Classification of Motor Imagery EEG Based on a Time-Frequency Analysis and Second-Order Blind Identification
In this paper, two methods for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task was described. The first one is based on a time-frequency analysis of EEG signals. The original EEG signals are converted to time-frequency signals by a function of short time Fourier transforms (STFTs). In another method, we applied second-order blind identification (SOBI), a blind source separation (BSS) algorithm to preprocess EEG data. Subsequently in both of two methods, Fisher class separability criterion was used to select the features. Finally, classification of Motor Imagery EEG evoked by a sequence of randomly mixed left and right motor imagery was performed by a linear classifier or back-propagation neural networks (BPNN), using as inputs the two STFTs timefrequency signals or the two SOBI-recovered SI components or the two EEG channels C3/C4. The results showed that classification accuracy of Motor Imagery EEG was significantly improved by STFTs or SOBI preprocessing.
motor imagery EEG brain computer interface short time Fourier transforms (STFTs) second-order blind identification backpropagation neural networks
Dan Xiao Jianfeng Hu
Institute of Information and Technology JiangXi Blue Sky University Nanchang, China
国际会议
上海
英文
2199-2201
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）