Research on Feature Eztraction Algorithms in BCI
In this paper, wavelet packet algorithm, wavelet entropy algorithm and AR model algorithm were investigated for feature extraction. EEG data of six subjects were analyzed while they performed five different mental tasks. Based on the recognition rate under different mental EEG combination and different subject, it proved that wavelet entropy algorithm had better classification accuracy compared with the other two algorithms. The highest recognition rate is up to 98.48. The research is valuable and significant in the realization of control and communication based on the mental tasks in BCI.
Wavelet Packet Wavelet Entropy AR model Mental EEG BCI
SUN-Yuge YE-Ning ZHAO-Lihong XU-Xinhe
College of Information Science and Engineering, Northeastern University, Shenyang, 110004
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
5874-5878
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）