IEEE Trans Cybern. 2021 Feb;51(2):558-567. doi: 10.1109/TCYB.2019.2963709. Epub 2021 Jan 15.
Achieving high classification performance in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data. In STECS, the channel selection problem is cast under the framework of spatiotemporal filter optimization by incorporating a group sparsity constraints, and a computationally efficient algorithm is developed to solve the optimization problem. The performance of STECS is assessed on three motor imagery EEG datasets. Compared with state-of-the-art spatiotemporal filtering algorithms using full EEG channels, STECS yields comparable classification performance with only half of the channels. Moreover, STECS significantly outperforms the existing channel selection methods. These results suggest that this algorithm holds promise for simplifying BCI setups and facilitating practical utility.
在基于脑电图（EEG）的脑机接口（BCI）中实现高分类性能通常需要大量通道，这阻碍了它们在实际应用中的使用。尽管之前已经做了很多努力，但仍然难以在不严重影响分类性能的情况下以特定于个体的方式确定最优的通道子集。在本文中，我们提出了一种新的方法，称为基于时空滤波的通道选择（STECS），通过利用 EEG 数据的时空信息，自动识别指定数量的有区别的通道。在 STECS 中，通过结合群组稀疏约束，将通道选择问题置于时空滤波器优化的框架下，并开发了一种计算高效的算法来解决优化问题。在三个运动想象 EEG 数据集上评估了 STECS 的性能。与使用全脑电通道的最新时空滤波算法相比，STECS 仅使用一半的通道即可获得可比的分类性能。此外，STECS 明显优于现有的通道选择方法。这些结果表明，该算法有望简化 BCI 设置并促进实际应用。
