Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

[2604.16926] Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2604.16926 (cs) [Submitted on 18 Apr 2026 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts Authors: Gabriel Jason Lee , Jathurshan Pradeepkumar , Jimeng Sun View PDF HTML (experimental) Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations. Test-time adaptation (TTA) offers a promising solution by enabling models to adapt to unlabeled target data during inference without access to source data, a valuable property in healthcare settings constrained by privacy regulations and limited labeled data. However, its effectiveness for EEG remains largely underexplored. In this work, we introduce NeuroAdapt-Bench, a systematic benchmark for evaluating test-time adaptation methods on EEG foundation models under realistic distribution shifts. We evaluate representative TTA approaches from other domains across multiple pretrained foundation models, diverse downstream tasks, and heterogeneous datasets spanning in-distribution, out-of-distribution, and extreme modality shifts (e.g., Ear-EEG). Our results show that the evaluated TTA methods yield inconsistent gains and often degrade performance, with gradient-based approaches particularly prone to heavy degradation and optimization-free methods showing greater stability. For the evaluated EEG foundation models and representative TTA methods, these findings highlight the limitations of directly applying existing TTA techniques to EEG and underscore the need for domain-specific adaptation strategies. Comments: Accepted to MLHC 2026 Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Signal Processing (eess.SP) Report number: PMLR 340:948-984 Cite as: arXiv:2604.16926 [cs.LG] (or arXiv:2604.16926v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.16926 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:948-984, 2026 Submission history From: Gabriel Jason Lee [ view email ] [v1] Sat, 18 Apr 2026 09:23:25 UTC (975 KB) [v2] Sun, 5 Jul 2026 05:55:36 UTC (967 KB) [v3] Tue, 25 Aug 2026 07:01:51 UTC (8,728 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI eess eess.SP References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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