  Convolutional neural networks (CNNs) and their variants have been
successfully applied to the electroencephalogram (EEG) based motor imagery (MI)
decoding task. However, these CNN-based algorithms generally have limitations
in perceiving global temporal dependencies of EEG signals. Besides, they also
ignore the diverse contributions of different EEG channels to the
classification task. To address such issues, a novel channel attention based
MLP-Mixer network (CAMLP-Net) is proposed for EEG-based MI decoding.
Specifically, the MLP-based architecture is applied in this network to capture
the temporal and spatial information. The attention mechanism is further
embedded into MLP-Mixer to adaptively exploit the importance of different EEG
channels. Therefore, the proposed CAMLP-Net can effectively learn more global
temporal and spatial information. The experimental results on the newly built
MI-2 dataset indicate that our proposed CAMLP-Net achieves superior
classification performance over all the compared algorithms.
