# Can resting-state EEG separate Parkinson's disease from controls?

Clinical research · resting-state EEG

Parkinson's disease against controls, person by person: a spectral model on resting-state EEG, and a comparator given only age and sex; and an infant case-control pilot with age and recording-setting controls; and resting EEG in schizophrenia and controls beside age and sex.

- [Reviewed aggregate JSON ↓](https://bci.report/data/clinical-update.json)
- 

Measured on: [OpenNeuro ds004584 · Parkinson's disease, rest eyes open](https://bci.report/datasets/ds004584/)

## Short answer

Above chance, as a group-level research result from one site. Across **149** people with Parkinson's disease or in the control group, a relative-spectrum logistic regression on resting-state EEG separated the two groups at **71.7%** balanced accuracy (95% interval **63.5%–79.3%**, AUROC **0.76**, chance **50%**); a comparator given only age and sex reached **55.0%** (**46.9%–63.6%**) on the same folds.

**The same 149 people and the same 5 folds.** Person-level balanced accuracy, Parkinson's disease against controls. Dot: the estimate; line: descriptive bootstrap 95% interval; dashed line: chance, 50%.

- Relative-spectrum logistic regression — resting-state EEG: 71.7% (63.5%–79.3%)
- Age-and-sex comparator — no EEG input: 55.0% (46.9%–63.6%)

**+16.6 pp** EEG minus the age-and-sex comparator, same 149 people

### A higher mean, with overlapping intervals

The EEG model's interval starts at 63.5%; the comparator's ends at 63.6%.

Research results on a public data set. Not a diagnosis, not diagnostic accuracy, and not a medical device or medical advice.

EEG model and comparator

## EEG against age and sex

Two classes, chance 50%; 5 stratified participant folds.

Person-level balanced accuracy with its 95% interval, macro F1 and AUROC.

| Row | Inputs | Balanced accuracy | Macro F1 | AUROC |
| --- | --- | --- | --- | --- |
| Relative-spectrum logistic regression — log relative band power, 60 shared channels | EEG only | 71.7% (63.5%–79.3%) | 71.1% | 0.76 |
| Age and sex only — not an EEG model | no EEG | 55.0% (46.9%–63.6%) | 54.0% | 0.56 |

Cohort and features

## One site, 149 people, two minutes each

The 60 named channels shared by four source layouts; the first 120 seconds of each recording, cut into 30 disjoint four-second windows; log relative power in five bands spanning 1–45 Hz. Each person's 300 features are averaged within that person, so the classifier sees one vector per person rather than 4,470 independent examples. Scaling is fitted on training folds only, the classifier is a fixed balanced logistic regression (C=1), and there is one split seed with no held-out tuning.

People

149

100 people with Parkinson's disease and 49 controls, one site

Participant folds

5

each person held out once

Four-second windows

4,470

averaged within each person

Methods & limits

## Limits

### Not a diagnosis

Group-level discrimination on a fixed research split, reported as balanced accuracy. Screening, clinical validation and interpretation for any individual person are outside its scope.

### Not replicated

One cohort, one split seed. The intervals are descriptive bootstrap ranges, because the cross-validation training folds overlap.

### Age and sex are not removed

The comparator shows that part of this separation is available with no brain signal at all. The EEG model is not adjusted for age or sex, so its score is not a measure of disease-specific brain activity.

### Group differences are not only in the brain

Anything that differs between patients and controls travels with the labels: medication, recording session, movement, alertness. A case/control score cannot separate those from the disease.

### Foundation models: held

The source states no physical amplitude unit. A relative spectrum needs none; models that need a calibrated scale are held.

### Independently replayed

149 recordings and 4470 spectral windows with a separate FFT, all classifiers refit. Reconstructed features and metrics differed by 0; intervals differed at floating-point precision.

### OpenNeuro ds004584 · Rest eyes open, Parkinson's disease and controls

Dataset: Arun Singh, Rachel Cole, Arturo Espinoza, Jim Cavanagh and Nandakumar Narayanan · Rest eyes open, OpenNeuro ds004584 v1.0.0, doi:10.18112/openneuro.ds004584.v1.0.0. Cohort study: Arun Singh, Rachel C. Cole, Arturo I. Espinoza, Jan R. Wessel, James F. Cavanagh and Nandakumar S. Narayanan · Evoked mid-frontal activity predicts cognitive dysfunction in Parkinson's disease, Journal of Neurology, Neurosurgery & Psychiatry 94, 945-953 (2023), doi:10.1136/jnnp-2022-330154. Transport mirror: NEMAR on004584, doi:10.82901/nemar.on004584.

