AI RESEARCH
EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors
arXiv CS.AI
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ArXi:2606.01884v1 Announce Type: new Practical non-invasive Brain-Computer Interface (BCI) systems require EEG decoders with strong cross-subject generalization and minimal calibration. However, inter-subject variability and signal non-stationarity often entangle motor semantics with subject-specific noise, limiting subject-independent decoding. Recent multimodal approaches use text as a semantic anchor, yet text provides sparse and static supervision for inherently dynamic motor processes.