Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

• URL: http://arxiv.org/abs/2602.23410v2
• Date: Mon, 02 Mar 2026 10:08:49 GMT
• Title: Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
• Authors: Hanning Guo, Farah Abdellatif, Hanwen Bi, Andrei Galbenus, Jon. N. Shah, Abigail Morrison, Jürgen Dammers,
• Abstract summary: We propose Brain-OF, the first brain foundation model jointly pretrained on fMRI, MEG and EEG inputs within a unified framework.<n>Brain-OF is pretrained on a large-scale corpus comprising around 40 datasets and demonstrates superior performance across diverse downstream tasks.
• Score: 2.783700146328046
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited to a single functional modality, restricting their ability to exploit complementary spatiotemporal dynamics and the collective data scale across imaging techniques. To address this limitation, we propose Brain-OF, the first omnifunctional brain foundation model jointly pretrained on fMRI, EEG and MEG, capable of handling both unimodal and multimodal inputs within a unified framework. To reconcile heterogeneous spatiotemporal resolutions, we introduce the Any-Resolution Neural Signal Sampler, which projects diverse brain signals into a shared semantic space. To further manage semantic shifts, the Brain-OF backbone integrates DINT attention with a Sparse Mixture of Experts, where shared experts capture modality-invariant representations and routed experts specialize in modality-specific semantics. Furthermore, we propose Masked Temporal-Frequency Modeling, a dual-domain pretraining objective that jointly reconstructs brain signals in both the time and frequency domains. Brain-OF is pretrained on a large-scale corpus comprising around 40 datasets and demonstrates superior performance across diverse downstream tasks, highlighting the benefits of joint multimodal integration and dual-domain pretraining.

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