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Education, Language and Sociology Research 
ISSN 2690-3644 (Print) ISSN 2690-3652 (Online) 

Vol. 1, No. 1, 2020 
www.scholink.org/ojs/index.php/elsr 

61 
 

Original Paper 

Psychophysiological Characteristics of Children with Dyslexia 

Pop-Jordanova N1*, Markovska-Simoska S1, Loleska S2 & Loleski M.3 

1Macedonian Academy of Sciences and Arts, Skopje, North Macedonia 
2 Public Health, Medical Faculty, University “Ss. Cyril and Methodius”, Skopje, North Macedonia  
3 Forensics Department, Skopje, North Macedonia 
* Pop-Jordanova N, Macedonian Academy of Sciences and Arts, Skopje, North Macedonia 

 

Received: April 30, 2020       Accepted: May 7, 2020      Online Published: May 12, 2020 

doi:10.22158/elsr.v1n1p61         URL: http://dx.doi.org/10.22158/elsr.v1n1p61 

 

Abstract 

Dyslexia is a specific learning disorder that involves difficulty reading due to decoding problems for 

letters and words. Statistics shows that 5-10% of the general population has dyslexia. The aetiology of 

reading disorder supposes some biological causes and morphological markers useful in the 

classification and early identification of the problem. 

The aim of this article is to find appropriate parameters, which will be useful for early diagnosis and 

finding the right modalities for treatment. 

Our findings about QEEG characteristics are not conclusive. However, slowing of brain activity in 

dyslexic children appeared to be confirmed. These findings lead to the possible hypothesis of delay in 

neurological development of these children.  

Significant theta/beta ratio suggest possible comorbidity with ADHD. 

Further research with more children included is proposed. 

Keywords 

dyslexia, children, QEEG, Neurogame 

 

1. Introduction 

Dyslexia is a specific learning disorder that involves difficulty reading due to decoding problems for 

letters and words. In the fifth revision of DSM (2013) the entity “Learning disorders” was changed in 

“Specific learning disorder” including Dyslexia, Dyscalculia and Disorder of Written expression. For 

exact diagnosis of this entity some core criteria must be fulfilled: the difficulty must persist at least 6 

months and failed to improve despite intervention made; it must affects academic skills below those 

which are age-related and expected; the start of the problem must be in school age; other disorder like 



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intellectual disabilities, auditory and visual problems, as well as other neurological disorder must be 

excluded. 

Statistics shows that 5-10% of the general population has dyslexia, but this number in some region can 

be as high as 17%. More precisely, the real incidence varies widely by country. For example, Italy 

registered only a half of the incidence found in the United States, where an estimated 5 to 15 percent of 

the population may have dyslexia to some degree. Scientists supposed that the difference of the 

incidence depends on the complexity in the language used. For example, English alphabet consists of 

44 different sounds which can be differently written and pronounced. Having trouble differentiating 

sounds (phonemes) people have problems in orthography. However, this condition was not understood 

worldwide until the late 20th century even today.  

For the experiences in developmental neuropsychology, the acquisition of reading involves two systems: 

the lexical system (sight-reading) which process familial words, and phonological system, which 

comprises decoding unfamiliar words. Awareness of phonological structure require knowledge of the 

correspondence letter-sound. Developmental analysis can facilitate understanding of how reading 

disabled children compensate their problem. Developmental classification of reading and spelling 

difficulties clarify the stage where these academic skills are not completed. In this context, reading 

difficulties are manifested in the stage when advancing from early phase of acquisition, where reading is 

visually based (logographic), to the alphabetic phase, where letter-sound association are used. In the 

logographic stage, child lacks strategies to decode unknown words other than by visual approximation to 

known words. During alphabetic stage the child uses phoneme-grapheme to sound out words, and decode 

them from the left to right depending of the consistency between letters and sounds. The logographic 

stage involves instant recognition and have difficulty with nonwords, for which reason the spelling tends 

to be dysphonetic. The third phase established as orthographic, where features are automatic and flexible; 

in other words, it is needed the instant analysis into orthographic units (morphemes) without initial 

phonological conversion (Harris, 1998).  

