用视频动作先验提升脑机接口跨人泛化能力，减少校准需求。
EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors
- 用视频与脑电特征对齐，降低个体差异影响。
- 在EEGMMI数据集上实现8.66%的跨被试准确率提升。
- 适合需要少校准、强泛化的实际脑机接口应用。
实用的非侵入式脑机接口系统需要具备强跨被试泛化能力且校准成本低的脑电信号解码器。然而，个体间差异和信号非平稳性常导致运动语义与个体特异性噪声混淆，限制了跨被试解码。现有多模态方法使用文本作为语义锚点，但文本提供的监督信息稀疏且静态，难以匹配动态的运动过程。为此，我们提出EVA-Net，一种两阶段框架，利用动作视频作为语义先验实现跨被试脑电运动解码。第一阶段通过跨模态与有监督对比学习，将脑电与视频特征对齐至共享空间，以减少个体差异。第二阶段通过视频类别原型与知识蒸馏，将视频导出的先验知识迁移至仅依赖脑电的分类器，且不增加推理开销。在两个公开数据集上的实验表明，EVA-Net实现了出色的跨被试解码性能，其中在EEGMMI数据集上相比基线提升8.66%的留一被试（LOSO）准确率。消融实验进一步表明，相较于本文所考虑的文本基线，视频提供了更有效的语义锚点。
原文摘要 · Abstract (English)
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.
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