id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cana-4790	Sharyu Ikhar	Learning Spatial and Temporal EEG Patterns for Alzheimer’s Disease Detection with CNN-LSTM Networks	2024	15	.pdf	application/pdf	5874	327	57	Both designs have their own advantages, but EEG signals inherently display temporal (i.e., signal changes over time) and spatial (i.e., channel interactions) characteristics. Most of the time, these models were taught using custom features made by hand, like spectral power, entropy measures, and wavelet coefficients from EEG signals [5, 6].	cache/cana-4790.pdf	txt/cana-4790.txt