Classification of Motor Imagery EEG Signals Based on Wavelet Transform and Sample Entropy

摘要：

　　Modeling and learning of brain activity patterns represent a huge challenge to the brain-computer interface(BCI)based on electroencephalography(EEG).Many existing methods based on EEG signal frequency domain feature extraction can not achieve high classification accuracy rate requirements1.This paper presents a method of feature extraction of EEG based on wavelet transform and sample entropy.The dynamic changes of the sample entropy of the left and right hand motion imaginary EEG signal and its neurophysiological significance were analyzed.Finally,Fisher s linear discriminant was used to classify left-right hand motion imaginary EEG.Experiments shows that the average correct rate of classification was 88.9%.

关键词： brain computer interface(BCI) motor imagery(MI) Wavelet transform sample entropy(SampEn)

作者: Manzhen Ma Libin Guo Kuifeng Su Deqian Liang

作者单位: Department of Control Engineering,Academy of Armored Force Engineering Beijing,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 905-910

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）