The aetiology of reading disorder supposes some biological causes and morphological markers useful in 

the classification and early identification of the problem. Post mortem studies confirmed some 

abnormalities in perinatal brain anatomy and physiology, as well as some neurocortical deficits that lead 

to disruption of cognitive processing. In some cases, cell migration abnormalities and number on 

chromosome 15 has been identified. Transgenerational appearance of dyslexia in some families supports 

possible genetic basis of transmission, but exact findings are not yet published. However, several 

candidate genes for dyslexia susceptibility (e.g., ROBO1, DCDC2, DYX1C1, KIAA0319) have been 

suggested, and all of these to play an important role in the brain development (Galaburda, LoTurco, 

Ramus, Fitch, & Rosen, 2006; Hannula-Jouppi, Kaminen-Ahola, Taipale, Eklund, Nopola-Hemmi, 

Kaariainen, & Kere, 2005; Meng, Hager, Held, Page, Olson, Pennington, & Gruen, 2005; Skiba, Landi, 

Wagner, & Grigorenko, 2011). 



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Magnetic Resonance Imaging (MRI) studies confirmed some asymmetry of the brain, using planum 

temporale as a marker (Rumsey, Dorwart, Vermess, Denckla, Krussi, & Rapaport, 1986). In newest 

studies, MRI in children and adults with dyslexia commonly demonstrate hypoactivation in 

left-hemispheric temporo-parietal, occipital-temporal, and inferior frontal networks. Further, reduced 

functional connectivity among these regions has also been demonstrated. Additionally, PET scan studies 

confirmed abnormalities in cerebral blood flow in the left temporoparietal region (Rumsey, 1992). An 

autoimmune aetiology has been proposed by Galaburda et al. (1993): some ischemic injury to the 

developing cortex produced by autoimmune damage to the wall of the arterial blood vessels supplying 

involved brain regions might results in scars and malformations and resulting as dyslexia (Galaburda & 

Livingstone, 1993). Having non-significant markers for dyslexia, EEG recording as a simple, 

cost-benefit method was largely used worldwide. The slowing of EEG is the most frequent abnormality 

in children with learning disabilities. In a review paper, Chabot et al. (Chabot, di Michele, Prichep, & 

John, 2001) showed poor EEG rhythm, low-voltage background rhythms (Hughes, 1978) and increased 

generalized slowing (Byring & Jarvilehto, 1985). Abnormalities in children with learning disorders 

included increased high amplitude atypical alpha, abnormal focal paroxysmal activity, excess focal delta, 

persistent delta asymmetry, and excessive EEG response to hyperventilation. EEG studies indicate that 

specific developmental disorders are associated with abnormal EEGs in 25% to 43.5% of these children. 

Neuropsychological testing is also used to establish the underlying characteristics of reading disorder. 

Having in mind that reading is a complex function of the nervous system, which require integration of 

visual and auditory processes, both central and peripheral, Boder (Boder & Jarrico, 1982; Boder, 1973) 

differentiated the following subtypes of dyslexia: dysphonic, dyseidetic, mixed and non-specific reading 

delay. For exact differentiation of the type, Bindelli and Chiarenza developed computerized Direct Test 

of Reading and Spelling (DTRS) for Italian language, which is a modification of original Boder test 

(Chabot, di Michele, Prichep, & John, 2001). 

Dyslexia is a school problem and it frequently disappears in the adulthood. Given what we know now, many 

famous people may have had dyslexia, including Leonardo da Vinci, Saint Teresa, Napoleon, Winston 

Churchill, Carl Jung, Albert Einstein, and Thomas Edison, as well as Steven Spielberg, Muhammad Ali, Keira 

Knightley, Danny Glover and other in a new time. There are evidence-based treatments which are effective, 

even for adults with the condition (like logopaedic exercises, biofeedback, etc.). 

Comorbidity with attention deficit hyperactivity disorder, anxiety and depression, disruptive problems 

with impulse-control, conduct disorder, and autism spectrum disorders is frequently found with learning 

disabilities, especially dyslexia (Hendren, Haft, Black, White, & Hoeft, 2018). 

