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

A channel attention based MLP-Mixer network for motor imagery decoding with EEG