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): 1107-1110.DOI: -

• 论著 • 上一篇    下一篇

基于共空间模式和K近邻分类器的脑-机接口信号分类方法

叶柠;孙宇舸;王旭;   

  1. 东北大学信息科学与工程学院;
  • 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: Ye, N.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50477015)

Classification of brain-computer interface signals based on common spatial patterns and K-nearest neighbors

Ye, Ning (1); Sun, Yu-Ge (1); Wang, Xu (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-08-15 Published:2013-06-22
  • Contact: Ye, N.
  • About author:-
  • Supported by:
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摘要： 脑-机接口是指在人脑和计算机之间建立的直接的交流和控制通道,它以脑电信号的形式反映人的意识,并转换成控制信号.针对两类运动想象脑电信号的分类问题,提出共空间模式和小波包分解相结合的脑电信号特征提取方法.利用不同小波包对训练集的多路脑电信号进行分解,再用共空间模式算法对不同分解层子带的脑电信号进行特征提取,并采用K近邻分类器对提取到的不同特征进行分类,得到最优小波包函数和小波包子带参数.将结果应用于测试集数据的分类.仿真实验结果表明,选择db4小波包函数和4层小波包分解层,对8个特征点进行分类,可以得到高达96%的正确率.

关键词: 脑-机接口, 脑电信号, 共空间模式, 小波包, K近邻分类器

Abstract: Brain-computer interface (BCI) refers to a communication/control channel between human and computer, which reflects human intention in the form of EEG signal then converts it into control signal. Two kinds of EEG signals of imaginary motions are classified, to which the feature extraction combining the wavelet packet decomposition (WPD) with common spatial pattern (CSP) is presented for EEG signals. Different wavelet packets are used to decompose the multi-channel EEG signals in training set, whereas CSP can extract the features of EEG signals from the subbands at different decomposed levels. Furthermore, the K-nearest neighbors (KNN) is used to classify the different features thus extracted to obtain the optimum wavelet function and relevant subband parameters which are used to classify test data. Simulation results showed that if the wavelet packet function is db4 with 4 decomposed levels and 8 feature points are selected, the highest classification accuracy can be up to 96%.

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