In Macedonian speaking population dyslexia has not been exactly diagnosed until the two last decades. 

Macedonian language comprises letter for every sound and it seems not to be so difficult for reading and 

writing. But, in the last time, school teachers started to differentiate a group of children with difficulty in 

reading and writing which not corresponded with the chronological age expectances. In this context, 

psychologists as well as special educators started with the diagnostics and treatment of these children. 



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Unfortunately, in our country the special test for dyslexia does not exists. The most used test is 

Macedonian translation of test developed by Kostic, Vladisavljevic and Popovic (1983) (Kostic, 

Vladisavljevic, Popovic, & Cudov, 1983). Some experiences in the assessment of children with dyslexia, 

dysgraphia and dyscalculia in our context, were published by a group of researchers from the Institute for 

Special Education, UKIM, Skopje in 2018 (Karovska-Ristovska, Kardaleska, Ajdinski, & Shurbanovska, 

2018). 

For this reason, and the scarce of data for dyslexia in our country, the aim of this research is to find 

appropriate parameters, which will be useful for early diagnosis and finding the right modalities for 

treatment. Such parameters would be possible specific abnormalities in EEG recordings, as well as the 

performances of these children tested with own modality we named as “Neurogame” which helps to 

evaluate concentration, focus attention and reaction time to some tasks. As far as we know, this is the first 

study that evaluates this issue in our country. 

 

2. Method 

2.1 Sample 

We selected randomly 10 children diagnosed as dyslexic according to ICD-10 and DSM-V criteria, 

referred by the Institute for child mental health in Skopje. The diagnostic was made from the team 

consisted of logopaedist, psychologist, child neurologist, paediatrician as well as child psychiatrist. 

Mean age of boys was 10.2±1.64 years and mean age of girls was 9.2±1.60 years. The written consent 

was obtained from parents. In the moment of evaluation children were in good health and without any 

medication 48 hours before recording. 

2.2 Evaluation 

EEG was recorded using a Mitsar 201 (www.mitsar-medical.com), a PC-controlled 19-channel 

electroencephalographic system with 19 electrodes, placed according to the international 10-20 system, 

referenced to linked ears (on the International 10-20 system) with 250 Hz sampling rate in 0.5-50 Hz 

frequency range in the following conditions: Eyes opened (EO)—5 minutes, and Eyes closed (EC)—5 

minutes as well as stimuli presentation protocol (Visual Continuous Performance test—VCPT). The 

obtained data from VCPT, are not aimed for analysis in this paper and this data will be analysed in 

another paper. 

The same equipment and procedures were used for children with dyslexia and controls. Subjects were 

tested in a quiet air-conditioned room with the experimenter and recording equipment present. During 

fitting of the electrodes, subjects were familiarized with the testing equipment and the procedure. 

Vertical Electro-Oculogram (VEOG) was recorded with 2 tin electrodes placed 1 cm above and 1 cm 

below the right eye. Eye-blink artifacts were corrected by zeroing the activation curves of individual 

ICA component score responding to eye blinks. In addition, epochs of the filtered 

electroencephalogram with excessive amplitude (>100 μV) and/or excessively fast (>35 μV in 20-35 

Hz band) and slow (>50 μV in 0-1 Hz band) frequency activities were automatically marked and 



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excluded from further analysis. Finally, EEG was manually inspected to verify artifact removal. 

Spectral analysis of relative power using fast Fourier transform was carried out for the four frequency 

bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-20 Hz). Relative power is 

represented by the percentage of the amplitude in a given frequency band compared with the total 

amplitude across all frequency bands. Also, we calculated the ratio between theta and beta absolute 

power in order to obtain the theta-beta ratio (TBR) at Cz.  

