高技术通讯2018，Vol.28Issue(1)：22-28,7.DOI:10.3772/j.issn.1002-0470.2018.01.004
基于局部均值分解和多尺度熵的运动想象脑电信号特征提取方法
A method for extraction of motor imagery EEG features based on local mean decomposition and multiscale entropy
摘要
Abstract
Electroencephalogram(EEG)feature extraction is studied.Considering that traditional EEG feature extraction methods can not depict EEG features accurately,thus causing difficulties to motor imagery EEG classification under different mental tasks,this study proposes a feature extraction method based on local mean decomposition(LMD) and multiscale entropy(MSE).Firstly, the method adaptively decomposes an electroencephalogram(EEG)signal into a series of product function(PF)components with physical significance.Then,it selects effective PF compo-nents,calculates multiscale entropy, and combines multiscale entropy as eigenvectors.Finally, eigenvectors are put into the support vector machine(SVM)to identify the type of the electroencephalogram.The experimental re-sults show that the proposed method can effectively extract the features of EEG signal,which verifies the method's effectiveness and feasibility.关键词
脑电信号(EEG)/特征提取/局部均值分解(LMD)/多尺度熵(MSE)/支持向量机(SVM)Key words
electroencephalogram(EEG)/feature extraction/local mean decomposition(LMD)/multiscale entropy(MSE)/support vector machine(SVM)引用本文复制引用
邹晓红,张轶勃,孙延贞..基于局部均值分解和多尺度熵的运动想象脑电信号特征提取方法[J].高技术通讯,2018,28(1):22-28,7.基金项目
国家自然科学基金(No.61472340),国家自然科学基金青年基金(No.61602401)资助项目. （No.61472340）
