针对中风患者脑机接口解码难题，提出病理感知的时序校准方法。
PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
- 分离慢速节律与快速扰动，融合病理信息增强时序建模
- 基于生理一致性的伪标签动态优化，提升跨患者适配性
- 在两个中风数据集上准确率超70%，适合个性化康复应用
中风患者跨被试运动想象脑机接口（MI-BCI）解码对运动康复至关重要，但病灶相关的异常时序动态和显著的个体差异常导致泛化性能下降。现有自适应方法易受病理慢波活动和不稳定的靶域伪标签干扰。为此，我们提出PA-TCNet，一种病理感知的时序校准框架，结合生理引导的目标精炼机制。该框架包含两个协同模块：病理感知节律状态Mamba（PRSM）将脑电时空特征分解为缓慢变化的节律上下文与快速瞬态扰动，将融合后的病理上下文注入选择性状态传播，更有效捕捉异常时序动态；生理引导目标校准（PGTC）模块构建源域运动感觉区兴趣区模板，施加生理一致性约束并动态优化靶域伪标签，提高适应可靠性。在两个独立的中风脑电数据集XW-Stroke和2019-Stroke上的留一被试者外实验结果显示，平均准确率分别为66.56%和72.75%，优于当前最优基线。结果表明，联合建模病理时序动态与生理约束的伪监督可为个性化中风后MI-BCI康复提供更鲁棒的跨被试初始化。代码已开源：https://github.com/wxk1224/PA-TCNet。
原文摘要 · Abstract (English)
Stroke patient cross-subject electroencephalography (EEG) decoding of motor imagery (MI) brain-computer interface (BCI) is essential for motor rehabilitation, yet lesion-related abnormal temporal dynamics and pronounced inter-patient heterogeneity often undermine generalization. Existing adaptation methods are easily misled by pathological slow-wave activity and unstable target-domain pseudo-labels. To address this challenge, we propose PA-TCNet, a pathology-aware temporal calibration framework with physiology-guided target refinement for stroke motor imagery decoding. PA-TCNet integrates two coordinated components. The Pathology-aware Rhythmic State Mamba (PRSM) module decomposes EEG spatiotemporal features into slowly varying rhythmic context and fast transient perturbations, injecting the fused pathological context into selective state propagation to more effectively capture abnormal temporal dynamics. The Physiology-Guided Target Calibration (PGTC) module constructs source-domain sensorimotor region-of-interest templates, imposing physiological consistency constraints and dynamically refining target-domain pseudo-labels, thereby improving adaptation reliability. Leave-one-subject-out experiments on two independent stroke EEG datasets, XW-Stroke and 2019-Stroke, yielded mean accuracies of 66.56\% and 72.75\%, respectively, outperforming state-of-the-art baselines. These results indicate that jointly modeling pathological temporal dynamics and physiology-constrained pseudo-supervision can provide more robust cross-subject initialization for personalized post-stroke MI-BCI rehabilitation. The implemented code is available at https://github.com/wxk1224/PA-TCNet.
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