东北大学学报（自然科学版）

东北大学学报（自然科学版） ›› 2013, Vol. 34 ›› Issue (12): 1695-1698.DOI: 10.12068/j.issn.1005-3026.2013.12.006

• 信息与控制 • 上一篇    下一篇

采用Fisher线性判别分析进行MEG信号的分类

赵海滨，颜世玉，于清文，王宏   

  1. (东北大学机械工程与自动化学院，辽宁沈阳110819)
  • 发布日期:2013-07-09
  • 通讯作者: 赵海滨
  • 作者简介:赵海滨(1979-)，男，河北唐山人，东北大学讲师，博士；王宏(1960-)，女，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61071057).

Classification of MEG Signals Using Fisher Linear Discriminant Analysis

ZHAO Haibin， YAN Shiyu， YU Qingwen， WANG Hong   

  1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China.
  • Published:2013-07-09
  • Contact: ZHAO Haibin
  • About author:-
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摘要： 脑磁图(MEG)具有比脑电(EEG)信号更高的时空分辨率，可以作为输入信号建立脑-机接口系统.提出一种脑磁图的特征提取和分类方法，首先对MEG信号进行预处理，然后提取时域特征，最后采用Fisher线性判别分析进行分类.将该算法用于2008年脑-机接口数据竞赛的数据集Ⅲ，该数据集为一个典型的采用MEG信号的脑-机接口系统.离线分析结果表明，该算法取得了很好的分类准确率，对两个测试者(S1和S2)的分类正确率分别为5946%和4324%.与其他方法相比，该方法简单有效，运算速度快，具有较高的参考价值.

关键词: 脑磁图, 脑-机接口, 线性判别分析, 特征提取, 分类

Abstract: The magnetoenephalography(MEG)signals have higher spatiotemporal resolution than EEG signals， which can be used as input signals to build braincomputer interface(BCI)system. Feature extraction and classification methods of the MEG signals were introduced. Firstly， the MEG signals were preprocessed， and then time domain features were extracted. Finally， Fisher linear discriminant analysis(LDA)was used to classify the MEG signals. This algorithm was used to the data set Ⅲ of 2008 BCI competition which was a typical MEGbased BCI system. The offline analysis results showed that high classification accuracy of 5946% and 4324% for two subjects(subject S1 and subject S2)could be obtained using this proposed algorithm. This algorithm is more efficient and simpler than others， which can be regarded as a good reference.

Key words: magnetoenephalography(MEG), braincomputer interface, linear discriminant analysis, feature extraction, classification

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