基于相关性和稀疏表示的运动想象脑电通道选择方法

在基于运动想象(MI)的脑机接口(BCI)中，通常采用较多通道的脑电信号(EEG)来提高分类精度，但其中会有包含与MI任务无关或冗余信息的通道，从而影响BCI的性能提升。该文针对运动想象脑电分类中的通道选择问题，提出一种采用相关性和稀疏表示对通道进行选择的方法(CSR-CS)。首先计算训练样本每个通道的皮尔逊相关系数来选择显著通道，然后提取显著通道所在区域的滤波器组共空间模式特征拼接成字典，利用由字典所得到的非零稀疏系数的个数表征每个区域的分类能力，选出显著区域所包含的显著通道作为最优通道，最后采用共空间模式和支持向量机分别进行特征提取与分类。在对BCI第3次竞赛数据集IVa和BCI第4次竞赛数据集I两个二分类MI任务的分类实验中，平均分类精度达到了88.61%和83.9%，表明所提通道选择方法的有效性和鲁棒性。

Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram

In Motor Imagery (MI) based Brain Computer Interface (BCI), more channels of ElectroEncephaloGram (EEG) signal are usually adopted to improve the classification accuracy. But there will be channels containing irrelevant or redundant information about MI tasks, which degenerate the performance improvement of BCI. A Channel Selection method based on Correlation and Sparse Representation (CSR-CS) is proposed for EEG classification. Firstly, the Pearson correlation coefficient of each channel of the training sample is calculated to select the significant channels. Then the filter bank common spatial pattern features of the region where the significant channels are located are extracted and spliced into a dictionary. The number of non-zero sparse coefficients obtained from the dictionary is used to characterize the classification ability of each region, and the significant channels contained in the significant regions are selected as the optimal channels. Finally, the common spatial pattern and support vector machine are employed for feature extraction and classification respectively. In the classification experiments of two categories of MI task with BCI competition III dataset IVa and BCI competition IV dataset I, the average classification accuracy reaches 88.61% and 83.9%, which indicates the effectiveness and robustness of the proposed channel selection method.