School of Computer Engineering, Nanyang Technological University, Singapore.
IEEE Trans Biomed Eng. 2011 Jun;58(6):1865-73. doi: 10.1109/TBME.2011.2131142. Epub 2011 Mar 22.
Multichannel EEG is generally used in brain-computer interfaces (BCIs), whereby performing EEG channel selection 1) improves BCI performance by removing irrelevant or noisy channels and 2) enhances user convenience from the use of lesser channels. This paper proposes a novel sparse common spatial pattern (SCSP) algorithm for EEG channel selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and outperformed existing channel selection methods based on Fisher criterion, mutual information, support vector machine, common spatial pattern, and regularized common spatial pattern in classification accuracy. The proposed SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three channels (C3, C4, and Cz).
多通道脑电图通常用于脑机接口（BCI）中，通过进行脑电图通道选择 1）可以通过去除不相关或噪声通道来提高 BCI 性能，2）通过使用较少的通道来提高用户的便利性。本文提出了一种新的稀疏共空间模式（SCSP）算法用于脑电图通道选择。所提出的 SCSP 算法被公式化为一个优化问题，以在分类准确性的约束下选择最少数量的通道。因此，通过去除噪声和不相关的通道，可以根据需要定制该方法以获得最佳的分类准确性，或者在不影响使用所有通道获得的分类准确性的情况下保留最少数量的通道。所提出的 SCSP 算法使用两个运动想象数据集进行评估，一个数据集具有中等数量的通道，另一个数据集具有大量通道。在两个数据集上，所提出的 SCSP 通道选择显著减少了通道数量，并且在分类准确性方面优于基于 Fisher 准则、互信息、支持向量机、共空间模式和正则化共空间模式的现有通道选择方法。与使用三个通道（C3、C4 和 Cz）相比，所提出的 SCSP 算法还平均提高了 10%的分类准确性。
