Channel Selection based Similarity Measurement for Motor Imagery Classification

Because of the redundant information contained in the EEG signals, the classification accuracy of motor imagery may be greatly reduced. The channel selection method helps to remove task-independent EEG signals, thereby improving the performance of the BCI system. However, the brain regions associated with motor imagery in different subjects are not exactly same, and the method of channel selection depends on the feedback of classification results. Aiming at the above problems, this paper proposed a new method of channel selection based on backward elimination and threshold. First, we calculated the variance of channels between categories based on the covariance matrix. The backward selection and threshold selection methods were then used to retain the highly differentiated channels. We also used different criterion to evaluate the discriminability between different classes. The selected EEG signals used common spatial pattern (CSP) and support vector machine (SVM) to calculate the classification accuracy. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 77.82% accuracy on the BCI Competition IV data set IIa, 86.02% accuracy on the BCI Competition III data set IIIa, and 86.86% accuracy on the binary class BCI Competition III data set IVa. The performance of the proposed algorithm is compared with other existing algorithms. The results of our experiments demonstrated that the proposed algorithm produces a higher classification accuracy compared to the other algorithms using lesser number of channels.