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

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.

EEG-based time and spatial interpretation of activation areas for relaxation and words writing between poor and capable dyslexic children.

Symptoms of dyslexia such as difficulties with accurate and/or fluent word recognition, and/or poor spelling as well as decoding abilities, are easily misinterpreted as laziness and defiance amongst school children. Indeed, 37.9% of 699 school dropouts and failures are diagnosed as dyslexic.