通过相位感知建模，实现脑电到步态的实时高精度解码。
EEG-to-Gait Decoding via Phase-Aware Representation Learning
- 分两阶段建模：先对齐脑电与步态语义，再动态融合会话特异性头。
- 在两个数据集上优于最新模型，跨被试泛化能力强。
- 每窗口推理延迟低于5毫秒，适合实时脑机接口应用。
从脑电信号精确解码下肢运动对于推动脑机接口在运动意图识别与控制中的应用至关重要。本文提出NeuroDyGait，一种两阶段、相位感知的脑电到步态解码框架，显式建模时间连续性与域间关系。第一阶段通过基于交叉注意力的度量进行相对对比学习，生成语义对齐的脑电-运动嵌入；第二阶段通过会话特异性头的动态融合实现域关系感知解码。在两个基准数据集（GED 和 FMD）上的综合实验表明，该框架显著优于基线方法，包括2025年最新模型EEG2GAIT。框架具备对未见被试的泛化能力，且每窗口推理延迟低于5毫秒，满足实时脑机接口需求。对学习到的注意力和相位特异性皮层显著性图的可视化揭示了可解释的步态相位神经关联。未来工作将扩展至康复人群及多模态融合。
原文摘要 · Abstract (English)
Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.
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