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Brain-computer interface (BCI) is a system for communication and control between the human brain and computers or other electronic devices. However, multichannel signal acquisition, which is time-consuming, laborious, and not conducive to subsequent real-time signal processing, can cause channel redundancy. When designing a BCI system, the selection of the optimal channels that match the expected pattern of potential cortical activity is useful for classifying brain activity during a mental task. The Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of channel selection to extract the most relevant channel for classification. The superior performance of the algorithm is demonstrated by the test results on BCI Competition III dataset I. For a motor imagery paradigm, we show that the number of used channels can be reduced significantly, and the classification performance of the classifier can be improved. By comparing the performances of the algorithm with or without channel selection, the best channel combination was selected, and only 28 out of the 64 acquisition electrodes were used to realize classification sensitivity, specificity, precision, accuracy and Kappa coefficient values of 98%, 96%, 96.08%, 97% and 0.94, respectively, thereby outperforming existing algorithms. The proposed algorithm can reduce the number of channels and select the best channel combination to improve the classification performance, thereby overcoming the redundant channel problem. In addition, the signal processing framework that is adopted by this research can serve as a reference for related BCI application system research.

Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System