  Objective. 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. Approach. 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. Channels are ranked and selected according to the proposed
criterion. The efficacy of XCDC is evaluated on two motor imagery EEG datasets.
Main results. 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. Significance.
This work proposes a quantitative criterion for assessing and ranking the
importance of EEG channels in MI tasks and provides a practical method for
selecting the ranked channels in the calibration phase of MI BCI systems, which
alleviates the computational complexity and configuration difficulty in the
subsequent steps, leading to real-time and more convenient BCI systems.
