The Offline Feature Extraction of Four-class Motor Imagery EEG Based on ICA and Wavelet-CSP

摘要：

　　The signal processing of electroencephalogram(EEG)is the key technology in a brain-computer interface(BCI)system.A widely used method is to purify the raw EEG with an 8-30Hz band-pass filter and extract features by common spatial patterns(CSP).However its results for BCI Competition IV are not very satisfactory.To improve the classification success rate,this paper proposed a novel Wavelet-CSP with ICA-filter method.For the data sets from BCI Competition IV,the features of the four-class motor imagery were trained and tested using the Support Vector Machines(SVM).The experimental results showed that the proposed method had a higher average kappa coefficient of 0.68 than 0.52 of the general method.

关键词： Brain-computer-interface (BCI) electroencephalogram (EEG) ICA Wavelet-CSP SVM

作者: BAI Xiaoping WANG Xiangzhou ZHENG Shuhua YU Mingxin

作者单位: School of Automation,Beijing Institution of Technology,Beijing 100081,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 7189-7194

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）