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Selection of Symlets wavelet function order for EEG signal feature extraction in children with dyslexia

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.