A multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism for EEG-based motor imagery decoding

Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications.