	top	NOUN
cana-4790	208	18	,	,	PUNCT
cana-4790	208	19	showing	show	VERB
cana-4790	208	20	fair	fair	ADJ
cana-4790	208	21	efficiency	efficiency	NOUN
cana-4790	208	22	.	.	PUNCT
cana-4790	209	1	it	it	PRON
cana-4790	209	2	does	do	VERB
cana-4790	209	3	a	a	DET
cana-4790	209	4	good	good	ADJ
cana-4790	209	5	job	job	NOUN
cana-4790	209	6	of	of	ADP
cana-4790	209	7	finding	find	VERB
cana-4790	209	8	ad	ad	NOUN
cana-4790	209	9	cases	case	NOUN
cana-4790	209	10	while	while	SCONJ
cana-4790	209	11	also	also	ADV
cana-4790	209	12	avoiding	avoid	VERB
cana-4790	209	13	fake	fake	ADJ
cana-4790	209	14	alarms	alarm	NOUN
cana-4790	209	15	,	,	PUNCT
cana-4790	209	16	which	which	PRON
cana-4790	209	17	makes	make	VERB
cana-4790	209	18	it	it	PRON
cana-4790	209	19	perfect	perfect	ADJ
cana-4790	209	20	for	for	ADP
cana-4790	209	21	use	use	NOUN
cana-4790	209	22	in	in	ADP
cana-4790	209	23	the	the	DET
cana-4790	209	24	real	real	ADJ
cana-4790	209	25	world	world	NOUN
cana-4790	209	26	.	.	PUNCT
cana-4790	210	1	5	5	X
cana-4790	210	2	.	.	X
cana-4790	210	3	conclusion	conclusion	NOUN
cana-4790	210	4	this	this	DET
cana-4790	210	5	study	study	NOUN
cana-4790	210	6	suggested	suggest	VERB
cana-4790	210	7	a	a	DET
cana-4790	210	8	mixed	mixed	ADJ
cana-4790	210	9	deep	deep	ADJ
cana-4790	210	10	learning	learning	NOUN
cana-4790	210	11	model	model	NOUN
cana-4790	210	12	using	use	VERB
cana-4790	210	13	both	both	CCONJ
cana-4790	210	14	convolutional	convolutional	ADJ
cana-4790	210	15	neural	neural	ADJ
cana-4790	210	16	networks	network	NOUN
cana-4790	210	17	(	(	PUNCT
cana-4790	210	18	cnn	cnn	PROPN
cana-4790	210	19	)	)	PUNCT
cana-4790	210	20	and	and	CCONJ
cana-4790	210	21	long	long	ADJ
cana-4790	210	22	short	short	ADJ
cana-4790	210	23	-	-	PUNCT
cana-4790	210	24	term	term	NOUN
cana-4790	210	25	memory	memory	NOUN
cana-4790	210	26	(	(	PUNCT
cana-4790	210	27	lstm	lstm	NOUN
cana-4790	210	28	)	)	PUNCT
cana-4790	210	29	networks	network	NOUN
cana-4790	210	30	to	to	PART
cana-4790	210	31	automatically	automatically	ADV
cana-4790	210	32	find	find	VERB
cana-4790	210	33	alzheimer	alzheimer	NOUN
cana-4790	210	34	's	's	PART
cana-4790	210	35	disease	disease	NOUN
cana-4790	210	36	(	(	PUNCT
cana-4790	210	37	ad	ad	NOUN
cana-4790	210	38	)	)	PUNCT
cana-4790	210	39	in	in	ADP
cana-4790	210	40	eeg	eeg	PROPN
cana-4790	210	41	data	datum	NOUN
cana-4790	210	42	.	.	PUNCT
cana-4790	211	1	the	the	DET
cana-4790	211	2	model	model	NOUN
cana-4790	211	3	was	be	AUX
cana-4790	211	4	tested	test	VERB
cana-4790	211	5	using	use	VERB
cana-4790	211	6	a	a	DET
cana-4790	211	7	dataset	dataset	NOUN
cana-4790	211	8	that	that	PRON
cana-4790	211	9	was	be	AUX
cana-4790	211	10	open	open	ADJ
cana-4790	211	11	to	to	ADP
cana-4790	211	12	the	the	DET
cana-4790	211	13	public	public	NOUN
cana-4790	211	14	and	and	CCONJ
cana-4790	211	15	included	include	VERB
cana-4790	211	16	resting	resting	NOUN
cana-4790	211	17	-	-	PUNCT
cana-4790	211	18	state	state	NOUN
cana-4790	211	19	eeg	eeg	NOUN
cana-4790	211	20	records	record	NOUN
cana-4790	211	21	from	from	ADP
cana-4790	211	22	people	people	NOUN
cana-4790	211	23	with	with	ADP
cana-4790	211	24	ad	ad	NOUN
cana-4790	211	25	and	and	CCONJ
cana-4790	211	26	healthy	healthy	ADJ
cana-4790	211	27	controls	control	NOUN
cana-4790	211	28	.	.	PUNCT
cana-4790	212	1	a	a	DET
cana-4790	212	2	full	full	ADJ
cana-4790	212	3	preparation	preparation	NOUN
cana-4790	212	4	workflow	workflow	NOUN
cana-4790	212	5	was	be	AUX
cana-4790	212	6	created	create	VERB
cana-4790	212	7	to	to	PART
cana-4790	212	8	standardise	standardise	VERB
cana-4790	212	9	,	,	PUNCT
cana-4790	212	10	filter	filter	NOUN
cana-4790	212	11	,	,	PUNCT
cana-4790	212	12	and	and	CCONJ
cana-4790	212	13	divide	divide	VERB
cana-4790	212	14	the	the	DET
cana-4790	212	15	eeg	eeg	NOUN
cana-4790	212	16	data	datum	NOUN
cana-4790	212	17	so	so	SCONJ
cana-4790	212	18	that	that	SCONJ
cana-4790	212	19	it	it	PRON
cana-4790	212	20	could	could	AUX
cana-4790	212	21	be	be	AUX
cana-4790	212	22	used	use	VERB
cana-4790	212	23	in	in	ADP
cana-4790	212	24	the	the	DET
cana-4790	212	25	model	model	NOUN
cana-4790	212	26	most	most	ADV
cana-4790	212	27	effectively	effectively	ADV
cana-4790	212	28	.	.	PUNCT
cana-4790	213	1	the	the	DET
cana-4790	213	2	tests	test	NOUN
cana-4790	213	3	clearly	clearly	ADV
cana-4790	213	4	showed	show	VERB
cana-4790	213	5	that	that	SCONJ
cana-4790	213	6	the	the	DET
cana-4790	213	7	suggested	suggest	VERB
cana-4790	213	8	cnn	cnn	PROPN
cana-4790	213	9	-	-	PUNCT
cana-4790	213	10	lstm	lstm	PROPN
cana-4790	213	11	design	design	NOUN
cana-4790	213	12	did	do	VERB
cana-4790	213	13	better	well	ADV
cana-4790	213	14	in	in	ADP
cana-4790	213	15	terms	term	NOUN