Every participant gave written informed consent, with decisional capacity established, in accordance with the Declaration of Helsinki; all research protocols were approved by the University of Iowa Human Subjects Review Board (IRB 201707828). Only cohort aggregates appear here: no participant rows, no clinical or cognitive scores, and no per-group age or sex composition.

[OpenNeuro dataset ↗](https://doi.org/10.18112/openneuro.ds004584.v1.0.0) · [Cohort study ↗](https://doi.org/10.1136/jnnp-2022-330154) · [Open manuscript ↗](https://europepmc.org/article/PMC/PMC10592174) · [NEMAR mirror ↗](https://doi.org/10.82901/nemar.on004584) · [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/)

Data source: [reviewed aggregate JSON](https://bci.report/data/clinical-update.json) · schema bci-report-clinical-update-v1.

Infant case-control pilot · awake EEG

## Do EEG case-control results survive age and recording-setting controls?

IESSEEG is a public EEG dataset from one institution’s retrospective cohort: 100 people, 50 cases of infantile epileptic spasms syndrome, each on the recording made before the treatment the source study evaluated, and 50 controls. Each person contributes one clinician-selected awake segment, chosen by a fixed rule with no signal-driven choice, read for its first 60 s on the 17 channels every recording shares. Every arm is scored out of fold on the publisher’s 5 participant-disjoint folds: 80 people train, 20 are held out, and each person is held out once.

**The probes and every score were reproduced independently; the NeuroLM features themselves were not re-extracted by a second implementation.**

Spectral EEG reached 81.00% balanced accuracy, and 80.00% with age added. Age alone reached 68.00%, recording settings alone 61.00%, and the training prior 50.00%. With age regressed out inside each training fold, spectral EEG reached 67.00%. On the same people, folds and probe, frozen GPT hidden states reached 84.00% and the frozen tokenizer 77.00%; with age regressed out, 71.00% and 72.00%.

**Both tables.** Point estimates out of fold; no interval or test was specified. One awake recording per person, first 60 s. Age and recording settings differ between groups; regressing out age is a sensitivity analysis. Balanced accuracy is the primary metric; AUROC is shown beside it for every arm, and no arm or threshold is chosen from these results.

The age ≤2 column re-scores the same out-of-fold predictions for the people aged 2 or under, with nothing refitted; it is a descriptive subgroup. Only the band’s size and its case and control counts are published; per-group age means and sex counts stay unpublished. In this cohort the band holds 68 people: 42 cases and 26 controls.

### Age and acquisition controls for an awake EEG case-control pilot

The spectral arms read Welch spectra at each recording’s native rate (4 s windows overlapping by half, 0.25 Hz resolution): log relative power in five bands from 1 to 45 Hz and log total power over the same range, for each of the 17 channels, 102 features per person. Every learned arm uses the same probe, fixed in advance with no tuning: standardisation fitted on the training fold, then L2 logistic regression (C = 1). The age terms are log1p of age at the recording and its square; the residualized arm regresses each feature on them inside the training fold and applies those coefficients to the training and held-out people. The recording-settings arm reads the original scalp channel count, 19 in 59 recordings and 17 in 41, and the native sampling rate, 200 Hz in 98 and 2,000 Hz in 2.

Six arms with and without EEG, each scored out of fold

| Arm | Balanced accuracy, all 100 | AUROC, all 100 | Balanced accuracy, age ≤2 years — 68 people |
| --- | --- | --- | --- |
| Without EEG |  |  |  |
| Training prior — No input: the training fold’s share of cases, which calls every person a case | 50.00% | 0.500 | 50.00% |
| Age only — No EEG: log1p of age at the recording, and its square | 68.00% | 0.621 | 50.00% |
| Acquisition metadata only — No EEG: the original scalp channel count and sampling rate | 61.00% | 0.524 | 69.23% |
| With EEG |  |  |  |
| EEG spectral features — The 102 log band-power features of the 17 channels | 81.00% | 0.824 | 76.56% |
| EEG spectral features + age — The 102 spectral features and the two age terms | 80.00% | 0.843 | 73.90% |
| EEG after training-fold age residualization — The 102 spectral features with age regressed out inside each training fold | 67.00% | 0.703 | 69.41% |

### Do frozen EEG representations survive age controls?

NeuroLM-B is read frozen, as a feature extractor: its weights are unchanged, and no text is involved. Each recording’s 60 s are cut into 15 windows of 4 s, with one token per channel and second, 68 tokens per window. Two of the model’s internal states are read: the tokenizer’s, after its final normalisation, and the GPT branch’s hidden states, after the final layer normalisation, 768 values each, averaged over each window’s tokens and then over the windows. The probe and the training-fold age regression are those of the spectral arms, unchanged.