In some article it was published that asymmetric feature for QEEG recording was typical for dyslexic 

children. Asymmetry is defined as a functional difference between the left and right hemispheres 

measured for relative power which exists between the homologous electrodes located on both 

hemispheres. It was calculated using the following equation: 

Power (Left) – Power (Right) / Power (Left) + Power (Right) 

	
	 	

 

where Power (Left) corresponds to the relative power of the electrode located on the left hemisphere, 

and Power (Right) to the relative power on the right hemisphere. These asymmetry data were 

statistically analysed. 

Before the QEEG recording, “Neurogame” was applied. Our original developed application on Android 

operating system, named “Neurogame” is based on an open source platform to enable assessment the 

focus and concentration, as well as reaction time, with the ability to monitor the progress of the results 

over a period of time. The testing for all clients was performed in the morning period 8-12 am (Hughes, 

1978). The complete evaluation of children takes around 2 hours.  

2.3 Data Analysis 

The Statistica StatSoft software was used to assess group differences. One-way analysis of variance 

(ANOVA) was carried out on relative EEG power for each band (delta, theta, alpha beta) in eyes-open 

condition in 5 regions (frontal [F]: (F3, F4, F7, F8); central [C]: (C3, Cz, C4); temporal [T]: (T3, T4, 

T5, T6), parietal [P]: (P3, Pz, P4) and occipital [O]: (O1,O2) regions. Additionally, we include and 

electrodes above Broca’s area (F3, F7 and C3) and Wernicke’s area (T3, T5 and P3). Group (dyslexia 

and control) was the between-subject factor. For some estimations because of the small sample size, the 

non-parametric Mann-Whitney U test was used to lower variability in the groups. The level of 

significance was set at p < .05. 

 

3. Results 

As mentioned before, the evaluated sample is small, consisting of 10 children, where mean age of boys 

was 10.2±1.64 years and mean age of girls was 9.2±1.60 years. The results are compared with matched 

control group consisting of 10 children with normo-typical development without any learning problems 

or neurodevelopmental delay. Mean age of boys in the control group was 10.4±1.84 years and mean 



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age of girls 9.8±1.30 years. They are paired with the examined group according to the age and gender 

without any significant difference (Current effect: F (1, 18) = .19240, p = .66615). 

Relative EEG power was estimated in eyes open condition, because in eyes closed condition alpha 

power usually prevails the other frequencies. 

Results obtained for Delta and Theta waves in frontal, central and temporal position are presented on 

Figure 1. As can be seen central, temporal, parietal and occipital slow waves are significantly greater in 

dyslexic children in comparison to control group (see graphs for p values). 

 
Group; LS Means

Wilks lambda=.68796, F(4, 15)=1.7009, p=.20208
Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 F3 Delta
 F4 Delta
 F7 Delta
F8 Delta

Dyslexia Control group

Group

0

50

100

150

200

250

300

Group; LS Means
Wilks lambda=.45895, F(4, 15)=4.4208, p=.01470

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 F3 Theta
 F4 Theta
 F7 Theta
 F8 Theta

Dyslexia Control group

Group

15

20

25

30

35

40

45

50

  
Group; LS Means

Wilks lambda=.56551, F(3, 16)=4.0977, p=.02459
Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 C3 Delta
 Cz Delta
C4 Delta

Dyslexia Control group

Group

20

40

60

80

100

120

140

160

180

200

Group; LS Means
Wilks lambda=.86437, F(3, 16)=.83686, p=.49323

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 C3 Theta
 Cz Theta
 C4 Theta

Dyslexia Control group

Group

20

25

30

35

40

45

50

55

60

Group; LS Means
Wilks lambda=.45356, F(4, 15)=4.5180, p=.01356

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 T3 Delta
 T4 Delta
 T5 Delta
T6 Delta

Dyslexia Control group

Group

20

40

60

80

100

120

140

160

180

200

220

Group; LS Means
Wilks lambda=.81741, F(4, 15)=.83765, p=.52233

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 T3 Theta
 T4 Theta
 T5 Theta
 T6 Theta

Dyslexia Control group

Group

15

20

25

30

35

40

45

50



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Group; LS Means
Wilks lambda=.60939, F(3, 16)=3.4186, p=.04290