cana-4790	213	16	of	of	ADP
cana-4790	213	17	accuracy	accuracy	NOUN
cana-4790	213	18	,	,	PUNCT
cana-4790	213	19	precision	precision	NOUN
cana-4790	213	20	,	,	PUNCT
cana-4790	213	21	recall	recall	NOUN
cana-4790	213	22	,	,	PUNCT
cana-4790	213	23	and	and	CCONJ
cana-4790	213	24	f1	f1	NOUN
cana-4790	213	25	score	score	NOUN
cana-4790	213	26	than	than	ADP
cana-4790	213	27	traditional	traditional	ADJ
cana-4790	213	28	machine	machine	NOUN
cana-4790	213	29	learning	learning	NOUN
cana-4790	213	30	methods	method	NOUN
cana-4790	213	31	(	(	PUNCT
cana-4790	213	32	like	like	ADP
cana-4790	213	33	svm	svm	ADJ
cana-4790	213	34	and	and	CCONJ
cana-4790	213	35	random	random	ADJ
cana-4790	213	36	forest	forest	NOUN
cana-4790	213	37	)	)	PUNCT
cana-4790	213	38	and	and	CCONJ
cana-4790	213	39	deep	deep	ADJ
cana-4790	213	40	learning	learning	NOUN
cana-4790	213	41	models	model	NOUN
cana-4790	213	42	that	that	PRON
cana-4790	213	43	worked	work	VERB
cana-4790	213	44	on	on	ADP
cana-4790	213	45	their	their	PRON
cana-4790	213	46	own	own	ADJ
cana-4790	213	47	(	(	PUNCT
cana-4790	213	48	cnn	cnn	PROPN
cana-4790	213	49	-	-	PUNCT
cana-4790	213	50	only	only	ADV
cana-4790	213	51	and	and	CCONJ
cana-4790	213	52	lstm	lstm	NOUN
cana-4790	213	53	-	-	PUNCT
cana-4790	213	54	only	only	ADV
cana-4790	213	55	)	)	PUNCT
cana-4790	213	56	.	.	PUNCT
cana-4790	214	1	notably	notably	ADV
cana-4790	214	2	,	,	PUNCT
cana-4790	214	3	the	the	DET
cana-4790	214	4	blend	blend	NOUN
cana-4790	214	5	model	model	NOUN
cana-4790	214	6	had	have	VERB
cana-4790	214	7	a	a	DET
cana-4790	214	8	recall	recall	NOUN
cana-4790	214	9	rate	rate	NOUN
cana-4790	214	10	of	of	ADP
cana-4790	214	11	94.8	94.8	NUM
cana-4790	214	12	%	%	NOUN
cana-4790	214	13	,	,	PUNCT
cana-4790	214	14	which	which	PRON
cana-4790	214	15	shows	show	VERB
cana-4790	214	16	how	how	SCONJ
cana-4790	214	17	well	well	ADV
cana-4790	214	18	it	it	PRON
cana-4790	214	19	works	work	VERB
cana-4790	214	20	at	at	ADP
cana-4790	214	21	reducing	reduce	VERB
cana-4790	214	22	false	false	ADJ
cana-4790	214	23	positives	positive	NOUN
cana-4790	214	24	,	,	PUNCT
cana-4790	214	25	which	which	PRON
cana-4790	214	26	is	be	AUX
cana-4790	214	27	an	an	DET
cana-4790	214	28	important	important	ADJ
cana-4790	214	29	part	part	NOUN
cana-4790	214	30	of	of	ADP
cana-4790	214	31	medical	medical	ADJ
cana-4790	214	32	diagnosis	diagnosis	NOUN
cana-4790	214	33	.	.	PUNCT
cana-4790	215	1	the	the	DET
cana-4790	215	2	cnn	cnn	PROPN
cana-4790	215	3	-	-	PUNCT
cana-4790	215	4	lstm	lstm	PROPN
cana-4790	215	5	model	model	NOUN
cana-4790	215	6	is	be	AUX
cana-4790	215	7	a	a	DET
cana-4790	215	8	strong	strong	ADJ
cana-4790	215	9	and	and	CCONJ
cana-4790	215	10	scalable	scalable	ADJ
cana-4790	215	11	way	way	NOUN
cana-4790	215	12	to	to	PART
cana-4790	215	13	find	find	VERB
cana-4790	215	14	early	early	ADJ
cana-4790	215	15	signs	sign	NOUN
cana-4790	215	16	of	of	ADP
cana-4790	215	17	ad	ad	NOUN
cana-4790	215	18	because	because	SCONJ
cana-4790	215	19	it	it	PRON
cana-4790	215	20	learns	learn	VERB
cana-4790	215	21	both	both	CCONJ
cana-4790	215	22	spatial	spatial	ADJ
cana-4790	215	23	features	feature	NOUN
cana-4790	215	24	from	from	ADP
cana-4790	215	25	eeg	eeg	NOUN
cana-4790	215	26	channels	channel	NOUN
cana-4790	215	27	and	and	CCONJ
cana-4790	215	28	temporal	temporal	ADJ
cana-4790	215	29	relationships	relationship	NOUN
cana-4790	215	30	in	in	ADP
cana-4790	215	31	brain	brain	NOUN
cana-4790	215	32	activity	activity	NOUN
cana-4790	215	33	at	at	ADP
cana-4790	215	34	the	the	DET
cana-4790	215	35	same	same	ADJ
cana-4790	215	36	time	time	NOUN
cana-4790	215	37	.	.	PUNCT
cana-4790	216	1	because	because	SCONJ
cana-4790	216	2	it	it	PRON
cana-4790	216	3	works	work	VERB
cana-4790	216	4	so	so	ADV
cana-4790	216	5	well	well	ADV
cana-4790	216	6	,	,	PUNCT
cana-4790	216	7	it	it	PRON
cana-4790	216	8	could	could	AUX
cana-4790	216	9	be	be	AUX
cana-4790	216	10	useful	useful	ADJ
cana-4790	216	11	in	in	ADP
cana-4790	216	12	clinical	clinical	ADJ
cana-4790	216	13	decision	decision	NOUN
cana-4790	216	14	support	support	NOUN
cana-4790	216	15	tools	tool	NOUN
cana-4790	216	16	and	and	CCONJ
cana-4790	216	17	real	real	ADJ
cana-4790	216	18	-	-	PUNCT
cana-4790	216	19	time	time	NOUN
cana-4790	216	20	tracking	tracking	NOUN
cana-4790	216	21	of	of	ADP
cana-4790	216	22	brain	brain	NOUN
cana-4790	216	23	health	health	NOUN
cana-4790	216	24	.	.	PUNCT
cana-4790	217	1	more	more	ADJ
cana-4790	217	2	work	work	NOUN
cana-4790	217	3	needs	need	VERB
cana-4790	217	4	to	to	PART
cana-4790	217	5	be	be	AUX
cana-4790	217	6	done	do	VERB
cana-4790	217	7	to	to	PART
cana-4790	217	8	increase	increase	VERB
cana-4790	217	9	the	the	DET
