发表机构
College of Computer Science and Technology, Zhejiang University; Nanhu Brain-Computer Interface Institute（浙江大学计算机科学与技术学院; 南湖脑机接口研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对侵入性脑机接口因神经漂移面临的长期部署挑战，提出UnSPC框架，通过UnSPL机制协同DA和DG，利用噪声鲁棒排序策略挖掘可靠伪标签样本，引入CycAG策略整合DA和DG，经实验验证其有效性和鲁棒性，为长期BMI控制铺路。
AI 中文摘要
脑机接口（BMI）将大脑与外部设备相连，在康复、人类性能增强和以人为本的机器人技术方面具有巨大潜力。然而，侵入性BMI由于神经漂移面临长期部署的关键挑战，现有缓解神经漂移的方法通常单独依赖域适应（DA）或域泛化（DG），无法捕捉神经子域的细粒度分布变化。为克服这些限制，我们提出不确定性引导自步循环（UnSPC）框架，通过不确定性引导自步伪标签（UnSPL）机制协同DA和DG进行目标域细化。为处理跨域的子域神经漂移，UnSPL通过噪声鲁棒排序策略迭代挖掘可靠伪标签样本进行微调。利用这些高质量样本，我们引入新颖的循环适应和泛化（CycAG）策略，在迭代循环中整合DA和DG以逐步缓解全局和子域漂移。大量实验证明了UnSPC的有效性和鲁棒性。我们提出的UnSPC首次将DA和DG与伪标签循环整合，为稳定的长期BMI控制铺平道路。
英文摘要
Brain-Machine Interfaces (BMIs), which link the brain to external devices, hold great potential in rehabilitation, human performance augmentation, and human-centered robotics. However, invasive BMIs face a critical challenge for long-term deployment due to neural drift, which degrades decoding performance over time and necessitates frequent recalibration. Existing methods designed to mitigate neural drift typically rely on either domain adaptation (DA) or domain generalization (DG) alone and often fail to capture fine-grained distribution shifts across neural subdomains, resulting in limited performance. To overcome these limitations, we propose Uncertainty-guided Self-paced Cycling (UnSPC), a robust framework that synergizes DA and DG for target domain refining under an Uncertainty-guided Self-paced Pseudo-labeling (UnSPL) mechanism. To handle subdomain neural drift across domains, UNSPL is proposed to iteratively mine reliable pseudo-labeled samples with a noise-robust ranking strategy for further fine-tuning. Leveraging these high-quality samples, we introduce a novel Cycling Adaptation and Generalization (CycAG) strategy, which integrates DA and DG within an iterative cycle to progressively mitigate both global and subdomain drift. This cyclic process enables effective alignment to evolving target distributions while preserving robust and transferable representations, thereby mitigating performance degradation under long-term neural drifts. Extensive experiments on multiple neural decoding datasets demonstrate the effectiveness and robustness of UnSPC. To our knowledge, our proposed UnSPC is the first to cyclically integrate DA and DG with pseudo-labeling, paving the way toward stable long-term BMI controls.
