智能系统学报2012，Vol.7Issue(4)：339-344,6.DOI:10.3969/j.issn.1673-4785.201204027
脑电信号的小波变换和样本熵特征提取方法
EEG feature extraction method based on wavelet transform and sample entropy
摘要
Abstract
Considering the issue of low recognition rate for electroencephalograph ( EEG) signal of motor imagery by using current single feature extraction method, a feature extraction method based on wavelet transform and sample entropy is presented in this paper. The EEG signals are decomposed to three levels by wavelet transform and the av-erage energy and its difference of wavelet coefficient corresponding to the p rhythm of EEG signals are computed. The feature vector is composed of the average energy, its difference of wavelet coefficient and sample entropy of EEG signals. Finally, the left-right hands motor imagery EEG signals are classified by a support vector machine classifier. The experimental results show that the feature extraction method combining wavelet transform and sample entropy is much better than the ways of only using wavelet transform, sample entropy, or others, and its highest recognition rate is 91.43%.关键词
脑电信号/样本熵/小波变换/支持向量机/特征提取Key words
electroencephalograph signal/sample entropy/wavelet transform/support vector machine/feature ex-traction分类
信息技术与安全科学引用本文复制引用
张毅,罗明伟,罗元..脑电信号的小波变换和样本熵特征提取方法[J].智能系统学报,2012,7(4):339-344,6.基金项目
科技部国际合作项目(2010DFA12160) （2010DFA12160）
国家自然科学基金资助项目(51075420). （51075420）