cana-4790	217	10	dataset	dataset	NOUN
cana-4790	217	11	,	,	PUNCT
cana-4790	217	12	make	make	VERB
cana-4790	217	13	the	the	DET
cana-4790	217	14	models	model	NOUN
cana-4790	217	15	easier	easy	ADJ
cana-4790	217	16	to	to	PART
cana-4790	217	17	understand	understand	VERB
cana-4790	217	18	,	,	PUNCT
cana-4790	217	19	and	and	CCONJ
cana-4790	217	20	add	add	VERB
cana-4790	217	21	real	real	ADJ
cana-4790	217	22	-	-	PUNCT
cana-4790	217	23	time	time	NOUN
cana-4790	217	24	eeg	eeg	NOUN
cana-4790	217	25	analysis	analysis	NOUN
cana-4790	217	26	so	so	SCONJ
cana-4790	217	27	that	that	SCONJ
cana-4790	217	28	they	they	PRON
cana-4790	217	29	can	can	AUX
cana-4790	217	30	be	be	AUX
cana-4790	217	31	used	use	VERB
cana-4790	217	32	in	in	ADP
cana-4790	217	33	more	more	ADV
cana-4790	217	34	therapeutic	therapeutic	ADJ
cana-4790	217	35	settings	setting	NOUN
cana-4790	217	36	.	.	PUNCT
cana-4790	218	1	references	reference	NOUN
cana-4790	218	2	[	[	X
cana-4790	218	3	1	1	NUM
cana-4790	218	4	]	]	X
cana-4790	218	5	de	de	X
cana-4790	218	6	mendonça	mendonça	X
cana-4790	218	7	,	,	PUNCT
cana-4790	218	8	l.j.c	l.j.c	NOUN
cana-4790	218	9	.	.	PUNCT
cana-4790	218	10	;	;	PUNCT
cana-4790	218	11	ferrari	ferrari	PROPN
cana-4790	218	12	,	,	PUNCT
cana-4790	218	13	r.j	r.j	PROPN
cana-4790	218	14	.	.	PROPN
cana-4790	218	15	alzheimer	alzheimer	PROPN
cana-4790	218	16	’s	’s	PART
cana-4790	218	17	disease	disease	NOUN
cana-4790	218	18	classification	classification	NOUN
cana-4790	218	19	based	base	VERB
cana-4790	218	20	on	on	ADP
cana-4790	218	21	graph	graph	NOUN
cana-4790	218	22	kernel	kernel	PROPN
cana-4790	218	23	svms	svms	PROPN
cana-4790	218	24	constructed	construct	VERB
cana-4790	218	25	with	with	ADP
cana-4790	218	26	3d	3d	PROPN
cana-4790	218	27	texture	texture	NOUN
cana-4790	218	28	features	feature	NOUN
cana-4790	218	29	extracted	extract	VERB
cana-4790	218	30	from	from	ADP
cana-4790	218	31	mr	mr	PROPN
cana-4790	218	32	images	image	NOUN
cana-4790	218	33	.	.	PUNCT
cana-4790	219	1	expert	expert	NOUN
cana-4790	219	2	syst	syst	PROPN
cana-4790	219	3	.	.	PUNCT
cana-4790	220	1	appl	appl	PROPN
cana-4790	220	2	.	.	PROPN
cana-4790	221	1	2023	2023	NUM
cana-4790	221	2	,	,	PUNCT
cana-4790	221	3	211	211	NUM
cana-4790	221	4	,	,	PUNCT
cana-4790	221	5	118633	118633	NUM
cana-4790	221	6	.	.	PUNCT
cana-4790	222	1	[	[	X
cana-4790	222	2	2	2	NUM
cana-4790	222	3	]	]	PUNCT
cana-4790	222	4	sharma	sharma	PROPN
cana-4790	222	5	,	,	PUNCT
cana-4790	222	6	a.	a.	NOUN
cana-4790	222	7	;	;	PUNCT
cana-4790	222	8	kaur	kaur	PROPN
cana-4790	222	9	,	,	PUNCT
cana-4790	222	10	s.	s.	PROPN
cana-4790	222	11	;	;	PUNCT
cana-4790	222	12	memon	memon	PROPN
cana-4790	222	13	,	,	PUNCT
cana-4790	222	14	n.	n.	PROPN
cana-4790	222	15	;	;	PUNCT
cana-4790	222	16	jainul	jainul	PROPN
cana-4790	222	17	fathima	fathima	PROPN
cana-4790	222	18	,	,	PUNCT
cana-4790	222	19	a.	a.	NOUN
cana-4790	222	20	;	;	PUNCT
cana-4790	222	21	ray	ray	PROPN
cana-4790	222	22	,	,	PUNCT
cana-4790	222	23	s.	s.	PROPN
cana-4790	222	24	;	;	PUNCT
cana-4790	222	25	bhatt	bhatt	PROPN
cana-4790	222	26	,	,	PUNCT
cana-4790	222	27	m.w	m.w	PROPN
cana-4790	222	28	.	.	PROPN
cana-4790	222	29	alzheimer	alzheimer	PROPN
cana-4790	222	30	’s	’s	PART
cana-4790	222	31	patients	patient	NOUN
cana-4790	222	32	detection	detection	NOUN
cana-4790	222	33	using	use	VERB
cana-4790	222	34	support	support	NOUN
cana-4790	222	35	vector	vector	NOUN
cana-4790	222	36	machine	machine	NOUN
cana-4790	222	37	(	(	PUNCT
cana-4790	222	38	svm	svm	PROPN
cana-4790	222	39	)	)	PUNCT
cana-4790	222	40	with	with	ADP
cana-4790	222	41	quantitative	quantitative	ADJ
cana-4790	222	42	analysis	analysis	NOUN
cana-4790	222	43	.	.	PUNCT
cana-4790	223	1	neurosci	neurosci	PROPN
cana-4790	223	2	.	.	PUNCT
cana-4790	224	1	inform	inform	NOUN
cana-4790	224	2	.	.	PUNCT
cana-4790	225	1	2021	2021	NUM
cana-4790	225	2	,	,	PUNCT
cana-4790	225	3	1	1	NUM
cana-4790	225	4	,	,	PUNCT
cana-4790	225	5	100012	100012	NUM
cana-4790	225	6	.	.	PUNCT
cana-4790	226	1	[	[	X
cana-4790	226	2	3	3	NUM
cana-4790	226	3	]	]	X
cana-4790	226	4	khan	khan	PROPN
cana-4790	226	5	,	,	PUNCT
cana-4790	226	6	y.f	y.f	PROPN
cana-4790	226	7	.	.	PUNCT
cana-4790	226	8	;	;	PUNCT
cana-4790	226	9	kaushik	kaushik	PROPN
cana-4790	226	10	,	,	PUNCT
cana-4790	226	11	b.	b.	PROPN
cana-4790	226	12	;	;	PUNCT
cana-4790	226	13	chowdhary	chowdhary	PROPN
cana-4790	226	14	,	,	PUNCT
cana-4790	226	15	c.l	c.l	PROPN
cana-4790	226	16	.	.	PROPN
cana-4790	226	17	;	;	PUNCT
cana-4790	226	18	srivastava	srivastava	PROPN
cana-4790	226	19	,	,	PUNCT
cana-4790	226	20	g.	g.	PROPN
cana-4790	226	21	ensemble	ensemble	ADJ
cana-4790	226	22	model	model	NOUN
cana-4790	226	23	for	for	ADP
