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

• URL: http://arxiv.org/abs/2110.10939v1
• Date: Thu, 21 Oct 2021 07:21:33 GMT
• Title: A channel attention based MLP-Mixer network for motor imagery decoding with EEG
• Authors: Yanbin He, Zhiyang Lu, Jun Wang, Jun Shi
• Abstract summary: CNNs and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. To address such issues, a novel channel attention based-Mixer network (CAMLP-Net) is proposed for EEG-based MI decoding.
• Score: 9.41450903202306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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