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ARXIV:2605.11410 · EEG FOUNDATION MODELS · SUBMITTED 13 MAY · 20:57 UTC · FRESHNESS STALE
ARXIV:2605.11410EEG FOUNDATION MODELSSUBMITTED 13 MAY · 20:57 UTCFRESHNESS STALELing Tang · Qian Chen · Jilin Mei · Houshi Xu · Quanshi Zhang · Jing Shao · +3 at arXiv
An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery.
Opportunity summary
Pain An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery. Modern EEG foundation models bypass this catalog, learn directly from…
Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across tasks, $50$ features qualify as universal candidates with strong support (all three architectures RC) in two or more tasks. Code availability is flagged…
EEG Foundation Models moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery.
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10.48550/arXiv.2605.11410An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery.
Abstract
Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraining, and match or outperform feature-engineered baselines on most clinical benchmarks. Whether the two representations align is an open question, which we decompose into three sub-questions: \emph{what does the model learn}, \emph{what does the model use}, and \emph{how much can be explained}. We answer them with layer-wise ridge probing, LEACE-style cross-covariance subspace erasure, and a transparent classifier benchmarked against a random-feature baseline. The audit covers three foundation models (CSBrain, CBraMod, LaBraM), five clinical tasks (MDD, Stress, ISRUC-Sleep, TUSL, Siena), and a 6-family 63-feature lexicon. Of the $945$ (model, task, feature) units, $648$ ($68.6\%$) are representation-causal and $199$ ($21.1\%$) are encoded-only. Across tasks, $50$ features qualify as universal candidates with strong support (all three architectures RC) in two or more tasks. Frequency-domain features dominate, but the other five families each contribute substantial causal mass. Confirmed features recover, on average, $79.3\%$ of the foundation model's advantage over the random baseline, with a clean task gradient (MDD $\approx 0.99$ down to Stress $\approx 0.56$): tasks near ceiling are almost fully recovered by the lexicon, while harder tasks leave a non-trivial residual that pinpoints a concrete target for future concept discovery.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretrain...
METHOD
Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraini...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across tasks, $50$ features qualify as universal candidates with strong support (all three architectures RC) in two or more tasks. Code availability is flagged in the production record; the public reposit...
WHY NOW
EEG Foundation Models moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraining, and match or outperform feature-engineered baselines on most clinical benchmarks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraining, and match or outperform feature-engineered baselines on most clinical benchmarks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across tasks, $50$ features qualify as universal candidates with strong support (all three architectures RC) in two or more tasks. 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
EEG Foundation Models moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An audit of EEG foundation models reveals which traditional brain signal features they capture and use, identifying universal candidates and areas for future discovery.
Segment
EEG Foundation Models
Adoption evidence
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Commercial read
7.0/10 public viability
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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stale
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Build readiness
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passport absent
stale
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GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
Current read
Buyer urgency is not verified from source.
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0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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missing
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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