MGT: Multi-Granularity Transfer for Cross-Subject EEG Adaptation

Building EEG models that can adapt reliably to unseen subjects across diverse paradigms remains challenging because EEG signals vary substantially across subjects and tasks. Although existing approaches increasingly emphasize transferable EEG representations, knowledge acquired from different sources does not contribute equally when adapting to unseen subjects, and indiscriminate transfer inevitably induces negative transfer and degrades cross-subject generalization. We therefore propose Multi-Granularity Transfer (MGT), a general transfer framework for cross-subject EEG adaptation. MGT jointly models transfer relationships in heterogeneous EEG knowledge at multiple granularities, enabling the model to preserve universally transferable patterns while suppressing domain-mismatched knowledge. We further introduce a progressive transfer mechanism to stabilize knowledge adaptation as learning proceeds, thereby improving the robustness of cross-subject adaptation. We systematically evaluate MGT on seven EEG datasets spanning five paradigms, including motor imagery, emotion recognition, and imagined speech. Under cross-subject evaluation protocols, MGT consistently achieves robust decoding performance across these diverse paradigms. The results demonstrate the effectiveness of modeling transferability at multiple granularities and highlight the potential of MGT for cross-subject EEG decoding.