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

Delta P3
 Delta Pz
Delta P4

Dyslexia Control group

Group

20

40

60

80

100

120

140

160

180

200

220

Group; LS Means
Wilks lambda=.86695, F(3, 16)=.81850, p=.50242

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 Theta P3
 Theta Pz
 Theta P4

Dyslexia Control group

Group

24

26

28

30

32

34

36

38

40

42

44

46

Group; LS Means
Wilks lambda=.65220, F(2, 17)=4.5328, p=.02644

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 Delta O1
 Delta O2

Dyslexia Control group

Group

60

80

100

120

140

160

180

200

220

240

260

Group; LS Means
Wilks lambda=.49892, F(2, 17)=8.5368, p=.00271

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 Theta O1
 Theta O2

Dyslexia Control group

Group

20

25

30

35

40

45

50

55

 

Figure 1. Relative Power for Delta and Theta in Eyes Open Condition for Compared Groups in 

Frontal, Central, Temporal, Parietal and Occipital Positions 

 

For alpha (frontal, central and temporal) and beta we obtained almost the same results for both groups 

without any significance (Figure 2). 

 
Group; LS Means

Wilks lambda=.65914, F(4, 15)=1.9392, p=.15609
Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 F3 Alpha
 F4 Alpha
 F7 Alpha
 F8 Alpha

Dyslexia Control group

Group

10

12

14

16

18

20

22

24

26

28

30

32

 

Group; LS Means
Wilks lambda=.75501, F(4, 15)=1.2169, p=.34479

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 F3 Beta
 F4 Beta
 F7 Beta
 F8 Beta

Dyslexia Control group

Group

8

10

12

14

16

18

20

22

24

 



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Group; LS Means
Wilks lambda=.91925, F(3, 16)=.46852, p=.70836

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 C3 Alpha
 Cz Alpha
 C4 Alpha

Dyslexia Control group

Group

10

15

20

25

30

35

40

45

50

Group; LS Means
Wilks lambda=.62919, F(3, 16)=3.1432, p=.05431

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 C3 Beta
 Cz Beta
 C4 Beta

Dyslexia Control group

Group

4

6

8

10

12

14

16

18

20

 
Group; LS Means

Wilks lambda=.89210, F(4, 15)=.45357, p=.76842
Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 T3 Alpha
 T4 Alpha
 T5 Alpha
 T6 Alpha

Dyslexia Control group

Group

5

10

15

20

25

30

35

40

Group; LS Means
Wilks lambda=.79533, F(4, 15)=.96503, p=.45508

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 T3 Beta
 T4 Beta
 T5 Beta
 T6 Beta

Dyslexia Control group

Group

6

8

10

12

14

16

18

20

22

24

26

28

 
Group; LS Means

Wilks lambda=.98935, F(3, 16)=.05741, p=.98128
Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 Alpha P3
 Alpha Pz
 Alpha P4

Dyslexia Control group

Group

15

20

25

30

35

40

45

50

Group; LS Means

Wilks lambda=.85304, F(3, 16)=.91884, p=.45405

Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 Beta P3
 Beta Pz
 Beta P4

Dyslexia Control group

Group

6

8

10

12

14

16

18

20

 

Group; LS Means
Wilks lambda=.69916, F(2, 17)=3.6574, p=.04774

Effective hypothesis decomposition
Vertical bars denote 0.95 confidence intervals

 Alpha O1
 Alpha O2

Dyslexia Control group

Group

10

15

20

25

30

35

40

Group; LS Means

Wilks lambda=.60977, F(2, 17)=5.4396, p=.01493

Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

 Beta O1
 Beta O2

Dyslexia Control group

Group

6

8

10

12

14

16

18

20

22

 

Figure 2. Comparison of Alpha and Beta Power between Dyslexic and Normal Control Groups 

 

 



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Figure 6. Nonparametric Mann-Whitney U Test for Delta and Theta Waves in Broca’s and 

Wernicke’s Areas 

 

From Figure 6 it is clear that significant differences in dyslexic group was found for delta and theta 

waves in Broca’s area, bur only for delta in Wernicke’s area.  

