Author:
Zainuddin AZA,Mansor W.,Y. Lee Khuan,Mahmoodin Z.
Abstract
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.
Publisher
Institute of Advanced Engineering and Science
Subject
Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Information Systems,Signal Processing
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献