The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact. We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms. Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture. Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for EEG motor imagery decoding within brain-computer interfaces.

翻译：本研究旨在探索不同通道注意力机制在脑机接口（BCI）运动想象解码领域中的应用。通道注意力机制可被视为传统运动想象解码中空间滤波器的强大演进版本。本研究通过将这些机制整合到一个轻量级架构框架中，系统性地比较了它们的影响。我们精心构建了一个简洁轻量的基线架构，旨在无缝集成不同的通道注意力机制。这一方法与以往仅研究单一注意力机制且通常构建高度复杂甚至嵌套架构的工作形成鲜明对比。该框架使我们能够在相同条件下评估并比较不同注意力机制的效果。不同通道注意力机制的易集成性以及低计算复杂度，使我们能够在四个数据集上进行广泛的实验，以全面评估基线模型及注意力机制的有效性。我们的实验证明了该架构框架的鲁棒性与泛化能力，并展示了通道注意力机制如何在保持基线架构小内存占用和低计算复杂度的同时提升性能。本架构强调简洁性，不仅便于集成通道注意力机制，还在不同数据集间保持了高度的泛化能力，从而为脑机接口中的脑电图运动想象解码提供了一种高效通用的解决方案。