Spatiotemporal-filtering-based channel selection for single-trial EEG classification
IEEE TRANSACTIONS ON CYBERNETICS
Abstract:
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-theart 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（EEG）的脑-机接口（bci）中实现高分类性能通常需要大量的通道，这阻碍了它们在实际应用中的应用。尽管已有研究，但如何在不严重影响分类性能的前提下，以特定主题的方式确定最佳信道子集仍然是一个挑战。在本文中，我们提出了一种新的方法，即基于时空滤波的信道选择（STECS），利用EEG数据的时空信息自动识别指定数目的鉴别信道。在STECS中，信道选择问题被置于时空滤波器优化的框架下，通过引入一组稀疏性约束，提出了一种计算效率高的算法来解决优化问题。在三个运动图像脑电数据集上评估了STECS的性能。和目前最先进的全脑电通道时空滤波算法相比，STECS仅用一半的信道就可以获得相当的分类性能。此外，STECS明显优于现有的信道选择方法。这些结果表明，该算法有希望简化BCI设置和促进实际应用。