传感技术学报Issue(12)：1638-1643,6.DOI:10.3969/j.issn.1004-1699.2013.12.003
基于CI-HMM的运动想象脑电信号分类
Motor Imagery EEG Classification Based on CI-HMM
摘要
Abstract
In the applications of hidden Markov model( HMM) in motor imagery electroencephalogram( EEG) classi-fication,the independence assumption of HMM is inconsistent with the inherent correlation of EEG signals. In order to resolve the problem,an EEG classification method based on Choquet fuzzy integral HMM( CI-HMM) is proposed. The independence assumption of HMM is relaxed by substituting the monotonicity of fuzzy integrals for the additivity of probability measures. Each signal was segmented using overlapping sliding window. Then from each segment,the absolute mean,wavelength and wavelet packet based relative energy features were extracted to constitute observation sequence for the CI-HMM training and classification. The BCI Competition 2008 Datasets 1 with two classes of motor imagery were selected for classification experiments. The experimental results show that this method can effectively improve the performance of the HMM method used in motor imagery EEG classification.关键词
脑电信号/运动想象/模糊积分/隐马尔科夫模型Key words
EEG/motor imagery/fuzzy integral/hidden Markov model分类
信息技术与安全科学引用本文复制引用
孟明,满海涛,佘青山..基于CI-HMM的运动想象脑电信号分类[J].传感技术学报,2013,(12):1638-1643,6.基金项目
国家自然科学基金项目(61172134,61201302) （61172134,61201302）
浙江省自然科学基金项目(LY12F03006) （LY12F03006）
浙江省科技计划项目(2010C33075,2013C24016) （2010C33075,2013C24016）
