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

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.