Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification
Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification
复制标题
用于单次试验脑电图分类的基于时空过滤的通道选择
DOI：
10.1109/tcyb.2019.2963709
复制
发表时间：
2021-02-01
影响因子：
11.8
通讯作者：
Li, Yuanqing
中科院分区：
文献类型：
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作者：
Qi, Feifei;Wu, Wei;Li, Yuanqing
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的通道，这阻碍了它们在实际应用中的使用。尽管先前已经做出了努力，但在不以严重损害分类性能为代价的情况下，以特定受试者的方式确定通道的最优子集仍然是一个挑战。在本文中，我们提出了一种新方法，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判别力的通道。在STECS中，通过纳入组稀疏性约束，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。与使用全部EEG通道的最先进的时空滤波算法相比，STECS仅使用一半的通道就可产生相当的分类性能。此外，STECS显著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。
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.