[1]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.
　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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脑电信号的小波变换和样本熵特征提取方法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 7 期数: 2012年第4期 页码: 339-344 栏目: 学术论文—脑认知基础 出版日期: 2012-08-25
Title:
EEG feature extraction method based on wavelet transform and sample entropy
文章编号:
1673-4785(2012)04-0339-06
作者:
张毅1,罗明伟1，罗元2
1.重庆邮电大学 自动化学院，重庆 400065；
2. 重庆邮电大学 光电工程学院，重庆 400065
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
关键词:
脑电信号; 样本熵; 小波变换; 支持向量机; 特征提取
Keywords:
electroencephalograph signal;  sample entropy;  wavelet transform;  support vector machine; feature extraction
分类号:
TP18； R318
文献标志码:
A
摘要:
针对现有的采用单一的特征提取算法对运动想象脑电信号识别率不高的问题，提出一种结合小波变换和样本熵的特征提取方法.通过小波变换，把脑电信号进行3层分解，抽取出对应于脑电β节律频带的小波系数的能量均值和能量均值差，并结合脑电信号的样本熵组成特征向量，使用支持向量机分类器对左右手运动想象脑电信号进行分类.结果表明，结合小波变换和样本熵的特征提取方法明显优于仅采用小波变换、样本熵以及其他传统的特征提取方法，得到的最高正确识别率为91.43%.
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%.
参考文献/References:
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备注/Memo

收稿日期： 2012-04-28.
网络出版日期：2012-08-03.
基金项目：科技部国际合作项目（2010DFA12160）；国家自然科学基金资助项目（51075420）.
通信作者：罗明伟. E-mail：350874723@qq.com.
作者简介：张毅，男，1966年生，教授，博士生导师，博士，重庆市人工智能学会理事,重庆邮电大学智能系统及机器人研究所所长，英国Essex大学机器人研究中心访问学者.主要研究方向为机器人技术及应用、生物信号处理及应用、模式识别.主持完成省部级及其他科研项目10余项，获国家发明专利4项，发表学术论文60余篇，其中30余篇被SCI、EI、ISTP检索,出版专著1部、教材2部;罗明伟，男，1987年生，硕士研究生.主要研究方向为脑电信号处理及应用,发表学术论文5篇;罗元，女，1972年生，教授，博士.主要研究方向为数字信号处理及应用、机器视觉、数字图像处理及应用.主持或参与国家自然科学基金、科技部国际合作项目、教育部回国留学人员科研项目等10余项，获国家发明专利6项，发表学术论文50余篇.

更新日期/Last Update: 2012-09-26
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