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ARXIV:2605.14698 · LLM TRAINING · SUBMITTED 15 MAY · 20:13 UTC · FRESHNESS FRESH
ARXIV:2605.14698LLM TRAININGSUBMITTED 15 MAY · 20:13 UTCFRESHNESS FRESHKonstantinos Kontras · Trui Osselaer · Stylianos G. Mouslech · Angeliki-Ilektra Karaiskou · Guido Gagliardi · Thomas Strypsteen · +9 at arXiv
NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG…
Opportunity summary
Pain NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG model.
Evidence 0 refs | 0 sources | 0% coverage
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NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG model. They have emerged…
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Published evaluations differ in datasets, in the EEG-specific preprocessing that might influence reported results, and in the reported metrics, frequently obscuring the clinical relevance…
LLM Training moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG…
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10.48550/arXiv.2605.14698NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG model.
Abstract
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field. Published evaluations differ in datasets, in the EEG-specific preprocessing that might influence reported results, and in the reported metrics, frequently obscuring the clinical relevance in EEG. We introduce NeuroAtlas, the largest EEG benchmark to date: 42 datasets and 260k hours covering clinical EEG (epilepsy, sleep medicine, brain age estimation) and brain-computer interfaces, and include multiple datasets per task along with bespoke clinical evaluation metrics. Besides evaluating EEG-FMs with respect to supervised baselines, we present results from generic time-series FMs. We report three findings. First, EEG-specific FMs do not consistently outperform time-series FMs, which have neither EEG-focused architectures nor been pretrained on EEG. Second, standard machine learning metrics are insufficient to assess clinical utility: thus, we thoroughly evaluate more appropriate measures such as the quality of event-level decision-making, hypnogram-derived features, and the brain-age gap in the domains of epilepsy, sleep, and brain age, respectively. Third, model rankings and performance can vary substantially within domains. We conclude that pretrained models perform largely on par, with only narrow advantages for a few, and that current models do not yet deliver on the promise of an out-of-the-box unified EEG model. NeuroAtlas exposes this gap and provides the datasets and metrics for the next generation of unified EEG FMs.
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PROBLEM
NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG model. They have emerged across fields, including electroencephalography (EEG), but it is...
METHOD
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Published evaluations differ in datasets, in the EEG-specific preprocessing that might influence reported results, and in the reported metrics, frequently obscuring the clinical relevance in EEG. Code ava...
WHY NOW
LLM Training moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG model. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Published evaluations differ in datasets, in the EEG-specific preprocessing that might influence reported results, and in the reported metrics, frequently obscuring the clinical relevance in EEG. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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NeuroAtlas is the largest EEG benchmark to date, evaluating foundation models for clinical applications and revealing that current models do not yet deliver on the promise of a unified EEG model.
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