Four arms reading frozen NeuroLM-B states with the same probe, on the same people and folds

| Arm | Balanced accuracy, all 100 | AUROC, all 100 | Balanced accuracy, age ≤2 years — 68 people |
| --- | --- | --- | --- |
| Frozen EEG tokenizer — The 768 frozen tokenizer values of each person | 77.00% | 0.901 | 73.90% |
| Tokenizer + training-fold age residualization — The tokenizer values with age regressed out inside each training fold | 72.00% | 0.798 | 71.34% |
| Frozen GPT hidden states — The 768 frozen GPT hidden-state values of each person | 84.00% | 0.902 | 84.43% |
| GPT hidden states + training-fold age residualization — The GPT hidden-state values with age regressed out inside each training fold | 71.00% | 0.794 | 75.92% |

**NeuroLM.** NeuroLM’s preprocessing differs from the spectral pipeline: the two 2,000 Hz recordings are resampled to 200 Hz, then a 0.1–75 Hz band-pass, 50 and 60 Hz notches and division by 100. A difference between the two pipelines therefore mixes preprocessing with architecture. The checkpoint’s training stage and its prior exposure to these participants are unknown.

[NeuroLM source code at revision `0cda9876` ↗](https://github.com/935963004/NeuroLM/tree/0cda9876d8ce6ee07ed0c43eee5e9a6f5c24b177) · checkpoint SHA-256 `ffe098bc138b89f8817d3710a3604498d8ecd15135080e2ca27735d05c6d29ab`

Research results on a public data set. Not a diagnosis, not diagnostic accuracy, and not a medical device or medical advice.

Limits of this pilot

### One small development cohort

A small awake-only case-control development cohort from one institution. Labels are each participant’s case or control status, with no timed spasm events. There is no external or clinical validation, and the publisher’s full benchmark is outside this pilot.

### Regressing out age

Regressing out age inside each training fold assigns no share of the score to age, does not show that confounding is gone, and can also remove disease-related signal that varies with age.

### Recording settings

Recording settings (channel count and sampling rate) are a no-EEG diagnostic control, shown beside the EEG rows and kept out of model comparisons.

### NeuroLM, frozen

NeuroLM is used as a frozen feature extractor read by supervised logistic probes: its weights are unchanged, and there is no text prompt, text generation or zero-shot diagnosis.

### Independently reproduced

Spectral and control arms: 30 models (25 refits and 5 prior checks) and 600 held-out probabilities. An independent program refit every learned model, checked every prior, rebuilt every held-out probability with zero difference, and recomputed the features of one recording per channel-count and sampling-rate group with its own EDF reader and FFT.

NeuroLM arms: 20 probe refits, 400 held-out probabilities, 300 input files and 100 feature files. An independent program refit every probe and rebuilt every held-out probability with zero difference, checked every input file’s hash, and checked each feature file’s hash, layout and finiteness.

An independent read-only audit checked every input file’s hash, timing and calibration: 300 files, 2,884,494,142 bytes; all 100 recordings passed, with none held and none with gaps. Endpoint and constant-sample counts were kept as descriptions; no recording was excluded on them.

### IESSEEG · awake case-control pilot

Dataset: IESSEEG: An Open EEG Dataset Towards Better Understanding of Infantile Epileptic Spasms Syndrome, Hugging Face Capur/IESSEEG at revision 1c32ee3a, CC BY 4.0 for the dataset and annotations. The release card declares that the 400 case clinician-selected segments were previously analysed by Rajaraman et al. (2024), Computational EEG attributes predict response to therapy for epileptic spasms.

The release card states institutional review board approval of the retrospective study, with waivers of informed consent and HIPAA research authorization, and asks that no one attempt to re-identify participants. The publication boundary is the one the Parkinson’s comparison uses: aggregate scores only, with no participant rows, predictions, ages or per-group age and sex composition; the age ≤2 column gives the band’s size and its case and control counts. The card gives the approval as protocol 17-000026 and does not name the institution whose board approved it.

