RMF-Net: Regional Multi-Mode Fusion Network for Fractal-Aware EEG Motor Imagery Decoding

Motor Imagery (MI) decoding based on electroencephalogram (EEG) is promising for Brain–Computer Interface (BCI) applications, yet existing methods generally suffer from two major limitations. (1) Global EEG signal analysis overlooks region-specific neural activity patterns, causing biased feature extraction and poor inter-subject generalization. (2) Few MI-EEG decoding studies adopt frequency decomposition for multi-rhythm feature extraction. Even when adopted, conventional methods rely on predefined frequency bands and suffer from mode mixing, failing to preserve the inherent fractal self-similarity and nonlinear characteristics of EEG signals, restricting the extraction of fine-grained specific features. To address these issues, we propose RMF-Net, a novel model integrating brain region division and Multi-variable Variational Mode Decomposition (MVMD). The model partitions EEG into functional brain regions based on MI neural mechanisms, performs dynamic modal feature extraction for each region via MVMD, and enables efficient cross-regional spatiotemporal feature interaction through an adaptive fusion. On the BCI Competition IV 2a open EEG MI dataset, our model achieves 80.06% accuracy in cross-session tasks and 63.05% in cross-subject tasks, outperforming other mainstream methods. Further analysis verifies that the cross-regional feature weight distribution of RMF-Net conforms to neuroanatomical principles. This work demonstrates that the spatiotemporal feature fusion framework combining brain region segmentation and fractal-aware multimodal signal decomposition is effective for EEG MI decoding tasks.