计算机工程与应用2017，Vol.53Issue(13)：9-15,54,8.DOI:10.3778/j.issn.1002-8331.1703-0199
公共空间模式算法结合经验模式分解的EEG特征提取
EEG signals feature extraction combined with empirical mode decomposition and common spatial pattern
摘要
Abstract
Normal Common Spatial Pattern(CSP)method is restricted to the abundant input channels and lacking fre-quency information. This paper puts forward an improved CSP method combined with the Empirical Mode Decomposition (EMD-CSP)to achieving feature vector by a different component choice. Firstly, the EMD method is proposed to decom-pose the EEG signal into a set of stationary time series called Intrinsic Mode Functions(IMF). Secondly, these IMFs are analyzed with the band-power to detect the valuable IMFs with characteristics of sensorimotor rhythms(5~28 Hz), and then the improved CSP filter is attached to the feature extraction of screening IMFs. Finally, once the feature vector is built, the classification of MI is performed using Support Vector Machine(SVM). The results obtained show that the EMD-CSP allow the most reliable features and that the accurate classification rate obtained is 92% which confirms the feasibility and availability of this method.关键词
脑电信号/经验模式分解/公共空间模式分解Key words
Electroencephalogram(EEG)/Empirical Mode Decomposition(EMD)/Common Spatial Pattern(CSP)分类
医药卫生引用本文复制引用
张学军,黄婉露,黄丽亚,成谢锋..公共空间模式算法结合经验模式分解的EEG特征提取[J].计算机工程与应用,2017,53(13):9-15,54,8.基金项目
国家自然科学基金(No.61271334). （No.61271334）