The participant-level crosswalk between that analysis and this release is unresolved; the cohort adds no dataset to any count.

[Dataset at the pinned revision ↗](https://huggingface.co/datasets/Capur/IESSEEG/tree/1c32ee3a425a324808a9d275b60ce85625af35c8) · [Release card ↗](https://huggingface.co/datasets/Capur/IESSEEG/blob/1c32ee3a425a324808a9d275b60ce85625af35c8/README.md) · [LICENSE ↗](https://huggingface.co/datasets/Capur/IESSEEG/blob/1c32ee3a425a324808a9d275b60ce85625af35c8/LICENSE) · [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) · [Data use](https://bci.report/data-use/#sources-clinical-iesseeg)

Data source: [reviewed aggregate JSON](https://bci.report/data/clinical-iesseeg-update.json) · schema bci-report-clinical-iesseeg-update-v1.

Schizophrenia case-control comparison · resting EEG

## Does resting EEG separate schizophrenia from controls better than age and sex do?

ASEEG is a public resting-EEG dataset from one clinic: 101 people with eyes-closed recordings, 51 with schizophrenia and 50 controls, and eyes-open recordings for 97 of the same people (47 and 50). Each recording is read for its first 256 s on its 19 channels as released. Every arm is scored out of fold on 5 stratified participant folds, averaged over 10 split seeds.

In the eyes-closed recordings, EEG spectra reached 71.19% balanced accuracy and the comparator given only age and sex 71.24%: a difference of −0.05 pp (−11.51 to +11.86 pp). EEG spectra with age and sex reached 75.94%, a difference of +4.71 pp from that comparator (−4.40 to +13.86 pp). With age and sex regressed out inside each training fold, EEG spectra reached 69.81% (62.27%–77.11%).

Seed-averaged out-of-fold scores with descriptive 95% paired participant-bootstrap intervals; chance is 50.00% for balanced accuracy and 0.5 for AUROC. Balanced accuracy is the primary metric; AUROC is shown beside it for every arm.

### Eyes closed, 101 people

Four arms on the eyes-closed recordings

| Arm | Balanced accuracy (95% interval) | AUROC |
| --- | --- | --- |
| Without EEG |  |  |
| Only age and sex — No EEG: age in years and sex | 71.24% (95% interval 62.61%–79.71%) | 0.694 |
| With EEG |  |  |
| EEG spectra — The 95 log relative band-power features | 71.19% (95% interval 63.29%–78.71%) | 0.775 |
| EEG spectra + age and sex — The 95 spectral features, age and sex | 75.94% (95% interval 68.64%–82.81%) | 0.833 |
| EEG spectra after age-and-sex residualisation — The 95 spectral features with age and sex regressed out inside each training fold | 69.81% (95% interval 62.27%–77.11%) | 0.775 |

Paired contrasts

| Contrast | Role | Difference in balanced accuracy (95% interval) | Interval excludes zero |
| --- | --- | --- | --- |
| EEG spectra − Only age and sex — The interval includes zero; the difference is not an equivalence test. | Primary | −0.05 pp (95% interval −11.51 to +11.86 pp) | No |
| EEG spectra + age and sex − Only age and sex — The interval includes zero. | Secondary | +4.71 pp (95% interval −4.40 to +13.86 pp) | No |
| EEG spectra after age-and-sex residualisation − chance — The interval lies above zero; regressing out linear effects inside training folds does not show that confounding is gone. | Sensitivity | +19.81 pp (95% interval +12.27 to +27.11 pp) | Yes |

### Frozen CBraMod, eyes closed

One arm reading frozen CBraMod with the same probe

| Arm | Balanced accuracy (95% interval) | AUROC |
| --- | --- | --- |
| Frozen CBraMod — The person-mean of CBraMod’s frozen embeddings, read by the same probe | 76.65% (95% interval 69.50%–83.37%) | 0.826 |

Paired contrasts

| Contrast | Role | Difference in balanced accuracy (95% interval) | Interval excludes zero |
| --- | --- | --- | --- |
| Frozen CBraMod − Only age and sex — The interval includes zero; the encoder has no age or sex input. | Frozen encoder | +5.41 pp (95% interval −4.92 to +15.93 pp) | No |
| Frozen CBraMod − EEG spectra — Descriptive; the interval includes zero. | Descriptive | +5.46 pp (95% interval −1.40 to +12.36 pp) | No |

