A Multi-Scale Attention-based Reconstruction Fusion Network for Motor Imagery Classification

Motor imagery (MI) is a widely used cognitive paradigm in brain-computer interface (BCI) systems, where accurate and efficient MI decoding is essential for real-time human-machine interaction. However, the non-stationary nature and pronounced inter-subject variability of electroencephalography (EEG) signals pose significant challenges to reliable decoding. To address these issues, we propose a multi-scale attention-based reconstruction fusion network (MSARFNet) for MI-EEG decoding. The proposed framework employs parallel multi-scale convolutional branches to extract discriminative spatio-temporal features at different temporal resolutions. An attention-based reconstruction fusion module is then introduced to selectively diminish non-dominant information while promoting effective interaction among multi-scale features. Furthermore, a local-global temporal encoding strategy is designed to enhance transient MI-related responses through local temporal context aggregation and subsequently capture long-range temporal dependencies via global temporal modeling. Subject-dependent experiments conducted on the BCI Competition IV 2a and 2b datasets demonstrate that MSARFNet achieves average classification accuracies of 84.64% and 87.96%, respectively, outperforming several state-of-the-art methods. These results indicate that MSARFNet provides an effective and robust solution for EEG-based MI decoding.