Support Vector Machine with Theta-Beta Band Power Features Generated from Writing of Dyslexic Children

Mahmoodin, Z.; Lee, Khuan Y.; Mansor, W.; Zainuddin, A. Z. Ahmad

doi:10.30880/ijie.2019.11.03.005

Cited by 6 publications

(7 citation statements)

References 15 publications

(15 reference statements)

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“…At the same time, the oversynchronization of beta range oscillations could be a compensatory mechanism to improve the processing of phonemic rate information. Although different methodologies and age ranges, we also found numerous abnormalities in alpha and beta frequencies (Duffy et al, 1980a;Dushanova & Tsokov, 2021;Flynn et al, 1992;Galin et al, 1988Galin et al, , 1992Haynes et al, 1989;Zulkifli Mahmoodin et al, 2019;Ortiz et al, 1992;Rippon & Brunswick, 2000;Spironelli et al, 2008;Taskov & Dushanova, 2021). Furthermore, alpha appears globally reduced in at rest conditions, whereas beta offers more contradictory results.…”

Section: Discussionmentioning

confidence: 66%

“…Some authors found differences in both conditions, but more pronounced in the task condition (Duffy et al 1992) or the rest condition (Leisman, 2002), whereas others found differences only during the task condition (Flynn et al, 1992;Galin et al, 1988;Ortiz et al, 1992). Finally, some studies did not clearly report what happened during the at-rest condition (Duffy et al 1980b;Flynn & Deering, 1989b;Galin et al, 1992;Mahmoodin et al, 2016Mahmoodin et al, , 2019Zainuddin et al, 2018).…”

Section: Eeg During a Taskmentioning

confidence: 99%

“…The methodology used most frequently is spectral analysis, which shows the spectral content in the different frequency bands (delta,theta,(4)(5)(6)(7)alpha,(8)(9)(10)(11)(12)beta,gamma,> 40 Hz). The methodology has been used alone (Arns et al, 2007;Bruni et al, 2009;Colon et al, 1979;Fein et al, 1983Fein et al, , 1986Galin et al, 1988Galin et al, , 1992Harmony et al, 1990;Mahmoodin et al, 2016Mahmoodin et al, , 2019Papagiannopoulou & Lagopoulos, 2016;Remschmidt & Warnke, 1992;Rippon and Brunswick 2000) or combined with other methods (Babiloni et al, 2012;Bosch-Bayard et al, 2018;Flynn & Deering, 1989a;Flynn et al, 1992;Fraga González et al, 2016;Leisman, 2002;Reda et al, 2021;Xue et al, 2020).…”

Section: Spectral Analysis Of the Rs-eegmentioning

confidence: 99%

“…4 The figure shows the results obtained in the studies using the spectral analysis of RS-EEG. In red the increases and blue the decreases obtained in children with dyslexia compared to controls; in black, if no differences were reported between the groups No correlation between connectivity (minimum spanning tree) and reading performance Harmony et al (1990) Reading (reading comprehension and oral reading) and writing (copying, dictation, and functional writing) + theta in almost all leads in children with minor difficulties, no antecedents, and good socioeconomic status + delta in left frontal and temporal areas (F3, F7, and T3) in children with a poor or very poor evaluation Duffy et al, 1980aDuffy et al, , 1980bFlynn et al, 1992;Galin et al, 1992;Haynes et al, 1989;Mahmoodin et al, 2019;Martinez-Murcia et al, 2020;Mattson et al, 1992). The results are not comparable, given the different tasks and EEG analysis methodologies, but the differences between DD and control children appear mainly localized in the left temporoparietal sites.…”

Section: Eeg During a Taskmentioning

confidence: 99%

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EEG correlates of developmental dyslexia: a systematic review

et al. 2022

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Dyslexia is one of the most studied learning disorders. Despite this, its biological basis and main causes are still not fully understood. Electroencephalography (EEG) could be a powerful tool in identifying the underlying mechanisms, but knowledge of the EEG correlates of developmental dyslexia (DD) remains elusive. We aimed to systematically review the evidence on EEG correlates of DD and establish their quality. In July 2021, we carried out an online search of the PubMed and Scopus databases to identify published articles on EEG correlates in children with dyslexia aged 6 to 12 years without comorbidities. We follow the PRISMA guidelines and assess the quality using the Appraisal Tool questionnaire. Our final analysis included 49 studies (14% high quality, 63% medium, 20% low, and 2% very low). Studies differed greatly in methodology, making a summary of their results challenging. However, some points came to light. Even at rest, children with dyslexia and children in the control group exhibited differences in several EEG measures, particularly in theta and alpha frequencies; these frequencies appear to be associated with learning performance. During reading-related tasks, the differences between dyslexic and control children seem more localized in the left temporoparietal sites. The EEG activity of children with dyslexia and children in the control group differed in many aspects, both at rest and during reading-related tasks. Our data are compatible with neuroimaging studies in the same diagnostic group and expand the literature by offering new insights into functional significance.

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Section: Discussionmentioning

confidence: 66%

Section: Eeg During a Taskmentioning

confidence: 99%

Section: Spectral Analysis Of the Rs-eegmentioning

confidence: 99%

Section: Eeg During a Taskmentioning

confidence: 99%

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EEG correlates of developmental dyslexia: a systematic review

et al. 2022

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“…Machine learning is also applicable to dyslexia and other types of learning disabilities, as well. This shows the breadth and promise of these technologies in educational diagnostics and clinical evaluation [25].…”

mentioning

confidence: 93%

Deep Learning-Based Classification of Dysgraphia Severity in Children from Handwriting Images

Anand,

Sharma,

Sharma

et al. 2023

Preprint

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This study focused on the design and testing of a deep learning model which could be used to distinguish between 'Low Potential Dysgraphia' and 'High Potential Dysgraphia', based upon using these handwriting image samples for children at school age. One of the methodologies was to create a Convolutional Neural Network (CNN) by using augmented samples of handwriting image data for training. These samples were divided into separate training and testing batches. All the handwriting samples were resized to 150x150 pixels and pre-processed. The CNN was made up of different convolutional and max-pooling layers, with dropout regularization included. At the end came a dense layer with sigmoid activation for binary classification. Training lasted for 10 epochs, with an emphasis placed on tracking accuracy at both the training and testing phases. Testing accuracy was about 84%; the training score also increased steadily, so the model is learning well without overfitting because its testing and training accuracies are similar. The model's performance in identifying 'High Potential Dysgraphia', was reflected by a precision of 0.76, a recall rate of 0.96, and an F1-score (the geometric mean between them)of respectively: On the contrary, 'Low Potential Dysgraphia' was identified, with a precision of 0.95 and falling recall at 0.74 giving an F1-score of 83 %.

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