东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (8): 1098-1101.DOI: -

• 论著 • 上一篇    下一篇

基于CSP与SVM算法的运动想象脑电信号分类

刘冲;赵海滨;李春胜;王宏;   

  1. 东北大学机械工程与自动化学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-08-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50435040)

CSP/SVM-based EEG classification of imagined hand movements

Liu, Chong (1); Zhao, Hai-Bin (1); Li, Chun-Sheng (1); Wang, Hong (1)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-08-15 Published:2013-06-20
  • Contact: Liu, C.
  • About author:-
  • Supported by:
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摘要： 针对基于两种不同意识任务(想象左手运动和想象右手运动)的脑机接口,使用共空间模式(common spatial pattern,CSP)算法对BCI 2003竞赛数据进行特征提取;基于滑动时间窗,利用CSP方法对C3,Cz和C4位置的脑电信号进行处理.利用支持向量机对特征进行分类,获得最大分类正确率82.86%,最佳时间点4.09 s,最大互信息0.47 bit,最大互信息陡度0.431 bit/s.与BCI 2003竞赛结果相比,最大互信息陡度有了显著提高,证明该方法更适合BCI实时系统的要求.

关键词: 脑电信号, 脑机接口, 共空间模式, 支持向量机, 互信息, 分类时间

Abstract: For the BCI (brain-computer interface) to classify the different imagined movements of both left and right hands, the method of CSP (common spatial pattern) was used to extract the features of BCI 2003 competitive dataset. Then, the CSP based on a sliding time window was used to filter the EEG(electroencephalogram) data from the electrodes C3, Cz and C4, with the SVM (support vector machine) used as a classifier of the features. As a result, the highest accuracy of classification is 82.86% with the best classification time point 4.09 sec, maximum mutual information (MI) 0.47 bit and maximum MI steepness 0.431 bit/s. Compared to the results of BCI 2003 competition, the method as above can provide greatly improved maximum MI steepness, thus verifying that the method is more adaptable to what are required by the online BCI system.

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