A channel attention based MLP-Mixer network for motor imagery decoding with EEG
This work addresses motor imagery decoding for EEG-based brain-computer interfaces, representing an incremental improvement by combining existing methods like MLP-Mixer and attention mechanisms.
The paper tackled the problem of decoding motor imagery from EEG signals by addressing limitations in perceiving global temporal dependencies and ignoring channel contributions, proposing a channel attention based MLP-Mixer network (CAMLP-Net) that achieved superior classification performance on the MI-2 dataset.
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.