Dyslexia is referred as learning disability that causes learner having difficulties in decoding, reading and writing words. This disability associates with learning processing region in the human brain. Activities in this region can be examined using electroencephalogram (EEG) which record electrical activity during learning process. This study looks into performance of Support Vector Machine (SVM) using RBF kernel in classifying EEG signal of Normal, Poor and Capable Dyslexic children during writing words and non-words. Discrete Wavelet Transform (DWT) with Daubechies order 2 was employed to extract the power of beta and theta waves of EEG signal. Beta and Theta/Beta ratio form the input features for classifier. Multiclass one versus one SVM was used in the classification where RBF kernel parameters and box constraint values were varied with the factor of 10 to analyze performance of the classifier. It was found that the best performance of SVM with 91% overall accuracy was obtained when both kernel scale and box constraint are set to one.
Electroencephalograph (EEG) signal provides information on brain functionalities where electrodes are placed on the surface of the scalp and is suitable in analyzing neurological based disorder such as dyslexia. Known to cause learning disorder, dyslexic tends to utilize different areas of the brain in processing information compared to that of a normal learner. Being non-stationary, the wavelet theory has been extensively used in extracting relevant features from the noisy EEG signal with a wide option of wavelet families. The aim of this paper is to identify a suitable function order within the Symlets family to extract power feature in the EEG signal of dyslexic children during writing. Recorded EEG signals from 8 electrode locations of C3, C4, P3, P4, FC5, FC6, T7 and T8 were analyzed using Symlets function of order 5, 7, 8 and 9. The final selection are based on the order ability to provide the most distinctive variance and consistency in term of its beta band power feature. Results indicated that Symlets of order 5 and 7 (Sym-5, Sym-7) are suitable for extracting power band feature for EEG signal of poor dyslexic children during writing. However, results with capable dyslexic children were inconsistent.
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