东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (8): 1103-1106.DOI: -

• 论著 • 上一篇    下一篇

采用相对小波能量法的脑-机接口设计

赵海滨;王宏;李春胜;   

  1. 东北大学机械工程与自动化学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-08-15 发布日期:2013-06-22
  • 通讯作者: Zhao, H.-B.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50435040)

Brain-computer interface design based on relative wavelet energy

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

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-08-15 Published:2013-06-22
  • Contact: Zhao, H.-B.
  • 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,提出采用相对小波能量的特征提取方法.首先深入研究了相对小波能量的计算方法,然后利用相对小波能量对脑电信号进行特征提取,最后采用支持向量机进行分类,并采用分类准确率和互信息作为该脑-机接口的评价标准.离线分析结果表明:分类准确率最高为85.7%,最大互信息为0.41比特.与较常用的自适应自回归(AAR)模型系数作为特征的方法相比,所提方法具有更高的识别准确率和互信息.

关键词: 脑电, 脑-机接口, 相对小波能量, 支持向量机, 互信息

Abstract: The feature extraction method using relative wavelet energy (RWE) is investigated for a brain-computer interface (BCI) based on two different mental tasks, i.e., the imaginary left and right hand movements. Discusses the computational method of RWE in depth, then RWE is used for the feature extraction of EEG signals with the support vector machine (SVM) used for classification. Classification accuracy and mutual information (MI) are taken as the evaluation criteria for BCI system. The off-line analysis results show that the maximum classification accuracy is 85.7% and maximum MI is 0.41 bit. Both are higher than the feature extraction characterized by the conventional adaptive autoregressive (AAR) coefficients.

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