用双模块脑机接口实现实时步态解码，提升假肢控制精度。
A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study
- 分两阶段处理：先去噪提取多域特征，再用新型时间可变层+LSTM分类四类步态。
- 在验证集上马修相关系数达0.435，比其他模型高0.187，且跨脑区和频段表现稳定。
- 闭环测试中步态启动成功率超50%，端到端延迟仅70.5毫秒，适合实时应用。
通过脑电图（EEG）实现下肢外骨骼的闭环控制仍受限于运动伪影、信噪比低以及二元步态建模无法捕捉完整的皮层步态复杂性。本文提出一种两模块脑机接口架构：一个可训练的会话特定特征提取模块，具备实时伪影抑制与多域特征提取能力；搭配一个基于新型多项式时变层（PolyTVL）+LSTM的解码模块，用于四状态步态分类（站立、启动、执行、终止）。消融实验表明，v01模型（PolyTVL+LSTM）在验证集上的马修相关系数达到0.435，优于所有变体（差距0.187），且在不同脑区与频段间特征可区分性一致（p<0.05）。闭环部署中，v01模型在辅助外骨骼（Rex）支持下实现55.3%的步态启动成功率，在自主驱动下为52.7%，平均端到端处理时间为70.5±41.5毫秒，验证了该方法在本试点研究中的实时可行性。
原文摘要 · Abstract (English)
Closed-loop lower-limb exoskeleton control via Electroencephalography (EEG) remains limited by motion artifacts, low signal-to-noise ratio, and binary gait formulations that fail to capture full cortical gait complexity. We propose a 2-block Brain-Computer Interface (BCI) architecture: a trainable session-specific Feature Extraction Block with real-time artifact suppression and multi-domain feature extraction, coupled with a Decoder Block built on a novel Polynomial Time-Varying Layer (PolyTVL)+LSTM for four-state gait classification (Stand, Initiate, Execute, Terminate). Ablation confirmed v01 (PolyTVL+LSTM) outperformed all variants (validation MCC: 0.435, gap: 0.187), with consistent EEG feature discriminability across ROIs and sub-bands (p<0.05). Closed-loop deployment with v01 achieved 55.3% (Rex-assisted) and 52.7% (volitional) gait initiation success, with a mean end-to-end processing time of 70.5~ms (+/-41.5), validating real-time feasibility in this pilot study.
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