基于CSP变换和滤波器组的对数带通功率特征提取方法

Logarithmic band power feature extraction method based on CSP transform and filter bank

为了进一步提升运动想象脑电解码的性能,针对共空域模式(CSP)特征提取方法存在的问题,提出了新的CSP改进方法,即基于CSP变换和滤波器组的对数带通功率特征提取方法。首先,对原始脑电信号进行预处理;接着,使用CSP变换对预处理信号进行空间滤波;然后,使用滤波器组把空间滤波信号分解成多个子带,并提取每个子带信号的对数带通功率作为特征;最后,使用最小绝对值收缩和选择算子(LASSO)进行特征选择,并使用支持向量机(SVM)进行分类。在脑机接口(BCI)竞赛IV数据集IIa上进行了实验,所提出的方法取得了最高的平均分类准确率,结果为81.97%。实验结果表明,所提出的方法其分类性能优于现有的CSP改进方法,而且特征提取时间也具有较大优势。

In order to further improve the performance of motor imagery electroencephalogram(EEG) decoding, a new common spatial pattern(CSP) improvement method is proposed to address the problems of the CSP feature extraction method, that is, the logarithmic band power feature extraction method based on CSP transform and filter bank. First, the original EEG signals are preprocessed;then the preprocessed signals are spatially filtered using CSP transform;after that, the spatially filtered signals are decomposed into multiple sub-bands using filter bank, and the logarithmic band power of each sub-band signal is extracted as a feature;finally, the least absolute shrinkage and selection operator(LASSO) is used for feature selection, and the support vector machine(SVM) is used for classification. Experiments were conducted on the data set IIa of the brain-computer interface(BCI) competition IV, the proposed method achieved the highest average classification accuracy, and the result was 81.97%. The experimental results show that the classification performance of the proposed method is better than the existing improved CSP method, and the feature extraction time also has a greater advantage.