CBraMod is read as a frozen encoder by the same person-level probe. Its contract differs from the foundation-model panels: the curators’ 128 Hz recordings are resampled to 200 Hz; each channel’s mean is removed in every 2-second window; the channels are in the released, undocumented order; the probe reads the mean of 128 window embeddings per person; and the microvolt scale comes from the descriptor’s Figure 2 caption, checked by a label-free plausibility test set in advance; the files store no unit. The checkpoint predates the dataset’s release and its stated pretraining corpus is TUEG; a difference between CBraMod and the spectral probe mixes preprocessing with architecture.

### Eyes open, 97 of the same people

Two arms on the eyes-open recordings

| Arm | Balanced accuracy (95% interval) | AUROC |
| --- | --- | --- |
| Only age and sex, eyes open — No EEG: age in years and sex | 69.91% (95% interval 60.96%–78.68%) | 0.676 |
| EEG spectra, eyes open — The 95 features of the eyes-open recording | 70.67% (95% interval 63.08%–77.93%) | 0.723 |

Paired contrasts

| Contrast | Role | Difference in balanced accuracy (95% interval) | Interval excludes zero |
| --- | --- | --- | --- |
| EEG spectra, eyes open − Only age and sex, eyes open — The interval includes zero. | Second condition | +0.75 pp (95% interval −10.64 to +11.90 pp) | No |

These are the same people as above, so this is not an independent replication.

Research results on a public data set. Not a diagnosis, not diagnostic accuracy, and not a medical device or medical advice.

Limits of this section

One clinic and one device; the recordings were taken retrospectively from the clinic’s records, and the controls were people having routine EEG there for reasons unrelated to mental health. A group-level research comparison.

The data descriptor reports that the groups differ in sex; the age-and-sex comparators and the residualised arm are shown, and residualising removes linear effects inside training folds only.

Medication and clinical scores are not released, so the patients’ treatment travels with the label.

The curators band-passed the signals to 1–45 Hz, notched them, downsampled them to 128 Hz and removed recordings with severe artefacts or incomplete sessions; transient artefacts remain by design.

The released demographic sheet differs slightly from the descriptor’s summary and is used as released; ages and sexes pair with recordings by the shared subject number, which the arms combining EEG with age and sex rely on.

The channel order is undocumented, so no channel-specific reading is possible.

Every value is the mean over ten split seeds of five-fold participant-held-out cross-validation; intervals are descriptive paired participant-bootstrap ranges that ignore the overlap of training folds and carry no multiplicity correction.

### How this differs from the Parkinson’s and IESSEEG sections

- ten split seeds averaged
- 10,000 bootstrap draws
- the first 256 s in 64 four-second windows
- 128 Hz as released, with 2-second Welch segments and 0.5 Hz bins
- 19 channels on the curators’ ear reference, in an undocumented order
- five relative bands, no log total power
- age in years and sex as the confounds, with no recording-setting arm
- each channel’s mean removed in every 2-second CBraMod window, and no age-and-sex-residualised CBraMod arm

### Independently reproduced

- **Pass**: The arms, contrasts, folds, split seeds and bootstrap were fixed in a protocol that an independent check passed before any score using the real labels existed.
- **Pass**: The out-of-fold predictions were sealed by SHA-256 before the independent audit read them.
- **Pass**: An independent program re-derived every spectral feature from the raw files, refitted every arm and re-extracted every CBraMod embedding, with zero difference.
- **Pass**: An independent reader checked every reading printed here against the frozen reading rules.

### ASEEG · Atieh Schizophrenia EEG

Bagherzadeh S, Norouzi M, Farokhshad F, Tolou Koroushi P, Ghasri A, Bahri Hampa S, Kazemi R, Rostami R, Shalbaf A. ASEEG: Atieh Schizophrenia EEG. Zenodo, doi:10.5281/zenodo.18029536 (First version, 2025-12-23), CC BY 4.0. Data descriptor: Scientific Data 13:1211 (2026), doi:10.1038/s41597-026-07968-9. Modifications: the first 256 s of each released recording read as released; log relative band power and frozen CBraMod features; cohort-level scores only. Derived analysis by BCI Report; not endorsed by the authors.

