Xu Jialin, Zuo Guokun
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Apr;33(2):201-7.
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.
针对脑机接口中运动想象任务的特征选择问题，提出了一种基于互信息和主成分分析（PCA）的脑电图（EEG）特征选择算法。该算法引入类别信息，用不同运动想象类别下特征间互信息矩阵之和代替协方差矩阵。和矩阵的特征向量表示主成分方向，和矩阵的特征值用于确定主成分维度。实验采用2005年国际脑机接口竞赛数据集，并采用四种特征提取方法，即功率谱估计、连续小波变换、小波包分解和 Hjorth 参数。采用所提出的特征选择算法选择并组合最有用的分类特征。结果表明，相对于PCA算法，在相同主成分维度下，借助支持向量机分类器，该算法在降维和分类准确率方面具有更好的性能。
