Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing

Support Vector Machine with Theta-Beta Band Power Features Generated from Writing of Dyslexic Children

The classification of dyslexia using EEGrequires the detection of subtle differences between groups of children in an environment that are known to be noisy and full of artifacts. It is thus necessary for the feature extraction to improve the classification. The normal and poor dyslexic are found to activate similar areas on the left hemisphere during reading and writing. With only a single feature vector of beta activation, it is difficult to distinguish the difference between the two groups. Our work here aims to examine the classification performance of normal, poor and capable dyslexic with theta-beta band power ratio as an alternative feature vector. EEG signals were recorded from 33 subjects (11 normal, 11 poor and 11 capable dyslexics) during tasks of reading and writing words and non-words. 8 electrode locations (C3, C4, FC5, FC6, P3, P4, T7, T8) on the learning pathway and hypothesized compensatory pathway in capable dyslexic were applied. Theta and beta band power features were extracted using Daubechies, Symlets and Coiflets mother wavelet function with different orders. These are then served as inputs to linear and RBF kernel SVM classifier, where performance is measured by Area Under Curve(AUC) of Receiver Operating Characteristic (ROC) graph. Result shows the highest average AUC is 0.8668 for linear SVM with features extracted from Symlets of order 2, while 0.9838 for RBF kernel SVM with features extracted from Daubechies of order 6. From boxplot, the normal subjects are found to have a lower theta-beta ratio of 2.5:1, as compared to that of poor and capable dyslexic, ranging between 3 to 5, for all the electrodes