The data descriptor states that the ethics review board at Shahid Beheshti University of Medical Sciences approved the study, under ethics code IR.SBMU.MSP.REC.1404.441, and that specific informed consent covered the recording and the sharing of de-identified data, with written authorization from a parent, guardian or responsible adult where needed. The publication boundary is the one the Parkinson’s and IESSEEG sections use: aggregate scores only, with no participant rows or predictions and no per-group age or sex composition.

**The data descriptor says:** “The participant group was sourced from the Atieh Clinic, a private facility located in Tehran, Iran.” “EEG signals were obtained retrospectively from the clinic’s medical records.” “All studies and activities are conducted under the ethics code number IR.SBMU.MSP.REC.1404.441 and adhere to the principles of ethical research.” “specific informed consent was obtained for the EEG recording procedure and for the subsequent sharing of de-identified data.” “a parent, legal guardian, or other directly responsible adult provided written authorization on a dedicated consent form.”

The data descriptor is CC BY-NC-ND 4.0, so only short quotes appear here.

CBraMod: code MIT, weights Apache-2.0 as its official model card states; not redistributed; Wang et al., arXiv 2412.07236.

[Zenodo record ↗](https://doi.org/10.5281/zenodo.18029536) · [Data descriptor ↗](https://doi.org/10.1038/s41597-026-07968-9) · [CBraMod, arXiv ↗](https://arxiv.org/abs/2412.07236) · [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) · [Data use](https://bci.report/data-use/#sources-clinical-aseeg)

Data source: [reviewed aggregate JSON](https://bci.report/data/clinical-aseeg-update.json) · schema bci-report-clinical-aseeg-update-v1.

Keep exploring

Transfer

- [— Sensor transfer **Dry vs. wet electrodes**](https://bci.report/topics/dry-vs-wet/)
- [— Context transfer **Screen to VR**](https://bci.report/topics/screen-to-vr/)
- [— Montage **Fewer electrodes**](https://bci.report/topics/fewer-electrodes/)
- [— Motion robustness **On the move**](https://bci.report/topics/on-the-move/)
- [— Session transfer · same person **Later sessions**](https://bci.report/topics/later-sessions/)

Adapting models

- [— Calibration budget **How much calibration?**](https://bci.report/topics/calibration-budget/)
- [— Model adaptation **Which part to update?**](https://bci.report/topics/model-adaptation/)
- [— Representation controls **Does pretraining help?**](https://bci.report/topics/does-pretraining-help/)
- [— Shared encoder · route 2 **One model, several questions**](https://bci.report/topics/shared-encoder/)
- [— Questions in language · route 3 **Questions in language**](https://bci.report/topics/questions-in-language/)
- [— Route 3 follow-ups **New SSVEP frequencies**](https://bci.report/topics/new-ssvep-frequencies/)

Reliability & clinical

- [— Abstention · decision research **When not to act**](https://bci.report/topics/when-not-to-act/)
- [— Sleep staging · simple baselines **Sleep-stage balance**](https://bci.report/topics/sleep-staging/)
- [— Clinical research **Clinical groups**](https://bci.report/topics/clinical-groups/)
- [— Data quality and reproducibility **Checked end to end**](https://bci.report/topics/data-quality/)

Other signals & pilots

- [— Other signals · human and macaque **Beyond scalp EEG**](https://bci.report/topics/beyond-scalp-eeg/)
- [— Pilot studies · small cohorts **Pilot studies**](https://bci.report/topics/pilot-studies/)

[All questions and the transfer-coverage map →](https://bci.report/topics/)

## Cite this page

BCI Report (2026). *Can resting-state EEG separate Parkinson's disease from controls?* https://bci.report/topics/clinical-groups/

Figures from releases [`clinical-aseeg-update-20261106`](https://bci.report/releases/#clinical-aseeg-update-20261106) (2026-11-06), [`clinical-iesseeg-update-20261020`](https://bci.report/releases/#clinical-iesseeg-update-20261020) (2026-10-20), [`clinical-update-20260923`](https://bci.report/releases/#clinical-update-20260923) (2026-09-23). Cite the upstream datasets as well: their credits are on this page.

Every release is archived on Zenodo: [doi:10.5281/zenodo.23123296](https://doi.org/10.5281/zenodo.23123296). [BibTeX for the site and its releases →](https://bci.report/api/#cite-heading) · [CITATION.cff ↗](https://raw.githubusercontent.com/twu3202/bci-report/main/CITATION.cff)

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