Portrait de Oumayma Gharbi

Oumayma Gharbi

oumayma.gharbi@mila.quebec
Visiteur de recherche indépendant - UdeM
Superviseur⋅e principal⋅e
Blake Richards
Sujets de recherche
Apprentissage multimodal
IA et santé
Modèles de fondation
Neurosciences
Science des données

Publications

A Roadmap for MEG Foundation Models
Philipp Thölke
Hamza Abdelhedi
Yorguin Mantilla-Ramos
Fouad Lbakali
Oumayma Gharbi
Catherine Duclos
Annalisa Pascarella
Vanessa Hadid
Oiwi Parker Jones
Karim Jerbi
Foundation models are beginning to reshape brain-signal analysis by moving the field beyond task-specific decoding pipelines toward reusable… (voir plus) 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.
2026-09-02
arXiv (prépublication)
doi.org
arxiv.org