Additionally, we have calculated theta/beta ratio for dyslexic and control group of children. Obtained 

results showed that there is significant higher theta/beta ratio in the group of dyslexic children in 

comparison to control group p < 0.05 (Figure 7). Following previous experience, these corresponds to 

ADHD marker even in dyslexic group. 

 
Group; LS Means

Current effect: F(1, 18)=4.5721, p=.04646
Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

Dyslexia Control group

Group

1

2

3

4

5

6

7

8

9

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Figure 7. Theta/Beta Ratio in Dyslexic and Control Group 

 

Topographic maps of brain waves for both evaluated groups are presented in Figure 8. 

 



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Children with dyslexia                        Control group 

Figure 8. Topographic Maps of Brain Waves for Dyslexic Versus Control Group 

  

Topographic maps confirmed that in dyslexic children (left) there is more theta and alpha and less beta 

compared to children with normal typical development (right). 

Children with dyslexia have higher theta of 6.35Hz frontally and in temporal areas of 7.57Hz. There is 

less alpha (10Hz) and beta (14.88Hz). Also, the alpha as a sign of maturation is not well differentiated 

from theta in dyslexic group. 

Results for graphs of EEG power spectra are presented on Figure 9. 

 

 

dyslexia                              control group                

Figure 9. Spectra Power Comparison 

 



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Figure 10. Topographic Maps for Asymmetry  

(Left-Group with dyslexia; Right-Control group) 

 

Even without statistical difference for asymmetry, the topographic maps show that there is more 

asymmetry between hemispheres for dyslexic group compared to control group, especially for temporal 

and parietal areas. 

The testing with “Neurogame” was applied in children with dyslexia and results were compared with 

sample of school healthy children (Table 2) 

 

 

 

 

 



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Table 2. Descriptive Statistics Dyslexia vs. Healthy 

Variable Mean Minimum Maximum Stand. Dev 

t T 182 141 214 20.07 healthy 

128 0 336 102.77 dyslexic 

t H 47.16 8 80 26.99 healthy 

14.4 0 39 15.5 dyslexic 

t M 134.83 94 189 26.93 healthy 

113.66 0 299 91.412 dyslexic 

t h 328.33 189 422 78.88 healthy 

329.88 0 578 207.64 dyslexic 

 

t T = max. tries; t H = max. hits; t M = max misses; t h = max time). 

Calculated Student t-test for parameters between groups is presented on Figure 11. It is clear that only 

time for maximum hits is statistically significant; all other parameters are similar for both groups. 

 

T-test for Independent Samples (Dyslexia vs Healthy)
Note: Variables were treated as independent samples

Group 1  vs. Group 2
Mean

Group 1
Mean

Group 2
t-value df p Std.Dev.

Group 1
Std.Dev.
Group 2

F-ratio
Variances

p
Variances

t T vs. t T
t H vs. t H
t M vs. t M
t h vs. t h

128.1111 182.0000 -1.7862 19 0.090035 102.7782 20.07260 26.2177 0.000008
14.4444 47.1667 -3.2447 19 0.004264 15.5009 26.99439 3.0327 0.126178

113.6667 134.8333 -0.7649 19 0.453750 91.4166 26.93033 11.5230 0.000468
329.8889 328.3333 0.0239 19 0.981169 207.6454 78.88926 6.9280 0.004460

Figure 11. T-test for Independent Samples (Dyslexia vs Healthy) 

 

Generally, results obtained with “Neurogame” are similar for dyslexic children and matched control 

healthy school children. 

 

4. Discussion 

A wide range of research has investigated what people understand about dyslexia. In this context, 

electrophysiological measures of brain function are used as effective tools to understand neurocognitive 

phenomena and as sensitive indicators of pathophysiological processes of this disorder. 

The aim of the present study was to examine group differences in spontaneous oscillatory brain activity 

during a resting (eyes opened) condition. EEG power was examined across all frequency bands in 

children with dyslexia and contrasted to neurotypical children. 

As presented, our group of dyslexic children did not differ significantly for results obtained by QEEG. It 

is obvious that the only difference is general slowing in brain activity and significantly higher theta/beta 

ratio found in dyslexic group.  



