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

TL;DR

This paper introduces a cross-correlation based discriminant criterion (XCDC) for selecting the most informative EEG channels in motor imagery BCI systems, reducing channels without losing accuracy, thus simplifying setup and improving practicality.

Contribution

The paper presents a novel XCDC method for ranking EEG channels based on their importance in discriminating motor imagery tasks, outperforming existing correlation-based methods.

Findings

XCDC significantly reduces channel count without accuracy loss

Fewer channels needed compared to existing methods

Results align with neurophysiological principles

Abstract

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.