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.

翻译：许多以电子脑-计算机界面为基础的大脑-计算机界面(BCI)系统使用大量高性能的渠道,而高性能需要花费大量时间来安装,而且难以实际应用。在不损害性能的情况下找到最佳的频道子集是一项必要和具有挑战性的任务。在本条中,我们提议了一个基于交叉关系、基于差异的标准(XCDC),用以评估不同运动图像任务的精神状态歧视渠道的重要性。 XCDC的性能在两个运动图像EEEG数据集中进行了评估。在两个数据集中, XCDC都大大减少了频道数量,但不会影响分类准确性,与所有频道设置相比。在相同的精确性限制下,拟议方法要求的频道选择比现有的基于Pearson相关系数和共同空间模式的频道选择方法少。 XCDC的视觉化显示了与神经生理原理一致的结果。