cana-4790	226	24	diagnostic	diagnostic	ADJ
cana-4790	226	25	classification	classification	NOUN
cana-4790	226	26	of	of	ADP
cana-4790	226	27	alzheimer	alzheimer	PROPN
cana-4790	226	28	’s	’s	PART
cana-4790	226	29	disease	disease	NOUN
cana-4790	226	30	based	base	VERB
cana-4790	226	31	on	on	ADP
cana-4790	226	32	brain	brain	NOUN
cana-4790	226	33	anatomical	anatomical	ADJ
cana-4790	226	34	magnetic	magnetic	ADJ
cana-4790	226	35	resonance	resonance	NOUN
cana-4790	226	36	imaging	imaging	NOUN
cana-4790	226	37	.	.	PUNCT
cana-4790	227	1	diagnostics	diagnostic	NOUN
cana-4790	227	2	2022	2022	NUM
cana-4790	227	3	,	,	PUNCT
cana-4790	227	4	12	12	NUM
cana-4790	227	5	,	,	PUNCT
cana-4790	227	6	3193	3193	NUM
cana-4790	227	7	.	.	PUNCT
cana-4790	228	1	[	[	X
cana-4790	228	2	4	4	NUM
cana-4790	228	3	]	]	X
cana-4790	228	4	khedher	khedher	PROPN
cana-4790	228	5	,	,	PUNCT
cana-4790	228	6	l.	l.	PROPN
cana-4790	228	7	;	;	PUNCT
cana-4790	228	8	illán	illán	PROPN
cana-4790	228	9	,	,	PUNCT
cana-4790	228	10	i.a	i.a	PROPN
cana-4790	228	11	.	.	PROPN
cana-4790	228	12	;	;	PUNCT
cana-4790	228	13	górriz	górriz	NOUN
cana-4790	228	14	,	,	PUNCT
cana-4790	228	15	j.m	j.m	PROPN
cana-4790	228	16	.	.	PROPN
cana-4790	228	17	;	;	PUNCT
cana-4790	228	18	ramírez	ramírez	NOUN
cana-4790	228	19	,	,	PUNCT
cana-4790	228	20	j.	j.	PROPN
cana-4790	228	21	;	;	PUNCT
cana-4790	228	22	brahim	brahim	PROPN
cana-4790	228	23	,	,	PUNCT
cana-4790	228	24	a.	a.	PROPN
cana-4790	228	25	;	;	PUNCT
cana-4790	228	26	meyer	meyer	PROPN
cana-4790	228	27	-	-	PUNCT
cana-4790	228	28	baese	baese	PROPN
cana-4790	228	29	,	,	PUNCT
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cana-4790	228	31	independent	independent	PROPN
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cana-4790	228	33	analysis	analysis	NOUN
cana-4790	228	34	-	-	PUNCT
cana-4790	228	35	support	support	NOUN
cana-4790	228	36	vector	vector	NOUN
cana-4790	228	37	machine	machine	NOUN
cana-4790	228	38	-	-	PUNCT
cana-4790	228	39	based	base	VERB
cana-4790	228	40	computer	computer	NOUN
cana-4790	228	41	-	-	PUNCT
cana-4790	228	42	aided	aid	VERB
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cana-4790	228	44	system	system	NOUN
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cana-4790	228	47	’s	’s	PART
cana-4790	228	48	with	with	ADP
cana-4790	228	49	visual	visual	ADJ
cana-4790	228	50	support	support	NOUN
cana-4790	228	51	.	.	PUNCT
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cana-4790	229	2	.	.	PUNCT
cana-4790	230	1	j.	j.	PROPN
cana-4790	230	2	neural	neural	PROPN
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cana-4790	230	4	.	.	PUNCT
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cana-4790	230	6	,	,	PUNCT
cana-4790	230	7	27	27	NUM
cana-4790	230	8	,	,	PUNCT
cana-4790	230	9	1650050	1650050	NUM
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cana-4790	231	2	5	5	NUM
cana-4790	231	3	]	]	X
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cana-4790	231	6	j.	j.	PROPN
cana-4790	231	7	;	;	PUNCT
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cana-4790	231	10	j.	j.	PROPN
cana-4790	231	11	;	;	PUNCT
cana-4790	231	12	hu	hu	PROPN
cana-4790	231	13	,	,	PUNCT
cana-4790	231	14	b.	b.	PROPN
cana-4790	231	15	;	;	PUNCT
cana-4790	231	16	wu	wu	PROPN
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cana-4790	231	18	f.-x	f.-x	PROPN
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cana-4790	231	20	;	;	PUNCT
cana-4790	231	21	pan	pan	PROPN
cana-4790	231	22	,	,	PUNCT
cana-4790	231	23	y.	y.	PROPN
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cana-4790	231	25	’s	’s	PART
cana-4790	231	26	disease	disease	NOUN
cana-4790	231	27	classification	classification	NOUN
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cana-4790	231	29	on	on	ADP
cana-4790	231	30	individual	individual	ADJ
cana-4790	231	31	hierarchical	hierarchical	ADJ
cana-4790	231	32	networks	network	NOUN
cana-4790	231	33	constructed	construct	VERB
cana-4790	231	34	with	with	ADP
cana-4790	231	35	3	3	NUM
cana-4790	231	36	-	-	PUNCT
cana-4790	231	37	d	d	NOUN
cana-4790	231	38	texture	texture	NOUN
cana-4790	231	39	features	feature	NOUN
cana-4790	231	40	.	.	PUNCT
cana-4790	232	1	ieee	ieee	PROPN
cana-4790	232	2	trans	trans	PROPN
cana-4790	232	3	.	.	PUNCT
cana-4790	233	1	nanobiosci	nanobiosci	PROPN
cana-4790	233	2	.	.	PUNCT
cana-4790	234	1	2017	2017	NUM
cana-4790	234	2	,	,	PUNCT
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cana-4790	234	6	.	.	PUNCT
cana-4790	235	1	[	[	X
cana-4790	235	2	6	6	NUM
cana-4790	235	3	]	]	SYM
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cana-4790	235	6	x.	x.	PROPN
cana-4790	235	7	;	;	PUNCT
cana-4790	235	8	chen	chen	PROPN
