Research of Feature Eztraction of BCI Based on Common Spatial Pattern and Wavelet Packet Decomposition
Brain-Computer Interface (BCI) is to establish a new communication system that translates human intentions reflected by EEG into a control signal for an output device such as a computer. This paper classified the EEG of two kinds of motor imagery. The feature extraction method combines wavelet packet decomposition and common spatial pattern. The k-nearest neighbors (KNN) is applied as classification method. The raw multi-channel EEG data is pre-processed by wavelet packet decomposition, with CSP method to extract the feature, and the best classification accuracy can reach 95.3％.If the EEG data is not decomposed by wavelet packet, the classification accuracy is only 83.3％. The result shows that if wavelet packet function and level is selected properly, the classification accuracy can improve effectively.
Brain-Computer Interface (BCI) EEG Common Spatial Pattern(CSP) Wavelet Packet(WP)
Ye Ning Mei Zhan Sun Yuge Wang Xu
Information Science & Engineering College,Northeastern University,Shenyang,110004 China College of Computer Science and Engineering,Shenyang University of Chemical Technology,Shenyang 1101
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5169-5171
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）