[Submitted on 3 Nov 2026]
Title:Recasting Visual Language Models as EEG Foundation Models for Clinical Neurodiagnostics
View a PDF of the paper titled Recasting Visual Language Models as EEG Foundation Models for Clinical Neurodiagnostics, by Xin Zhou and 5 other authors
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Abstract:Historically automated electroencephalography (EEG) systems was done using task-specific supervised models trained on purposely annotated data. A more recent modeling paradigm relies on creating a unified representation, or a 'foundation model', for multiple modality-specific tasks through large-scale domain-specific pretraining on the unannotated EEG signal. The quality of this unified representation directly depends on the availability of the modality-specific datasets. At the same time, generalist Vision Language Models (VLMs) exhibit broad few-shot learning capacities as well as good fine-tuning adaptability to different visual and language tasks. Can VLMs learn how to read and interpret EEG visually, emulating to a degree the way human clinicians inspect the EEG patterns? In this work we make a case that they can: we show that generalist VLMs without large-scale EEG pretraining perform competitively across a collection of clinical neuro-diagnostic tasks, can adapt to different tasks in a few-shot learning setting better than specialist foundation models, and exhibit better performance with model scaling when modality-specific models fail to exhibit.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2611.05538 [cs.LG]
(or arXiv:2611.05538v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.05538
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xin Zhou [view email] [v1] Tue, 3 Nov 2026 19:30:45 UTC (1,752 KB)
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