发表机构
Université de Montréal; Mila – Quebec Artificial Intelligence Institute; IMT Atlantique; IVADO, Université de Montréal; Hôpital du Sacré-Cœur de Montréal, Santé Québec Nord-de-l’Île-de-Montréal - Universitaire; Faculty of Medicine, Université de Montréal（蒙特利尔大学; 米拉-魁北克人工智能研究所; IMT大西洋国立高等矿业电信学校; 蒙特利尔大学IVADO研究所; 蒙特利尔圣心医院，魁北克健康部蒙特利尔岛北-大学网络; 蒙特利尔大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该文提出MEG基础模型的核心概念，概述其关键设计选择，围绕原生MEG预训练等方向给出发展路线，强调需构建配套基础设施以推动MEG基础模型研究发展。
AI 中文摘要
基础模型正重塑脑信号分析领域，推动其从特定任务解码流程转向基于广泛神经数据集预训练的可复用模型。脑磁图（MEG）是这一转变中极具潜力但仍待开发的目标：它以毫秒级分辨率捕捉人类皮层动态，同时比脑电图（EEG）具备更强的空间可解释性，对感知、语言、认知及临床脑功能的源解析研究尤为重要。然而MEG基础模型仍处于早期阶段，仅存在少量MEG专用及含MEG的多模态模型，预训练语料规模有限，基准测试也刚起步且范围受限。本文提出理解MEG基础模型所需的基本概念，并对该领域关键设计选择提供教学式概述，包括分词、传感器域与源域表征、传感器几何编码、骨干架构、自监督目标及预训练数据。随后围绕原生MEG预训练、EEG基础模型适配、通用时间序列模型迁移，以及与EEG、fMRI、MRI、行为及刺激特征的多模态整合，为未来发展提供路线图。本文强调需构建协调的基础设施，包括多样化可复用MEG数据集、跨被试、站点、任务及临床场景的严格评估，以及符合知情同意、隐私、访问和治理要求的负责任数据共享实践。
英文摘要
Foundation models are beginning to reshape brain-signal analysis by moving the field beyond task-specific decoding pipelines toward reusable models pretrained on broad neural datasets. Magnetoencephalography (MEG) is a compelling but still underdeveloped target for this shift: it captures human cortical dynamics at millisecond resolution while offering stronger spatial interpretability than EEG, making it especially valuable for source-resolved studies of perception, language, cognition, and clinical brain function. Yet MEG foundation models remain at an early stage, with only a small number of MEG-specific and MEG-inclusive multi-modal models, modest pretraining corpora, and emerging but still limited benchmarks. This perspective lays down the basic concepts needed to understand MEG foundation models and provides a didactic overview of the field's key design choices, including tokenization, sensor- versus source-space representations, sensor-geometry encoding, backbone architectures, self-supervised objectives, and pretraining data. We then offer a roadmap for future development, organized around native MEG pretraining, adaptation of EEG foundation models, transfer from generic time-series models, and multi-modal integration with EEG, fMRI, MRI, behaviour, and stimulus features. We highlight the need for coordinated infrastructure, including diverse and reusable MEG datasets, rigorous evaluation across subjects, sites, tasks, and clinical settings, and responsible data-sharing practices that address consent, privacy, access, and governance.
