Optimal channel and frequency band‐based feature selection for motor imagery electroencephalogram classification

Abstract Common spatial pattern (CSP) is a widely adopted method for electroencephalogram (EEG) feature extraction in brain‐computer interface (BCI) based on motor imagery. Bandpass‐filtering EEG into several subbands related to brain activity tasks is an effective approach to improve the performance of CSP based algorithm. However, this approach tends to suffer the over‐fitting problem because of the increase in feature dimension. Therefore, we proposed an optimal channel and frequency band‐based CSP feature selection method in this paper. Firstly, the correlation coefficient was calculated to select the optimal channels, and these channels were bandpass‐filtered into multiple overlapping subbands. The subbands with higher power spectrum density were chosen for CSP feature extraction. Next, the pair‐wise relevance was utilized to remove subband features with less difference. And then the screened subband features were combined with features extracted from the broadband signal. The Fisher ratio was exploited to carry out further feature selection. Finally, a support vector machine (SVM) was trained to classify the selected CSP features. An experimental study was implemented on BCI competition III dataset IVa and BCI competition IV dataset 1. The average classification accuracy reached 89.33% and 84.08%, which indicated the rationality and effectiveness of the proposed method.