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Long time ago, the QEEG parameters were used to differentiated between children with learning 

Disorders (LD) and those with good academic achievement (Hendren, Haft, Black, White, & Hoeft, 2018; 

Harmony et al., 1990; Jäncke & Alahmadi, 2016; John, Prichep, Ahn, Easton, Fridman, & Kaye, 1983; 

Karovska-Ristovska, Kardaleska, Ajdinski, & Shurbanovska, 2018). LD children are characterized by 

more power in the theta band and less amount of power in the range of alpha frequencies (Kostic, 

Vladisavljevic, Popovic, & Cudov, 1983; Knight, 2018; Loleski, Loleskа, & Pop-Jordanova, 2017; 

Lubar, Bianchini, Calhoun, Lambert, Brody, & Shabsin, 1985; Marosi, 1992; Marosi, 1997; Marosi et al., 

1992; Meng, Hager, Held, Page, Olson, Pennington, & Gruen, 2005). Even increases in power in the 

delta band have also been observed in cases with severe difficulties (Harmony et al., 1990).  

Furthermore, Jäncke and Alahmadi (2016) (Jäncke & Alahmadi, 2016), showed significant QEEG 

differences between children with LD-NOS (not otherwise specified), those with learning disabilities 

with verbal disabilities (LD-Verbal), and healthy controls. The features were selected by using a group 

independent component analysis (gICA) model. Finally, in the study by Roca-Stappung et al. (2017) 

(Turker, Reiterer, Preisler, & Schneider, 2017) it was shown that QEEG parameters differed between 

subtypes of LD-NOS in which group dyslexic children belong. 

Bosch-Bayard J. and all (Bosch-Bayard et al., 2018; Bosch-Bayard, Peluso, Galan, Valdes Sosa, & 

Chiarenza, 2018) applied specific methodology to find an optimal predictor of LD-NOS disability 

severity based on a reduced set of QEEG variables that may be of use in real world screening settings. 

They divided nonspecific learning disabilities in three subgroups each with EEG characteristics related to 

the cognitive scores. 

In a very large and significant study of Bosch-Bayard J. et all. Chiarenza (Bosch-Bayard, Peluso, Galan, 

Valdes Sosa, & Chiarenza, 2018), children with dysphonetic dyslexia showed significant excess in delta 

band in the middle line (Fz, Cz and Pz), as well as Fp2 and the occipital leads bilaterally (O1 and O2). A 

significant excess in high theta (6-7.5 Hz) and low alpha (7.5-8.5 Hz) bands was also present in the Fz, 

Cz and Fp2. Fz, Cz and Pz also showed significant excess of activity in the dyslexic group. However, a 

significant reduction of high alpha (11-12.5 Hz) activity was present in the dyslexic group bilaterally in 

F3, C3, C4 and in P3 but more pronounced in the left leads. Additionally, significant reduction was also 

present in the left leads F7, F3, C3, P3 and T5. 

Having in mind the work of Broca and Wernicke and more recently those of language production as well 

as specific aspects of syntactic processing which are primarily localized in the left hemisphere, the left 

hemispheric localisation of abnormalities are expected. Papagiannopoulou and Lagopoulos 

(Papagiannopoulou & Lagopoulos, 2016) find a presence of an atypical linguistic network in dyslexic 

children where dominance of theta activity in the left frontal region implicated delayed maturation and 

abnormal hypoarousal mechanisms.  

The results of these analyses revealed significantly increased theta power for the dyslexia group (when 

compared to controls) in frontal brain regions, the scalp topography corresponding to Broca’s area and 

greater theta power in the left hemisphere. Children with dyslexia also had significantly increased slow 



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wave activity (for both delta and theta), in Broca’s compared to Wernicke’s area, which was in direct 

contrast to the control children who did not exhibit any asymmetry across these two areas. Children with 

dyslexia also had significantly decreased EEG power on the left for alpha2 and beta frequency bands, but 

had significantly increased EEG power in the left hemisphere for the theta band. 

