# Evidence you can inspect. Recordings stay with their sources.

SOURCES & RESPONSIBLE USE

BCI Report is a personal, noncommercial research project. It has no advertising, paid access, EEG upload form or diagnostic service.

**Downloads contain aggregate benchmark results and methods.**

They do not contain raw EEG, individual scores, participant identifiers, recordings, embeddings or model weights. Access original data through the credited source and follow that source’s terms.

## What a source review means

A source review checks the original release: its stated license, attribution requirements and available consent or ethics documentation. A Hugging Face mirror is a distribution source, and its metadata alone does not establish the uploader’s rights or participants’ consent. Numerical audits and permission to publish are separate checks.

“Aggregate results” means this project selected cohort-level outputs for the stated research scope. It implies no legal certification, permission to reuse raw data, verified anonymity of the source files or endorsement by the original researchers.

## Core and deployment sources

The deployment extension carries nine reviewed citation records: six for datasets already in the matrix or research register, and three for the wearable and mobile sources.

| Dataset | Source terms | Scope |
| --- | --- | --- |
| Stable 39-comparison matrix and research register |  |  |
| [ds003810 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) — Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/ | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate results** — Clear CC0 snapshot plus dataset-specific ethics and signed-consent evidence; aggregate-only publication materially limits privacy exposure. |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate results** — Attribution license and study-specific approval/consent are documented; eligibility assumes aggregate-only output and exclusion of subject-info fields. |
| [EESM19 scalp subset ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) — Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed. | [CC0-1.0 declared by upstream and mirror ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate results** — Clear upstream CC0 and study ethics evidence; use only the pinned scalp-signal derivative and publish aggregates, with provenance disclosed. Consent covered the study, not publication; the public release rests on a GDPR anonymization assessment (amended 2026-09-22). |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate results** — Eligible only under the stated personal, research-led, noncommercial operation. Any ads, sponsorship, fees, or use directed toward commercial advantage requires fresh review or permission. |
| [BNCI2014-008 ↗](https://bnci-horizon-2020.eu/database/data-sets/008-2014) — BNCI Horizon 2020 catalog · Original researchers credited at source. | [CC BY-NC-ND 4.0 ↗](https://creativecommons.org/licenses/by-nc-nd/4.0/) | **Not in this release** — Small clinical cohort and linked health attributes create disproportionate reidentification risk; aggregate-only output does not resolve the missing secondary-publication consent evidence. |
| [BNCI2014-009 ↗](https://bnci-horizon-2020.eu/database/data-sets/009-2014) — BNCI Horizon 2020 catalog · Original researchers credited at source. | [CC BY-NC-ND 4.0 ↗](https://creativecommons.org/licenses/by-nc-nd/4.0/) | **Not in this release** — License permits only noncommercial unadapted redistribution, and the consent/ethics chain for public secondary results remains unverified. |
| [stew-monster-processed ↗](https://ieee-dataport.org/open-access/simultaneous-task-eeg-workload-dataset) — monster-monash/STEW Hugging Face processed mirror · Original researchers credited at source. | [Mirror declares CC BY 4.0; exact upstream DataPort license not independently verified ↗](https://creativecommons.org/licenses/by/4.0/) | **Not in this release** — Ethics evidence is positive, but upstream license and the processed mirror's authority to apply CC BY remain unresolved. |
| [TMNRED / ds005383 ↗](https://doi.org/10.18112/openneuro.ds005383.v1.0.0) — Yanru Bai, Qi Tang et al. · TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments (2025), doi:10.1038/s41597-025-05036-2. OpenNeuro ds005383 v1.0.0. | [OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate results** — Strong study-specific open-sharing evidence. Aggregate metrics avoid redistribution of stimulus text and participant metadata; credit under CC BY. |
| [ds006593 ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) — OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate results** — Pinned CC0 release with dataset-specific IRB and consent evidence and comparatively low metadata exposure. |
| [physionet-eegmmidb-1.0.0 ↗](https://physionet.org/content/eegmmidb/1.0.0/) — PhysioNet EEG Motor Movement/Imagery Dataset 1.0.0 · Original researchers credited at source. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Not in this release** — Reuse license is clear, but the dataset-specific human-subject consent and ethics chain is not evidenced strongly enough for a new public benchmark release. |
| [BNCI2014-001 ↗](https://bnci-horizon-2020.eu/database/data-sets/001-2014) — BNCI Horizon 2020 catalog · Original researchers credited at source. | [CC BY-ND 4.0 ↗](https://creativecommons.org/licenses/by-nd/4.0/) | **Not in this release** — Per-dataset license is verified, but consent evidence and the application of ND to the planned publication pipeline should be clarified. |
| [BNCI2014-004 ↗](https://bnci-horizon-2020.eu/database/data-sets/004-2014) — BNCI Horizon 2020 catalog · Original researchers credited at source. | [CC BY-ND 4.0 ↗](https://creativecommons.org/licenses/by-nd/4.0/) | **Not in this release** — Per-dataset license is verified, but consent evidence and the application of ND to the planned publication pipeline should be clarified. |
| [ds005342 ↗](https://doi.org/10.18112/openneuro.ds005342.v1.0.3) — OpenNeuro ds005342 contributors · version 1.0.3, doi:10.18112/openneuro.ds005342.v1.0.3; associated study doi:10.3389/fninf.2022.961089. Original author credits are retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate results** — Eligible only as an aggregate-only case study: remove all participant IDs/results and condition breakdowns, label n=4 prominently, avoid subgroup claims, and make no claim that the cohort is anonymous. |
| Deployment-topic extension |  |  |
| [An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces ↗](https://figshare.com/articles/dataset/An_Open_Dataset_for_Wearable_SSVEP-Based_Brain-Computer_Interfaces/13560281) — Official Tsinghua BCI Lab author mirror snapshot acquired 2026-09-20; the mirror itself has no separate DOI or version number. | CC BY 4.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [Lee2021 – SSVEP paradigm of the Mobile BCI dataset ↗](https://nemar.org/dataset/nm000125) — NEMAR/MOABB-generated BIDS derivative | CC BY 4.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [