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arXiv AI

A Roadmap for MEG Foundation Models

The article outlines the emerging field of Magnetoencephalography (MEG) foundation models, explaining how these reusable, pretrained models can surpass traditional task‑specific decoding pipelines. It reviews current design choices—such as tokenization, sensor versus source representations, and self‑supervised objectives—and notes the limited number of existing MEG‑specific models and datasets. The authors propose a roadmap that includes native MEG pretraining, adaptation of EEG models, transfer from generic time‑series models, and multimodal integration with other neuroimaging and behavioral data, while emphasizing the need for coordinated infrastructure, rigorous evaluation, and responsible data‑sharing practices.

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