[1]ZHANG Yi,LUO Mingwei,LUO Yuan.EEG feature extraction method based on wavelet transform and sample entropy[J].CAAI Transactions on Intelligent Systems,2012,7(4):339-344.
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EEG feature extraction method based on wavelet transform and sample entropy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 4 Page number: 339-344 Column: 学术论文—脑认知基础 Public date: 2012-08-25
Title:
EEG feature extraction method based on wavelet transform and sample entropy
Author(s):
ZHANG Yi1;  LUO Mingwei1;  LUO Yuan2
1. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
?2. College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
electroencephalograph signal;  sample entropy;  wavelet transform;  support vector machine; feature extraction
CLC:
TP18； R318
DOI:
-
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 average energy and its difference of wavelet coefficient corresponding to the β 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%.
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