Reliable long-term decoding of surface electromyography (EMG) is hindered by signal drift caused by electrode shifts, muscle fatigue, and posture changes. While state-of-the-art models achieve high intra-session accuracy, their performance often degrades sharply. Existing solutions typically demand large datasets or high-compute pipelines that are impractical for energy-efficient wearables. We propose a lightweight framework for Test-Time Adaptation (TTA) using a Temporal Convolutional Network (TCN) backbone. We introduce three deployment-ready strategies: (i) causal adaptive batch normalization for real-time statistical alignment; (ii) a Gaussian Mixture Model (GMM) alignment with experience replay to prevent forgetting; and (iii) meta-learning for rapid, few-shot calibration. Evaluated on the NinaPro DB6 multi-session dataset, our framework significantly bridges the inter-session accuracy gap with minimal overhead. Our results show that experience-replay updates yield superior stability under limited data, while meta-learning achieves competitive performance in one- and two-shot regimes using only a fraction of the data required by current benchmarks. This work establishes a path toward robust, "plug-and-play" myoelectric control for long-term prosthetic use.

翻译：表面肌电信号（EMG）的长期可靠解码受到电极移位、肌肉疲劳和姿势变化引起的信号漂移的阻碍。虽然最先进的模型在会话内能达到较高的准确率，但其性能通常会急剧下降。现有解决方案通常需要大型数据集或高计算量的处理流程，这对于高能效可穿戴设备而言并不实用。我们提出了一种基于时序卷积网络（TCN）主干的轻量化测试时自适应（TTA）框架。我们引入了三种可直接部署的策略：（i）用于实时统计对齐的因果自适应批量归一化；（ii）结合经验回放的高斯混合模型（GMM）对齐以防止遗忘；（iii）用于快速、少样本校准的元学习。在NinaPro DB6多会话数据集上的评估表明，我们的框架以极小的开销显著缩小了会话间的准确率差距。我们的结果表明，在数据有限的情况下，经验回放更新能带来更优的稳定性，而元学习仅需当前基准所需数据的一小部分，就能在单样本和双样本场景下达到具有竞争力的性能。这项工作为长期假肢应用建立了一条通往鲁棒、"即插即用"肌电控制的道路。