Our analysis for asymmetry of relative power did not confirmed any hemispheric asymmetry in dyslexic 

children. 

Oscillatory dynamics of brain activity during processing of words and non-words was analysed thought 

analysis of EEG signals. Starting from the knowledge that reading is a complex cognitive skill sub served 

by a distributed network of visual and language-related regions, Žarić G. et al. (Žarić, 2017) investigated 

whether directed connectivity during reading scales with the level of dysfluency in dyslexic children 

exist. Obtained results of this study suggest disrupted visual processing of words in both dyslexic groups, 

together with a compensatory recruitment of right posterior brain regions. Functional connectivity in the 

brain’s reading network may thus depend on the level of reading dysfluency beyond group differences 

between dyslexic and typical readers. 

The finding that the functional connectivity pattern in dyslexic children is related to their reading level, 

may in part explain the mixed results obtained in previous functional connectivity studies of dyslexia. 

Recent research has shown that the morphology of certain brain regions may correlate with a number of 

cognitive skills such as musicality or language ability. In a study of Turker S. et al. (Skiba, Landi, 

Wagner, & Grigorenko, 2011) the aim was to explore the extent to which foreign language aptitude, in 

particular phonetic coding ability, is influenced by the morphology of Heschl’s gyrus (auditory cortex), 

working memory capacity, and musical ability. Study showed the importance of the right hemisphere for 

language processing, especially when linked or common resources are involved, such as the 

inter-dependency between phonetic and musical aptitude. Although there is no doubt that numerous 

external variables influence the development of language and musical skills, authors of this study 

supported the claim that there are strong innate and/or prenatally determined neurological factors that 

remain to be uncovered in the next decades. 

Given that reported rates range for dyslexia are from of 4% to 20%, it is of great importance that teachers 

have an accurate understanding of what dyslexia is and how it effects their students. In the study of 

Cathryn Knight (Knight, 2018) it was demonstrated that teachers held only a basic understanding of 

dyslexia, based on the behavioral issues that it is associated with. Teachers lacked the knowledge of the 

biological (i.e., neurological) and cognitive (i.e., processing) aspects of dyslexia. Additionally, teacher 

training, which informs teachers of the up-to-date research on the biological, cognitive, and behavioral 

aspects of dyslexia, is essential to combat misconceptions and ensure that teachers have more nuanced 

and informed understandings of dyslexia. 

There is emerging evidence that neuroimaging measures, combined with key behavioral measures, can 

enhance the accuracy of identification of dyslexia risk in prereading children but its sensitivity, 

specificity, and cost-efficiency is still unclear. Early identification of dyslexia risk carries important 



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implications for dyslexia remediation and the amelioration of the psychosocial consequences commonly 

associated with reading failure. 

Structural MRI studies have demonstrated atypicality within the dorsal and ventral reading networks and 

functional MRI studies have shown reduced neural processing of phonological, rapid auditory, and 

orthographic information in these networks. 

Despite the progress in studies for reading difficulties we are still far from having reliable biomarkers of 

dyslexia. Comorbidity make dyslexia very difficult for many treatment procedures. In our study, we also 

confirmed possible comorbidity with ADHD obtaining significantly higher theta/beta ratio in a group 

diagnosed as dyslexia.  

 

5. Conclusion 

Studies devoted to dyslexia are enormous and gives different interpretation about the aetiology. Not yet 

uniform conclusions about specific neurophysiological aspect exist.  

Our finding about QEEG characteristics are not conclusive. However, slowing of brain activity in 

dyslexic children appeared to be confirmed. This finding lead to the possible hypothesis of delay in 

neurological development of these children.  

Significant theta/beta ratio suggest comorbidity with ADHD. 

The small sample size that we have evaluated must stimulate further research with more sophisticated 

analyses of the results. 

 

Acknowledgements 

Great thanks to prof. Giuseppe Chiarenza, president of the International Centre for Learning, Attention 

and Hyperactivity Disorders (CIDAAI) who stimulate our concern about dyslexia in our country, as well 

as to support the construction of modality we named “Neurogame”.  

 

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