基于互信息与主成分分析的运动想象脑电特征选择算法

Motor Imagery Electroencephalogram Feature Selection Algorithm Based on Mutual Information and Principal Component Analysis

针对脑机接口中运动想象任务的特征选择问题,提出一种基于互信息与主成分分析的脑电特征选择算法。该算法融入类别信息,用不同运动想象类别条件下特征间的互信息矩阵之和取代传统主成分分析算法中的协方差矩阵,其特征向量表示新的主成分空间内各主成分的方向,特征值则作为评价准则判断主成分维数。对2005年国际BCI竞赛数据集,联合功率谱估计、连续小波变换、小波包分解、Hjorth参数四种方法进行特征提取,采用所提出的算法进行特征选择并与主成分分析算法对比,实验结果表明,所提出算法的降维效果更好,以支持向量机为分类器,相同维数的主成分,所得分类正确率更高。

Aiming at feature selection problem of motor imagery task in brain computer interface（BCI）,an algorithm based on mutual information and principal component analysis（PCA）for electroencephalogram（EEG）feature selection is presented.This algorithm introduces the category information,and uses the sum of mutual information matrices between features under different motor imagery category to replace the covariance matrix.The eigenvectors of the sum matrix represent the direction of the principal components and the eigenvalues of the sum matrix are used to determine the dimensionality of principal components.2005 International BCI competition data set was used in our experiments,and four feature extraction methods were adopted,i.e.power spectrum estimation,continuous wavelet transform,wavelet packet decomposition and Hjorth parameters.The proposed feature selection algorithm was adopted to select and combine the most useful features for classification.The results showed that relative to the PCA algorithm,our algorithm had better performance in dimensionality reduction and in classification accuracy with the assistance of support vector machine classifier under the same dimensionality of principal components.