cana-4790	235	9	,	,	PUNCT
cana-4790	235	10	l.	l.	PROPN
cana-4790	235	11	;	;	PUNCT
cana-4790	235	12	jiang	jiang	PROPN
cana-4790	235	13	,	,	PUNCT
cana-4790	235	14	c.	c.	PROPN
cana-4790	235	15	;	;	PUNCT
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cana-4790	235	17	,	,	PUNCT
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cana-4790	235	21	classification	classification	NOUN
cana-4790	235	22	of	of	ADP
cana-4790	235	23	alzheimer	alzheimer	PROPN
cana-4790	235	24	disease	disease	NOUN
cana-4790	235	25	based	base	VERB
cana-4790	235	26	on	on	ADP
cana-4790	235	27	quantification	quantification	NOUN
cana-4790	235	28	of	of	ADP
cana-4790	235	29	mri	mri	ADJ
cana-4790	235	30	deformation	deformation	NOUN
cana-4790	235	31	.	.	PUNCT
cana-4790	236	1	plos	plos	PROPN
cana-4790	236	2	one	one	NUM
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cana-4790	236	4	,	,	PUNCT
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cana-4790	236	6	,	,	PUNCT
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cana-4790	236	8	.	.	PUNCT
cana-4790	237	1	[	[	X
cana-4790	237	2	7	7	NUM
cana-4790	237	3	]	]	PUNCT
cana-4790	237	4	pasnoori	pasnoori	NOUN
cana-4790	237	5	,	,	PUNCT
cana-4790	237	6	n.	n.	NOUN
cana-4790	237	7	;	;	PUNCT
cana-4790	237	8	flores	flores	PROPN
cana-4790	237	9	-	-	PUNCT
cana-4790	237	10	garcia	garcia	PROPN
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cana-4790	237	13	;	;	PUNCT
cana-4790	237	14	barkana	barkana	PROPN
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cana-4790	237	16	b.d	b.d	PROPN
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cana-4790	237	19	-	-	PUNCT
cana-4790	237	20	based	base	VERB
cana-4790	237	21	features	feature	NOUN
cana-4790	237	22	track	track	VERB
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cana-4790	237	24	’s	’s	PART
cana-4790	237	25	progression	progression	NOUN
cana-4790	237	26	in	in	ADP
cana-4790	237	27	brain	brain	NOUN
cana-4790	237	28	mri	mri	NOUN
cana-4790	237	29	.	.	PUNCT
cana-4790	238	1	sci	sci	PROPN
cana-4790	238	2	.	.	PROPN
cana-4790	238	3	rep	rep	PROPN
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cana-4790	238	8	,	,	PUNCT
cana-4790	238	9	257	257	NUM
cana-4790	238	10	.	.	PUNCT
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cana-4790	239	2	8	8	NUM
cana-4790	239	3	]	]	X
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cana-4790	239	9	of	of	ADP
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cana-4790	239	15	analysis	analysis	NOUN
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cana-4790	241	3	,	,	PUNCT
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cana-4790	241	5	,	,	PUNCT
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cana-4790	241	7	.	.	PUNCT
cana-4790	242	1	[	[	X
cana-4790	242	2	9	9	NUM
cana-4790	242	3	]	]	SYM
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cana-4790	242	5	,	,	PUNCT
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cana-4790	242	7	;	;	PUNCT
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cana-4790	242	9	,	,	PUNCT
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cana-4790	242	11	;	;	PUNCT
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cana-4790	242	13	,	,	PUNCT
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cana-4790	242	34	.	.	PUNCT
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cana-4790	243	7	.	.	PUNCT
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cana-4790	244	2	on	on	ADP
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cana-4790	244	6	issn	issn	NOUN
cana-4790	244	7	:	:	PUNCT
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cana-4790	245	5	214	214	NUM
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cana-4790	246	11	;	;	PUNCT
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cana-4790	246	23	mci	mci	PROPN
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cana-4790	246	26	with	with	ADP
cana-4790	246	27	fdgpet	fdgpet	ADJ
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cana-4790	246	29	images	image	NOUN
cana-4790	246	30	at	at	ADP
cana-4790	246	31	different	different	ADJ
cana-4790	246	32	prodromal	prodromal	ADJ
cana-4790	246	33	stages	stage	NOUN
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cana-4790	250	23	;	;	PUNCT
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cana-4790	250	29	;	;	PUNCT
cana-4790	250	30	daducci	daducci	PROPN
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cana-4790	250	35	,	,	PUNCT
cana-4790	250	36	c.	c.	PROPN
cana-4790	250	37	;	;	PUNCT
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cana-4790	250	40	s.	s.	PROPN
cana-4790	250	41	;	;	PUNCT
