持续在线脑电图运动想象解码的微调策略：来自大规模纵向研究的见解
Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study
AI总结：
本研究首次在大型用户组的多会话因果场景中探索持续微调策略，结合在线测试时适应（OTTA），发现基于受试者特定信息的逐步微调可提升性能与稳定性，OTTA实现无校准适配，为纵向在线EEG运动想象解码提供泛化性见解。
摘要：
本研究在涉及大型用户组且每位参与者有多个会话的因果场景中，调查了深度学习在在线纵向脑电图（EEG）运动想象（MI）解码中的持续微调策略。由于纵向适应通常在单受试者场景下用单一适应策略研究，限制了研究结果的泛化能力，我们首次在大型用户组中探索此类策略。首先，我们考察了不同微调方法对解码器性能和稳定性的影响。在此基础上，我们整合了在线测试时适应（OTTA），以便在部署期间适配模型，补充先前微调的效果。我们的研究结果表明，基于先前受试者特定信息逐步构建的微调可同时提升性能和稳定性，而OTTA能有效使模型适应连续会话中演化的数据分布，实现无需校准的操作。这些结果为纵向在线MI解码的未来研究提供了宝贵见解和建议，并强调了结合域适应策略对提升现实应用中脑机接口（BCI）性能的重要性。临床相关性：我们的研究使更稳定、高效的长期运动想象解码成为可能，这对神经康复和辅助技术至关重要。
英文摘要：
This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting involving a large user group and multiple sessions per participant. We are the first to explore such strategies across a large user group, as longitudinal adaptation is typically studied in the single-subject setting with a single adaptation strategy, which limits the ability to generalize findings. First, we examine the impact of different fine-tuning approaches on decoder performance and stability. Building on this, we integrate online test-time adaptation (OTTA) to adapt the model during deployment, complementing the effects of prior fine-tuning. Our findings demonstrate that fine-tuning that successively builds on prior subject-specific information improves both performance and stability, while OTTA effectively adapts the model to evolving data distributions across consecutive sessions, enabling calibration-free operation. These results offer valuable insights and recommendations for future research in longitudinal online MI decoding and highlight the importance of combining domain adaptation strategies for improving BCI performance in real-world applications. Clinical Relevance: Our investigation enables more stable and efficient long-term motor imagery decoding, which is critical for neurorehabilitation and assistive technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
