自动化学报2017，Vol.43Issue(5)：743-752,10.DOI:10.16383/j.aas.2017.c160175
基于总体经验模态分解的多类特征的运动想象脑电识别方法研究
Multiple Feature Extraction Based on Ensemble Empirical ModeDecomposition for Motor Imagery EEG Recognition Tasks
摘要
Abstract
EEG signals are complicated as well as nonlinear and non-stationary, which make them hard to analyze. Recognition result is dependent on the datasets selected, and is not stable. The ensemble empirical mode decomposition (EEMD) as a kind of adaptive signal processing method is used for motor imagery recognition tasks because of its good decomposition resolution. An efficient EEMD-based feature extraction scheme is presented, which combines the Hilbert marginal spectrum (MS) and instantaneous energy spectrum (IES) features with window-added EEMD-based approximate entropy (ApEn) features. The impactful factors of IMFs and frequency bands are selected for the features as well. A linear discriminant analysis (LDA) classifier is designed for classifyication. The method is tested on nine subjects. The result shows that the proposed feature combination is competitive in recognition rate with other methods on the same dataset. The maximal classification accuracy for S2 and S3 can reach 79.60% and 87.77%, respectively. The mean accuracy of nine subjects is 82.74%. The average recognition rate obtained is superior to other methods on the same datasets.关键词
脑电信号/运动想象/总体经验模态分解/线性判别分类器Key words
Electroencephalogram (EEG)/motor image/ensemble empirical mode decomposition (EEMD)/linear dis-criminant analysis (LDA)引用本文复制引用
杨默涵,陈万忠,李明阳..基于总体经验模态分解的多类特征的运动想象脑电识别方法研究[J].自动化学报,2017,43(5):743-752,10.基金项目
吉林省科技发展计划自然基金(20150101191JC), 吉林大学研究生创新基金(2016092) 资助Supported by Natural Science Foundation for Science and Tech-nology Development Plan of Jilin Province(20150101191JC), and Graduate Innovation Fund of Jilin University(2016092) （20150101191JC）
