基于共空域频谱模式的少通道运动想象分类  

Common Spatial Spectral Pattern for Motor Imagery Tasks in Small Channel Configuration

作　　者：孟建军[1] 盛鑫军[1] 姚林[1] 朱向阳[1] 

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《中国生物医学工程学报》2013年第5期553-561,共9页Chinese Journal of Biomedical Engineering

基　　金：国家重点基础研究发展计划(973计划)(2011CB013305);上海市科委重点项目(11JC140600);国家重点实验室基金重点项目(MSVZD201204)

摘　　要：针对少通道脑电数据采集如2～3通道的情况下,利用每个通道信号的2～10个延时来扩展EEG信号的通道数,然后再利用共空域模式CSP算法进行特征抽取。针对每个通道信号的延时选取问题,提出利用非参数化估计互信息熵的方法来选择最佳个数的延时因子。应用多个时延的共空域频谱模式(CSSP)算法,对2008年BCI竞赛数据集IIb中的9个受试者以及实验室采集的13个受试者的想象运动数据集分析,结果表明可以使两个数据集的平均Kappa系数分别达到0.6与0.34。该方法可以依据8～30 Hz内频段的区分度自动优化权重系数,从而提高少通道数目下想象运动的分类正确率。Targeting to the deficient recording condition with 2 ~ 3 channels, a novel method, multiple time- delayed common spatial spectral pattern （CSSP）, was proposed in this paper. In this approach, 2 - 10 time- delayed Electroencephalogram （EEG） signals for each channel were used to expand the number of EEG channels and then common spatial pattern （CSP） was used to extract the features. Mutual information criterion was applied to choose the optimal number of time-delayed signals. The proposed method was tested by the IIb datasets of 9 subjects in BCI Competition 2008 and datasets of 13 subjects in our experiments. The average Kappa coefficients for the two datasets are 0.6 and 0. 34, respectively. This method automatically weights the importance of frequency in 8 ~ 30 Hz and enhances the classification accuracy in the case of deficient recording consequently.

关 键 词：脑机接口 共空域频谱模式 特征选择 信息熵 

分 类 号：R318[医药卫生—生物医学工程]