This article proposes an approach to select EEG channels based on EEG shapelet transformation, aiming to reduce the setup time and inconvenience for subjects and to improve the applicable performance of Brain-Computer Interfaces (BCIs). In detail, the method selects top- k EEG channels by solving a logistic loss-embedded minimization problem with respect to EEG shapelet learning, hyperplane learning, and EEG channel weight learning simultaneously. Especially, to learn distinguished EEG shapelets for weighting contributions of each EEG channel to the logistic loss, EEG shapelet similarity is also minimized during the procedure. Furthermore, the gradient descent strategy is adopted in the article to solve the non-convex optimization problem, which finally leads to the algorithm termed StEEGCS. In a result, classification accuracy, with those EEG channels selected by StEEGCS, is improved compared to that with all EEG channels, and classification time consumption is reduced as well. Additionally, the comparisons with several state-of-the-art EEG channel selection methods on several real-world EEG datasets also demonstrate the efficacy and superiority of StEEGCS.

中文翻译：

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Shapelet 变换的多通道 EEG 通道选择

本文提出了一种基于 EEG shapelet 变换的 EEG 通道选择方法，旨在减少受试者的设置时间和不便，并提高脑机接口 (BCI) 的适用性能。详细地，该方法选择顶部ķ通过同时解决有关 EEG shapelet 学习、超平面学习和 EEG 通道权重学习的逻辑损失嵌入最小化问题的 EEG 通道。特别是，为了学习区分 EEG shapelet 以加权每个 EEG 通道对逻辑损失的贡献，EEG shapelet 相似性也在过程中最小化。此外，本文采用梯度下降策略来解决非凸优化问题，最终产生了称为StEEGCS的算法。结果，与所有EEG通道相比，StEEGCS选择的EEG通道的分类精度提高了，分类时间消耗也减少了。此外，