  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 three 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.

Human-Computer Interaction;Artificial Intelligence;Machine Learning