发表机构
Institute of Automation, Chinese Academy of Sciences; University of Pittsburgh; Chinese University of Hong Kong, Shenzhen（中国科学院自动化研究所; 匹兹堡大学; 香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出任务条件潜在对齐（TCLA）框架，通过共享潜在空间和任务条件分布对齐，在跨会话和跨受试者神经解码中显著提升稳定性和鲁棒性，优于现有方法。
AI 中文摘要
在侵入式脑机接口（BMIs）中，实现稳定的长期神经解码仍具挑战性，原因是跨会话记录的神经群体存在变异。现有的潜在对齐方法在跨会话适应过程中可能忽视任务依赖的结构。我们提出了任务条件潜在对齐（TCLA），一种通过学习共享潜在空间来稳定神经解码的框架。TCLA利用神经重建和连续行为监督学习低维源表示。在目标会话适应阶段，共享表示保持不变，而目标神经活动通过分别对齐源分布和目标分布在每个任务条件下映射到源潜在空间。我们在七个非人灵长类数据集上评估了TCLA，这些数据集涵盖多个任务。在长期跨会话评估中，TCLA实现了平均$R^2$为$0.476\pm0.014$，负$R^2$失败率仅为6.8%。在1,356个受试者内会话对中，TCLA实现了平均$R^2$为$0.371\pm0.009$，失败率为6.8%。在2,134个跨受试者会话对中，TCLA实现了平均$R^2$为$0.218\pm0.004$，失败率为12.9%，显著优于比较方法。这些结果表明，通过保留行为相关和任务依赖的潜在结构，TCLA提高了跨记录会话和受试者的神经解码鲁棒性。源代码公开于\href{https://github.com/FAMD-CASIA/TCLA}{https://github.com/FAMD-CASIA/TCLA}。
英文摘要
Achieving stable long-term neural decoding in invasive brain-machine interfaces (BMIs) remains challenging due to variations in recorded neural populations across sessions. Current latent alignment approaches may overlook task-dependent structure during cross-session adaptation. We propose Task-Conditioned Latent Alignment (TCLA), a framework that stabilizes neural decoding by learning a shared latent space. TCLA learns a low-dimensional source representation using neural reconstruction and continuous behavioral supervision. During target-session adaptation, the shared representation is fixed, while target neural activity is mapped into the source latent space by aligning source and target distributions separately for each task condition. We evaluated TCLA on seven nonhuman primate datasets spanning multiple tasks. In long-term cross-session evaluation, TCLA achieved a mean $R^2$ of $0.476\pm0.014$ with a negative $R^2$ failure rate of only 6.8\%. Across 1,356 within-subject session pairs, TCLA achieved a mean $R^2$ of $0.371\pm0.009$ with a failure rate of 6.8\%. Across 2,134 cross-subject session pairs, TCLA achieved a mean $R^2$ of $0.218\pm0.004$ with a failure rate of 12.9\%, substantially better than those of the comparison methods. These results demonstrate that by preserving behaviorally relevant and task-dependent latent structure, TCLA improves the robustness of neural decoding across recording sessions and subjects. The source code is publicly available at \href{https://github.com/FAMD-CASIA/TCLA}{https://github.com/FAMD-CASIA/TCLA}.
