Early detection of dyslexia based on EEG with novel predictor extraction and selection

Abstract

Abstract Dyslexia is a learning disorder caused by difficulties in the brain’s processing of letters and words. This study used EEG recordings to detect dyslexia at a young age. EEG recordings of 53 individuals, including 29 dyslexic and 24 normal individuals, were collected while they were engaged in two distinct mental activities known as the N-Back task and the Oddball task. Predictors were extracted using several methods and reduced using Principal Component Analysis (PCA). A relief-based strategy was applied to select predictors, and Support Vector Machine (SVM) classifier was used to achieve an average accuracy of 79.3% for dyslexia detection, which is better than the performance of its predecessors. The results indicate that EEG recordings and machine learning methods could be useful for identifying dyslexia in children.

Keywords

• Dyslexia
• EEG
• Power moments
• Principal component analysis (PCA)
• Feature selection
• Support vector machine (SVM)