# EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors Source: [EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors]() Collection index: [llms.txt]() Data updated: 2026-07-09T05:00:01.225Z Authors: Ziyuan Li, Yueru Sun, Yimeng Zhang Published: 2026-06-01T08:29:37.000Z ## Abstract 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. To address this issue, we propose EVA-Net, a two-stage framework that uses action videos as semantic priors for subject-independent EEG motor decoding. In the first stage, EEG and video features are aligned in a shared space using cross-modal and supervised contrastive objectives to reduce subject-specific variation. In the second stage, video category prototypes and knowledge distillation transfer video-derived priors to an EEG-only classifier without adding inference overhead. Experiments on two public datasets show that EVA-Net achieves strong subject-independent decoding performance, including an 8.66% LOSO accuracy gain on EEGMMI. Ablation results further suggest that video provides a more effective semantic anchor than the text baseline considered in this work. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Motor Imagery Decoding on EEGMMI (Leave-One-Subject-Out)](): Motor Imagery decoding; EEGMMI (Leave-One-Subject-Out); Accuracy: 72.3. - [Motor Imagery Decoding on EEGMMI (pooled-subject K-fold cross-validation)](): Motor Imagery decoding; EEGMMI (pooled-subject K-fold cross-validation); Accuracy: 76.1. - [Motor Imagery Decoding on BCIC-IV-2a (subject-dependent (session-independent))](): Motor Imagery decoding; BCIC-IV-2a (subject-dependent (session-independent)); Accuracy: 75.8. - [Motor Imagery Decoding on BCI Competition IV 2a I (cross-subject)](): Motor Imagery decoding; BCI Competition IV 2a I (cross-subject); Average Accuracy: 71.25. ## Original sources and implementation [Original paper]() [PDF]()