Yantao Chen

Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices, providing a bio-inspired link between humans and artificial systems. However, electroencephalography (EEG)-based motor imagery (MI) decoding continues to pose challenges, due to limited temporal exploitation and insufficient spatio-temporal modeling. To alleviate these limitations, a complementary dual-branch receptive field network (DBRFNet) is proposed for cross-channel-density MI-EEG decoding. Specifically, two complementary branches with distinct temporal receptive fields are designed to capture multi-scale temporal dynamics. Each branch employs a progressive spatio-temporal module integrating temporal, depthwise spatial, and dilated convolutions for modeling short- and long-range temporal dependencies. The features from the two branches are fused using element-wise addition and further encoded for classification. The proposed method is evaluated on three public datasets representing low-, medium-, and high-density EEG configurations. DBRFNet achieves decoding accuracies of 80.48%, 84.37%, and 92.24% under cross-validation analysis, and 87.76%, 80.94%, and 94.64% under hold-out analysis, demonstrating competitive performance compared with state-of-the-art methods. Ablation studies further validate the effectiveness of each component within the proposed architecture. These results indicate that DBRFNet effectively learns discriminative spatio-temporal representations and provides a robust solution for MI-BCI systems.