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cana-4790	251	13	impairment	impairment	NOUN
cana-4790	251	14	and	and	CCONJ
cana-4790	251	15	alzheimer	alzheimer	NOUN
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cana-4790	251	17	disease	disease	NOUN
cana-4790	251	18	.	.	PUNCT
cana-4790	252	1	neuroimage	neuroimage	PROPN
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cana-4790	253	5	7–17	7–17	PROPN
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cana-4790	254	1	[	[	X
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cana-4790	254	3	]	]	X
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cana-4790	254	5	,	,	PUNCT
cana-4790	254	6	y.	y.	PROPN
cana-4790	254	7	;	;	PUNCT
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cana-4790	254	10	s.	s.	PROPN
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cana-4790	255	3	]	]	X
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cana-4790	255	7	;	;	PUNCT
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cana-4790	255	11	;	;	PUNCT
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cana-4790	255	14	p.	p.	PROPN
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cana-4790	255	16	wang	wang	PROPN
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cana-4790	255	18	s.	s.	PROPN
cana-4790	255	19	;	;	PUNCT
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cana-4790	255	21	,	,	PUNCT
cana-4790	255	22	g.	g.	PROPN
cana-4790	255	23	;	;	PUNCT
cana-4790	255	24	yang	yang	PROPN
cana-4790	255	25	,	,	PUNCT
cana-4790	255	26	j.	j.	PROPN
cana-4790	255	27	;	;	PUNCT
cana-4790	255	28	yuan	yuan	PROPN
cana-4790	255	29	,	,	PUNCT
cana-4790	255	30	t.-f	t.-f	PROPN
cana-4790	255	31	.	.	PUNCT
cana-4790	255	32	detection	detection	NOUN
cana-4790	255	33	of	of	ADP
cana-4790	255	34	subjects	subject	NOUN
cana-4790	255	35	and	and	CCONJ
cana-4790	255	36	brain	brain	NOUN
cana-4790	255	37	regions	region	NOUN
cana-4790	255	38	related	relate	VERB
cana-4790	255	39	to	to	ADP
cana-4790	255	40	alzheimer	alzheimer	PROPN
cana-4790	255	41	’s	’s	PART
cana-4790	255	42	disease	disease	NOUN
cana-4790	255	43	using	use	VERB
cana-4790	255	44	3d	3d	NUM
cana-4790	255	45	mri	mri	NOUN
cana-4790	255	46	scans	scan	NOUN
cana-4790	255	47	based	base	VERB
cana-4790	255	48	on	on	ADP
cana-4790	255	49	eigenbrain	eigenbrain	NOUN
cana-4790	255	50	and	and	CCONJ
cana-4790	255	51	machine	machine	NOUN
cana-4790	255	52	learning	learning	NOUN
cana-4790	255	53	.	.	PUNCT
cana-4790	256	1	front	front	ADJ
cana-4790	256	2	.	.	PUNCT
cana-4790	257	1	comput	comput	NOUN
cana-4790	257	2	.	.	PUNCT
cana-4790	258	1	neurosci	neurosci	PROPN
cana-4790	258	2	.	.	PUNCT
cana-4790	259	1	2015	2015	NUM
cana-4790	259	2	,	,	PUNCT
cana-4790	259	3	9	9	NUM
cana-4790	259	4	,	,	PUNCT
cana-4790	259	5	66	66	NUM
cana-4790	259	6	.	.	PUNCT
cana-4790	260	1	[	[	X
cana-4790	260	2	14	14	NUM
cana-4790	260	3	]	]	X
cana-4790	260	4	xu	xu	PROPN
cana-4790	260	5	,	,	PUNCT
cana-4790	260	6	y.	y.	PROPN
cana-4790	260	7	;	;	PUNCT
cana-4790	260	8	pan	pan	PROPN
cana-4790	260	9	,	,	PUNCT
cana-4790	260	10	x.	x.	PROPN
cana-4790	260	11	;	;	PUNCT
cana-4790	260	12	zhou	zhou	PROPN
cana-4790	260	13	,	,	PUNCT
cana-4790	260	14	z.	z.	PROPN
cana-4790	260	15	;	;	PUNCT
cana-4790	260	16	yang	yang	PROPN
cana-4790	260	17	,	,	PUNCT
cana-4790	260	18	z.	z.	PROPN
cana-4790	260	19	;	;	PUNCT
cana-4790	260	20	zhang	zhang	PROPN
cana-4790	260	21	,	,	PUNCT
cana-4790	260	22	y.	y.	PROPN
cana-4790	260	23	structural	structural	ADJ
cana-4790	260	24	least	least	ADJ
cana-4790	260	25	square	square	ADJ
cana-4790	260	26	twin	twin	ADJ
cana-4790	260	27	support	support	NOUN
cana-4790	260	28	vector	vector	NOUN
cana-4790	260	29	machine	machine	NOUN
cana-4790	260	30	for	for	ADP
cana-4790	260	31	classification	classification	NOUN
cana-4790	260	32	.	.	PUNCT
cana-4790	261	1	appl	appl	PROPN
cana-4790	261	2	.	.	PUNCT
cana-4790	262	1	intell	intell	PROPN
cana-4790	262	2	.	.	PUNCT
cana-4790	263	1	2015	2015	NUM
cana-4790	263	2	,	,	PUNCT
cana-4790	263	3	42	42	NUM
cana-4790	263	4	,	,	PUNCT
cana-4790	263	5	527–536	527–536	NUM
cana-4790	263	6	.	.	PUNCT
cana-4790	264	1	[	[	X
cana-4790	264	2	15	15	NUM
cana-4790	264	3	]	]	PUNCT
cana-4790	264	4	retico	retico	NOUN
cana-4790	264	5	,	,	PUNCT
cana-4790	264	6	a.	a.	NOUN
cana-4790	264	7	;	;	PUNCT
cana-4790	264	8	bosco	bosco	PROPN
cana-4790	264	9	,	,	PUNCT
cana-4790	264	10	p.	p.	PROPN
cana-4790	264	11	;	;	PUNCT
cana-4790	264	12	cerello	cerello	VERB
cana-4790	264	13	,	,	PUNCT
cana-4790	264	14	p.	p.	PROPN
cana-4790	264	15	;	;	PUNCT
cana-4790	264	16	fiorina	fiorina	PROPN
cana-4790	264	17	,	,	PUNCT
cana-4790	264	18	e.	e.	PROPN
cana-4790	264	19	;	;	PUNCT
cana-4790	264	20	chincarini	chincarini	PROPN
