Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems

Many electroencephalogram (EEG)-based brain-computer interface (BCI) systems use a large amount of channels for higher performance, which is time-consuming to set up and inconvenient for practical applications. Finding an optimal subset of channels without compromising the performance is a necessary and challenging task. In this article, we proposed a cross-correlation based discriminant criterion (XCDC) which assesses the importance of a channel for discriminating the mental states of different motor imagery (MI) tasks. The performance of XCDC is evaluated on two motor imagery EEG datasets. In both datasets, XCDC significantly reduces the amount of channels without compromising classification accuracy compared to the all-channel setups. Under the same constraint of accuracy, the proposed method requires fewer channels than existing channel selection methods based on Pearson's correlation coefficient and common spatial pattern. Visualization of XCDC shows consistent results with neurophysiological principles.