发表机构
McGill University Health Centre, McGill University; Université de Montréal; Institute of Applied Mathematics M. Picone, National Council of Research; MILA (Quebec Artificial Intelligence Institute); Centre UNIQUE (Quebec Neuro-AI Research Center); Northwestern University（麦吉尔大学健康中心，麦吉尔大学; 蒙特利尔大学; M. Picone应用数学研究所，国家研究委员会; MILA（魁北克人工智能研究所）; UNIQUE中心（魁北克神经人工智能研究中心）; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于主成分分析的神经轨迹分析方法，将EEG/MEG信号映射到低维状态空间，以比较条件并关联行为或临床结果，推动群体动力学进入人类认知神经科学。
AI 中文摘要
神经活动并非作为一组独立信号展开，而是作为一个协调的动力学过程，可被描述为在高维状态空间中移动的一个点。这一视角对系统神经科学的最新发展做出了重要贡献，尤其是通过对直接记录的神经元群体活动的研究，但在EEG和MEG等非侵入性人类记录中仍相对未被充分利用。在本入门指南中，我们提出了一种基于主成分分析（PCA）的神经轨迹分析方法，作为将状态空间中神经轨迹概念扩展到人类电生理学的一条可行途径。我们解释了如何将EEG/MEG活动组织成神经状态表征，投影到按递减协方差组织的轴上，截断为低维表征，并可视化为捕捉分布式活动随时间演变的轨迹。我们展示了如何使用轨迹几何来比较不同条件，并将神经动力学与行为或临床结果联系起来。一个EEG运动执行和运动想象的示例，附有教程笔记本，展示了从传统EEG摘要到基于轨迹分析的工作流程。我们还阐明了PCA的局限性，包括其线性和方差驱动的特性，并讨论了EEG/MEG特有的挑战，如空间混合、预处理敏感性、验证和过度解释。最后，我们将PCA置于更广泛的状态空间方法家族中。通过使神经轨迹实用且可解释，本入门指南为将群体水平动力学的概念框架引入主流人类认知神经科学提供了一份指南。
英文摘要
Neural activity unfolds not as a set of independent signals, but as a coordinated dynamical process that can be described as a point moving through a high-dimensional state space. This perspective has contributed substantially to recent developments in systems neuroscience, especially through studies of directly recorded neuronal population activity, but remains comparatively underused in non-invasive human recordings such as EEG and MEG. In this primer, we present a neural trajectory analysis based on Principal Component Analysis (PCA) as an accessible route into extending the concepts of neural trajectories in state space to human electrophysiology. We explain how EEG/MEG activity can be organized into neural state representations, projected onto axes organized by decreasing covariance, truncated into low-dimensional representations, and visualized as trajectories that capture how distributed activity evolves over time. We show how trajectory geometry can be used to compare conditions and relate neural dynamics to behavior or clinical outcomes. An example of EEG motor execution and imagery, accompanied by a tutorial notebook, illustrates the workflow from conventional EEG summaries to trajectory-based analysis. We also clarify the limits of PCA, including its linear, variance-driven nature, and discuss EEG/MEG-specific challenges such as spatial mixing, preprocessing sensitivity, validation, and overinterpretation. Finally, we situate PCA within a broader family of state-space methods. By making neural trajectories practical and interpretable, this primer offers a guide for bringing the conceptual framework of population-level dynamics into mainstream human cognitive neuroscience.
