Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children

Abstract

Dyslexia diagnosis in children could not be performed in the absence of a specialist. This issue can be overcome with the use of advanced technology. Using convolution neural networks (CNN), the automatic classification of dyslexia from electroencephalogram (EEG) can be achieved. The role of the CNN and EEG in distinguishing dyslexia in children has not been explored. This study reveals the performance of CNN in recognising EEG signals of dyslexic and normal children using raw signals. The recorded EEG signals were passed through Short-Time Fourier Transform Analysis to transform the signals into images, which were then served as the input of the CNN. It was found that the CNN could recognise the EEG signals of dyslexic children with an accuracy of 80.9% and 72.1% using training and testing data, respectively.