cana-4790	264	21	,	,	PUNCT
cana-4790	264	22	a.	a.	NOUN
cana-4790	264	23	;	;	PUNCT
cana-4790	264	24	fantacci	fantacci	PROPN
cana-4790	264	25	,	,	PUNCT
cana-4790	264	26	m.e	m.e	PROPN
cana-4790	264	27	.	.	PROPN
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cana-4790	264	29	models	model	NOUN
cana-4790	264	30	based	base	VERB
cana-4790	264	31	on	on	ADP
cana-4790	264	32	support	support	NOUN
cana-4790	264	33	vector	vector	NOUN
cana-4790	264	34	machines	machine	NOUN
cana-4790	264	35	:	:	PUNCT
cana-4790	264	36	whole	whole	ADJ
cana-4790	264	37	-	-	PUNCT
cana-4790	264	38	brain	brain	NOUN
cana-4790	264	39	versus	versus	ADP
cana-4790	264	40	regional	regional	ADJ
cana-4790	264	41	analysis	analysis	NOUN
cana-4790	264	42	of	of	ADP
cana-4790	264	43	structural	structural	ADJ
cana-4790	264	44	mri	mri	NOUN
cana-4790	264	45	in	in	ADP
cana-4790	264	46	the	the	DET
cana-4790	264	47	alzheimer	alzheimer	PROPN
cana-4790	264	48	’s	’s	PART
cana-4790	264	49	disease	disease	NOUN
cana-4790	264	50	.	.	PUNCT
cana-4790	265	1	j.	j.	PROPN
cana-4790	265	2	neuroimaging	neuroimage	VERB
cana-4790	265	3	2015	2015	NUM
cana-4790	265	4	,	,	PUNCT
cana-4790	265	5	25	25	NUM
cana-4790	265	6	,	,	PUNCT
cana-4790	265	7	552–563	552–563	NUM
cana-4790	265	8	.	.	PUNCT
cana-4790	266	1	[	[	X
cana-4790	266	2	16	16	NUM
cana-4790	266	3	]	]	X
cana-4790	266	4	ortiz	ortiz	PROPN
cana-4790	266	5	,	,	PUNCT
cana-4790	266	6	a.	a.	NOUN
cana-4790	266	7	;	;	PUNCT
cana-4790	266	8	munilla	munilla	PROPN
cana-4790	266	9	,	,	PUNCT
cana-4790	266	10	j.	j.	PROPN
cana-4790	266	11	;	;	PUNCT
cana-4790	266	12	álvarez	álvarez	NOUN
cana-4790	266	13	-	-	PUNCT
cana-4790	266	14	illán	illán	PROPN
cana-4790	266	15	,	,	PUNCT
cana-4790	266	16	i.	i.	NOUN
cana-4790	266	17	;	;	PUNCT
cana-4790	266	18	górriz	górriz	PROPN
cana-4790	266	19	,	,	PUNCT
cana-4790	266	20	j.m	j.m	PROPN
cana-4790	266	21	.	.	PROPN
cana-4790	266	22	;	;	PUNCT
cana-4790	266	23	ramírez	ramírez	NOUN
cana-4790	266	24	,	,	PUNCT
cana-4790	266	25	j.	j.	PROPN
cana-4790	266	26	exploratory	exploratory	ADJ
cana-4790	266	27	graphical	graphical	ADJ
cana-4790	266	28	models	model	NOUN
cana-4790	266	29	of	of	ADP
cana-4790	266	30	functional	functional	ADJ
cana-4790	266	31	and	and	CCONJ
cana-4790	266	32	structural	structural	ADJ
cana-4790	266	33	connectivity	connectivity	NOUN
cana-4790	266	34	patterns	pattern	NOUN
cana-4790	266	35	for	for	ADP
cana-4790	266	36	alzheimer	alzheimer	PROPN
cana-4790	266	37	’s	’s	PART
cana-4790	266	38	disease	disease	NOUN
cana-4790	266	39	diagnosis	diagnosis	NOUN
cana-4790	266	40	.	.	PUNCT
cana-4790	267	1	front	front	ADJ
cana-4790	267	2	.	.	PUNCT
cana-4790	268	1	comput	comput	NOUN
cana-4790	268	2	.	.	PUNCT
cana-4790	269	1	neurosci	neurosci	PROPN
cana-4790	269	2	.	.	PUNCT
cana-4790	270	1	2015	2015	NUM
cana-4790	270	2	,	,	PUNCT
cana-4790	270	3	9	9	NUM
cana-4790	270	4	,	,	PUNCT
cana-4790	270	5	132	132	NUM
cana-4790	270	6	.	.	PUNCT
cana-4790	271	1	[	[	X
cana-4790	271	2	17	17	NUM
cana-4790	271	3	]	]	X
cana-4790	271	4	zhu	zhu	PROPN
cana-4790	271	5	,	,	PUNCT
cana-4790	271	6	y.	y.	PROPN
cana-4790	271	7	;	;	PUNCT
cana-4790	271	8	zhu	zhu	PROPN
cana-4790	271	9	,	,	PUNCT
cana-4790	271	10	x.	x.	PROPN
cana-4790	271	11	;	;	PUNCT
cana-4790	271	12	kim	kim	PROPN
cana-4790	271	13	,	,	PUNCT
cana-4790	271	14	m.	m.	NOUN
cana-4790	271	15	;	;	PUNCT
cana-4790	271	16	shen	shen	PROPN
cana-4790	271	17	,	,	PUNCT
cana-4790	271	18	d.	d.	PROPN
cana-4790	271	19	;	;	PUNCT
cana-4790	271	20	wu	wu	PROPN
cana-4790	271	21	,	,	PUNCT
cana-4790	271	22	g.	g.	PROPN
cana-4790	271	23	early	early	ADJ
cana-4790	271	24	diagnosis	diagnosis	NOUN
cana-4790	271	25	of	of	ADP
cana-4790	271	26	alzheimer	alzheimer	PROPN
cana-4790	271	27	’s	’s	PART
cana-4790	271	28	disease	disease	NOUN
cana-4790	271	29	by	by	ADP
cana-4790	271	30	joint	joint	ADJ
cana-4790	271	31	feature	feature	NOUN
cana-4790	271	32	selection	selection	NOUN
cana-4790	271	33	and	and	CCONJ
cana-4790	271	34	classification	classification	NOUN
cana-4790	271	35	on	on	ADP
cana-4790	271	36	temporally	temporally	ADV
cana-4790	271	37	structured	structured	ADJ
cana-4790	271	38	support	support	NOUN
cana-4790	271	39	vector	vector	NOUN
cana-4790	271	40	machine	machine	NOUN
cana-4790	271	41	.	.	PUNCT
cana-4790	272	1	in	in	ADP
cana-4790	272	2	proceedings	proceeding	NOUN
cana-4790	272	3	of	of	ADP
cana-4790	272	4	the	the	DET
cana-4790	272	5	medical	medical	ADJ
cana-4790	272	6	image	image	NOUN
cana-4790	272	7	computing	computing	NOUN
cana-4790	272	8	and	and	CCONJ
cana-4790	272	9	computer	computer	NOUN
cana-4790	272	10	-	-	PUNCT
cana-4790	272	11	assisted	assist	VERB
cana-4790	272	12	intervention	intervention	NOUN
