Function Classification of EEG Signal for Human–Computer Interface

Abstract

This paper proposes an adaptive algorithm for function classification of left-hand and right-hand imagery movements obtained from EEG signal. The electroencephalogram (EEG) is the signal acquired from human brain to monitor and identify human actions to different stimuli. The data was obtained from BCI competition III (b) 2003, acquired by Graz University of Technology. The EEG recorded was being sampled with 125 Hz and had been filtered between 0.5 and 30 Hz. The features were extracted using discrete wavelet transform (DWT). To obtain precise detail information, the EEG signal was processed with dimensionality reduction techniques as (i) singular value decomposition and (ii) LDA. The support vector machines (SVM) were being used for optimal classification of each motor movement. The result for binary class SVM was at an accuracy level of 100%. The results obtained and established an accuracy of singular value decomposition as the best tool to identify the imagery movements.