Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment

Oliaee, Anahita; Mohebbi, Maryam; Shirani, Sepehr; Rostami, Reza

doi:10.1007/s11571-022-09794-2

Cited by 7 publications

(3 citation statements)

References 69 publications

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“…Early identification and intervention methods based on artificial intelligence are receiving increasing attention. Although little attention has been paid to interventions in relation to MD, many researchers in the field of RD research believe that machine learning can not only quickly and objectively identify RD but also improve the accuracy of screening and distinguish the subtypes of RD by integrating multi-modal indicators of children's behavior and neurological activity characteristics ( 90 ) to improve intervention quality ( 91 , 92 ). We found that parents and teachers of children with MD in China have limited abilities to identify children with MD and that they lack special education resources, resulting in a lack of intervention for these children.…”

Section: Discussionmentioning

confidence: 99%

The prevalence of mathematical difficulties among primary school children in Mainland China: a systematic review and meta-analysis

Wang,

Long,

Wang

2024

Front. Public Health

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The prevalence of mathematical difficulties (MD) among primary school children in China and its related factors remain unclear. To provide a suitable foundation for formulating appropriate screening and intervention strategies, this study aimed to estimate the prevalence of MD and its possible factors among primary school children in China. Articles were identified from seven databases and included if they reported on the prevalence of MD among primary school children in China. A total of 30,881 articles were retrieved, 975 of which were subjected to full evaluation. In total, 54 studies involving 34,815 participants (mean age 9.86 ± 3.13 years) were included. Analyses were conducted using the Meta and Metafor packages in the statistical software environment R. We used meta-regression and subgroup analyses by variable type to evaluate the potential prevalence factors. The pooled prevalence of MD among primary school children in China was 8.97%, whereas the prevalence rates of MD in lower, middle, and upper primary schools were 11.57%, 10.07%, and 4.76%, respectively. The results showed that the prevalence of MD among primary school children in China was significantly related to grade level and the cut-off points selected for screening MD and that the prevalence of MD in middle primary school was higher in girls than in boys. The prevalence of MD in primary schools in China was slightly higher than that in countries such as the United Kingdom and the United States. The prevalence of MD was higher in the lower and middle grades of primary school. The grade level and cut-off points selected during MD screening were closely related to the prevalence of MD. More attention should be paid to girls with MD in middle primary school.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023410311, identifier: CRD42023410311.

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Section: Discussionmentioning

confidence: 99%

The prevalence of mathematical difficulties among primary school children in Mainland China: a systematic review and meta-analysis

Wang,

Long,

Wang

2024

Front. Public Health

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“…Since EEG (electroencephalogram) recordings do not involve radiation risks and are significantly more economical, it is a reliable and safe method to acquire data in children and adolescents. Oliaee et al [39] designed a study to investigate discriminating characteristics of EEG signals on dyslexic children associated with a particular tDCS therapy with occupational therapy. It is the study that tested a wealth of EEG features and found discriminative features for 16 children by recording EEG signals before and after the combined intervention.…”

Section: Tdcs On Relevant Visual and Auditory Processingmentioning

confidence: 99%

A systematic review of transcranial magnetic and direct current stimulation application on the improvement of reading ability of developmental dyslexia

2024

TNS

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Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), two non-invasive brain stimulation techniques (NIBS), are modulatory tools to probe into a possible causal and interaction relationship between brain structure and function. Developmental dyslexia (DD) is a neurodevelopmental disorder, which is characterized by significant and persistent difficulty in learning and reading-related skills. Previous studies have shown that TMS and tDCS play an important role in reading ability and neurocognitive plasticity, such as visuospatial working memory, attention, speech recognition, and motor perception. However, research on the effectiveness of TMS/tDCS in treating DD is still limited. The current study aimed to systematically review the broadly application of TMS/tDCS in the treatment of dyslexic children and adolescents as well as adults. Twelve studies involving 128 dyslexic subjects (199 children/adolescents and 29 adults) were included in this systematic review. Overall, TMS/tDCS appeared to be an effective technique for treating dyslexia. However, further future studies with larger sample sizes as well as more different languages are essential to demonstrate its potential for successful intervention in DD. This systematic review suggested that the combination of tDCS and cognitive training was effective. Moreover, the treatment with multiple sessions and coupled with behavioral training appeared to result in greater efficacy than stand-alone NIBS treatment.

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“…In (13) proposed treatment plans were proposed for improving dyslexic children's cognitive skills using electroencephalogram (EEG) patterns. They used a treatment consisting of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the Brainwave SAFARI software.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Feature Selection and Classification Methods for Predicting Dyslexia

Vanitha,

Kasthuri

2023

IJST

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Objectives: This study aims to select efficient and relevant features to detect Dyslexia with better accuracy using various Machine Learning (ML) models. Methods: A benchmark online gamified test dataset was used. Dyslexia from Kaggle used which contains 196 features. The dataset is divided as training and testing with 80-20%. Information Gain (IG), Principal Components Analysis (PCA), and Correlation Attribute Evaluation (CAE) are used to select relevant features. The performances of the selected features are evaluated using ML Classifiers models such as C 4.5, Random Forest (RF), Decision Table (DT), Logistic Regression (LR), and Support Vector Machine (SVM). Findings: Our feature selection method IG selects 192, PCA selects 195, and CAE selects 186 features out of 196 features. The selected features are tested with various above-mentioned ML classifier models. This study shows CAE with the LR classifier model well suited for select relevant features with 89.8% of accuracy. Novelty: This study presents a CAE feature selection approach with LR classifier approximately greater than 1.5 % accuracy of the existing approach of MIG, K-Best Features, and Recursive Feature Elimination in Random Forest. The proposed technique achieved improvement in accuracy.

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Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment

Cited by 7 publications

References 69 publications

The prevalence of mathematical difficulties among primary school children in Mainland China: a systematic review and meta-analysis

The prevalence of mathematical difficulties among primary school children in Mainland China: a systematic review and meta-analysis

A systematic review of transcranial magnetic and direct current stimulation application on the improvement of reading ability of developmental dyslexia

Performance Analysis of Feature Selection and Classification Methods for Predicting Dyslexia

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