cana-4790	272	13	—	—	PUNCT
cana-4790	272	14	miccai	miccai	NOUN
cana-4790	272	15	2016	2016	NUM
cana-4790	272	16	,	,	PUNCT
cana-4790	272	17	athens	athens	PROPN
cana-4790	272	18	,	,	PUNCT
cana-4790	272	19	greece	greece	PROPN
cana-4790	272	20	,	,	PUNCT
cana-4790	272	21	17–21	17–21	NUM
cana-4790	272	22	october	october	PROPN
cana-4790	272	23	2016	2016	NUM
cana-4790	272	24	;	;	PUNCT
cana-4790	272	25	pp	pp	CCONJ
cana-4790	272	26	.	.	PUNCT
cana-4790	273	1	264	264	NUM
cana-4790	273	2	–	–	PUNCT
cana-4790	273	3	272	272	NUM
cana-4790	273	4	.	.	PUNCT
cana-4790	274	1	[	[	X
cana-4790	274	2	18	18	NUM
cana-4790	274	3	]	]	X
cana-4790	274	4	khazaee	khazaee	PROPN
cana-4790	274	5	,	,	PUNCT
cana-4790	274	6	a.	a.	NOUN
cana-4790	274	7	;	;	PUNCT
cana-4790	274	8	ebrahimzadeh	ebrahimzadeh	NOUN
cana-4790	274	9	,	,	PUNCT
cana-4790	274	10	a.	a.	NOUN
cana-4790	274	11	;	;	PUNCT
cana-4790	274	12	babajani	babajani	X
cana-4790	274	13	-	-	PUNCT
cana-4790	274	14	feremi	feremi	PROPN
cana-4790	274	15	,	,	PUNCT
cana-4790	274	16	a.	a.	NOUN
cana-4790	274	17	application	application	NOUN
cana-4790	274	18	of	of	ADP
cana-4790	274	19	advanced	advanced	ADJ
cana-4790	274	20	machine	machine	NOUN
cana-4790	274	21	learning	learn	VERB
cana-4790	274	22	methods	method	NOUN
cana-4790	274	23	on	on	ADP
cana-4790	274	24	resting	rest	VERB
cana-4790	274	25	-	-	PUNCT
cana-4790	274	26	state	state	NOUN
cana-4790	274	27	fmri	fmri	ADJ
cana-4790	274	28	network	network	NOUN
cana-4790	274	29	for	for	ADP
cana-4790	274	30	identification	identification	NOUN
cana-4790	274	31	of	of	ADP
cana-4790	274	32	mild	mild	ADJ
cana-4790	274	33	cognitive	cognitive	ADJ
cana-4790	274	34	impairment	impairment	NOUN
cana-4790	274	35	and	and	CCONJ
cana-4790	274	36	alzheimer	alzheimer	NOUN
cana-4790	274	37	’s	’s	PART
cana-4790	274	38	disease	disease	NOUN
cana-4790	274	39	.	.	PUNCT
cana-4790	275	1	brain	brain	NOUN
cana-4790	275	2	imaging	imaging	NOUN
cana-4790	275	3	behav	behav	NOUN
cana-4790	275	4	.	.	PUNCT
cana-4790	276	1	2016	2016	NUM
cana-4790	276	2	,	,	PUNCT
cana-4790	276	3	10	10	NUM
cana-4790	276	4	,	,	PUNCT
cana-4790	276	5	799–817	799–817	NUM
cana-4790	276	6	.	.	PUNCT
cana-4790	277	1	[	[	X
cana-4790	277	2	19	19	NUM
cana-4790	277	3	]	]	SYM
cana-4790	277	4	suk	suk	NOUN
cana-4790	277	5	,	,	PUNCT
cana-4790	277	6	h.-i	h.-i	PROPN
cana-4790	277	7	.	.	PUNCT
cana-4790	277	8	;	;	PUNCT
cana-4790	277	9	lee	lee	PROPN
cana-4790	277	10	,	,	PUNCT
cana-4790	277	11	s.-w	s.-w	PROPN
cana-4790	277	12	.	.	PUNCT
cana-4790	277	13	;	;	PUNCT
cana-4790	277	14	shen	shen	PROPN
cana-4790	277	15	,	,	PUNCT
cana-4790	277	16	d.	d.	PROPN
cana-4790	277	17	latent	latent	PROPN
cana-4790	277	18	feature	feature	NOUN
cana-4790	277	19	representation	representation	NOUN
cana-4790	277	20	with	with	ADP
cana-4790	277	21	stacked	stack	VERB
cana-4790	277	22	auto	auto	NOUN
cana-4790	277	23	-	-	PUNCT
cana-4790	277	24	encoder	encoder	NOUN
cana-4790	277	25	for	for	ADP
cana-4790	277	26	ad	ad	NOUN
cana-4790	277	27	/	/	SYM
cana-4790	277	28	mci	mci	NOUN
cana-4790	277	29	diagnosis	diagnosis	NOUN
cana-4790	277	30	.	.	PUNCT
cana-4790	278	1	brain	brain	NOUN
cana-4790	278	2	struct	struct	NOUN
cana-4790	278	3	.	.	PUNCT
cana-4790	279	1	funct	funct	PROPN
cana-4790	279	2	.	.	PUNCT
cana-4790	280	1	2015	2015	NUM
cana-4790	280	2	,	,	PUNCT
cana-4790	280	3	220	220	NUM
cana-4790	280	4	,	,	PUNCT
cana-4790	280	5	841–859	841–859	NUM
cana-4790	280	6	.	.	PUNCT
cana-4790	281	1	[	[	X
cana-4790	281	2	20	20	NUM
cana-4790	281	3	]	]	X
cana-4790	281	4	plocharski	plocharski	NOUN
cana-4790	281	5	,	,	PUNCT
cana-4790	281	6	m.	m.	NOUN
cana-4790	281	7	;	;	PUNCT
cana-4790	281	8	østergaard	østergaard	NOUN
cana-4790	281	9	,	,	PUNCT
cana-4790	281	10	l.r	l.r	PROPN
cana-4790	281	11	.	.	PROPN
cana-4790	281	12	extraction	extraction	NOUN
cana-4790	281	13	of	of	ADP
cana-4790	281	14	sulcal	sulcal	ADJ
cana-4790	281	15	medial	medial	ADJ
cana-4790	281	16	surface	surface	NOUN
cana-4790	281	17	and	and	CCONJ
cana-4790	281	18	classification	classification	NOUN
cana-4790	281	19	of	of	ADP
cana-4790	281	20	alzheimer	alzheimer	PROPN
cana-4790	281	21	’s	’s	PART
cana-4790	281	22	disease	disease	NOUN
cana-4790	281	23	using	use	VERB
cana-4790	281	24	sulcal	sulcal	ADJ
cana-4790	281	25	features	feature	NOUN
cana-4790	281	26	.	.	PUNCT
cana-4790	282	1	comput	comput	NOUN
cana-4790	282	2	.	.	PUNCT
cana-4790	283	1	methods	method	NOUN
cana-4790	283	2	programs	program	NOUN
cana-4790	283	3	biomed	biome	VERB
cana-4790	283	4	.	.	PUNCT
cana-4790	284	1	2016	2016	NUM
cana-4790	284	2	,	,	PUNCT
cana-4790	284	3	133	133	NUM
cana-4790	284	4	,	,	PUNCT
cana-4790	284	5	35–44	35–44	NUM
cana-4790	284	6	.	.	PUNCT