Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children

Abstract

Dyslexia is a specific learning difficulty associated with brain capability in processing numbers and letters. Analysis of Electroencephalogram (EEG) could provide insight information on differences in brain processing. In this work, two machine learning techniques were applied to distinguish EEG signals of normal, poor and capable dyslexic children during writing word and non-word. The performance of k-nearest neighbour (KNN) with correlation distance function and extreme learning machine (ELM) with radial basis function (RBF) were compared. The performance of each classifier was determined using sensitivity, specificity and accuracy. It was found that ELM was capable of classifying the dyslexic children with 89% accuracy compared to KNN which is only 83%. These results showed that ELM is feasible and reliable in recognising normal, poor and capable dyslexic children through writing.