EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors

EVA-Net is a novel two-stage framework designed for subject-independent EEG motor decoding that leverages action videos as semantic priors. It employs cross-modal and supervised contrastive learning to align EEG and video features, achieving an 8.66% improvement in leave-one-subject-out (LOSO) accuracy on the EEGMMI dataset compared to traditional text-based approaches. This advancement is significant for practitioners as it enhances the robustness of BCI systems by reducing inter-subject variability and minimizing calibration requirements, thereby facilitating broader application of EEG decoding in real-world scenarios.