Comparison between characteristics of EEG signal generated from dyslexic and normal children

Fadzal, C. W. N. F. Che Wan; Mansor, W.; Lee, Khuan Y.; Mohamad, Suraya; Mohamad, N. B.; Amirin, S.

doi:10.1109/iecbes.2012.6498210

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…As previously stated, different studies have been performed to correlate EEG data with reading and mathematical learning difficulties. Several reports have shown increasing power in delta ( Arns et al, 2011 ), theta ( Arns et al, 2011 ; Soltanlou et al, 2019 ), alpha and beta bands ( Che Wan Fadzal et al, 2012a , b ). Similarly, an increment in power ratios from theta/alpha ( Clarke et al, 2002 ; Jäncke and Alahmadi, 2016 ; Pavithran et al, 2019 ; Nassehi et al, 2020 ), and theta/beta ( Clarke et al, 2002 ; Jäncke and Alahmadi, 2016 ) was obtained in children with learning difficulties.…”

Section: Discussionmentioning

confidence: 99%

“…These findings agree with Nora et al (2021) , who suggested that there are right hemispheric pathways that could compensate neural impairments in the processing of reading, despite the extensive literature supporting the role of the left hemisphere in language processing. However, higher alpha and beta power have been identified in dyslexic children over centroparietal cortex, during a writing task ( Che Wan Fadzal et al, 2012a , b ). As noted by Soltanlou et al (2019) , an increase in theta band in frontal and parietotemporal regions are associated with arithmetic problem solving and mathematical information retrieval, respectively.…”

Section: Introductionmentioning

confidence: 98%

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Psychophysiological evaluation of the Smartick method in children with reading and mathematical difficulties

Corona-González,

Ramos-Flores,

Alonso-Valerdi

et al. 2024

Front. Hum. Neurosci.

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IntroductionAssistive technologies for learning are aimed at promoting academic skills, such as reading and mathematics. These technologies mainly embrace mobile and web apps addressed to children with learning difficulties. Nevertheless, most applications lack pedagogical foundation. Additionally, the task of selecting suitable technology for educational purposes becomes challenging. Hence, this protocol posits the psychophysiological assessment of an online method for learning (OML) named Smartick. This platform comprises reading and math activities for learning training. In this protocol, individual monitoring of each child is proposed to determine the progress in learning caused by Smartick.Methods and analysisOne hundred and twelve children aged between 8 and 12 who present reading or math difficulty after a rigorous psychometric evaluation will be recruited. The study comprises four sessions. In sessions 1 and 2, collective and individual psychometric evaluations will be performed, respectively. Reading and mathematical proficiency will be assessed, as well as attentional levels and intellectual quotient. Subsequently, each child will be semi-randomly assigned to either the experimental or control groups. Afterward, a first EEG will be collected for all children in session 3. Then, experimental groups will use Smartick for 3 months, in addition to their traditional learning method. In contrast, control groups will only continue with their traditional learning method. Finally, session 4 will consist of performing a second psychometric evaluation and another EEG, so that psychophysiological parameters can be encountered that indicate learning improvements due to the OML, regardless of the traditional learning method at hand.DiscussionCurrently, few studies have validated learning improvement due to assistive technologies for learning. However, this proposal presents a psychophysiological evaluation addressed to children with reading or math difficulties who will be trained with an OML.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Psychophysiological evaluation of the Smartick method in children with reading and mathematical difficulties

Corona-González,

Ramos-Flores,

Alonso-Valerdi

et al. 2024

Front. Hum. Neurosci.

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“…All the study-participants are righthanded, aged 18+ years with normal/corrected-to-normal vision and normal hearing. Other researchers who have used EEG datasets for dyslexia detection using machine learning methods include Chen Wan Fadzal et al [49], Karim et al [50], and Fried and Breznitz [51], [52]. For MRI datasets, Chimeno et al [37] collected 3D structural images for both fMRI and DTI scans from 52 schoolchildren aged between 9 and 12 years old.…”

Section: B Data Collectionmentioning

confidence: 99%

Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

et al. 2021

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Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low selfesteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.

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Dyslexic Frequency Signatures in Relaxation and Letter Writing

Mohamad

Lee

Mansor

et al. 2020

Intelligent Information and Database Systems

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Comparison between characteristics of EEG signal generated from dyslexic and normal children

Cited by 7 publications

References 8 publications

Psychophysiological evaluation of the Smartick method in children with reading and mathematical difficulties

Psychophysiological evaluation of the Smartick method in children with reading and mathematical difficulties

Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

Dyslexic Frequency Signatures in Relaxation and Letter Writing

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