面向脑机接口个体差异的滤波器组设计方法及其模型研究  

Filter Bank-based Model for the Subject’s Variance on Brain-compute Interface

作　　者：李子波 唐荣年[1] 谢小峰 Li Zibo;Tang Rongnian;Xie Xiaofeng(Electrical and Mechanical College,Hainan University,Haikou 570228,China)

机构地区：[1]海南大学机电工程学院,海南海口570228

出　　处：《海南大学学报（自然科学版）》2021年第2期177-184,共8页Natural Science Journal of Hainan University

基　　金：海南省基础与应用基础研究计划高层次人才项目(2019RC165);海南省科协青年科技英才创新计划项目(QCXM202011)。

摘　　要：脑机接口系统中的个体差异问题会导致脑电分类效率不稳定,因此,设计新型的滤波器组预处理结构及特征提取组合模型,减缓个体差异给脑机接口系统带来的影响.首先在预处理阶段,对脑电信号进行多带通滤波预处理,消除个体差异导致的脑电信号频带波动影响,其次在特征提取阶段,用共空域模式(CSP)算法进行粗特征粗提取,再分别用组稀疏(GL),基于极限学习机的自编码器(ELMAE)和互信息熵(MI)进行特征筛选,提高特征可分性,最后实验结果表明本文所提方法能够突出与运动想象相关的alpha和beta波段的信息,得到高可分的特征,而且具有优秀的分类性能.In our report,a novel filter bank-based preprocessing structure and feature extraction model was designed to alleviate the effects of the variance of subject on BCI system. During the filter bank-based stage,EEG signals were filtered by multi-band pass filter to alleviate the effects of the difference of related sub-band frequency corresponding to different subjects. During feature extraction stage,common spatial pattern(CSP)was used to extract features library,then,group lasso(GL),extreme learning machine autoencoder(ELMAE)and mutual information(MI)were used for feature selection to improve feature separability. The results showed that the proposed method can highlight the sub-band frequency of the alpha and beta wave,which has high separable features and classification performance.

关 键 词：脑电信号 滤波器组 CSP算法 ELM自编码器 组稀疏 互信息熵 

分 类 号：R318[医药卫生—生物医学工程]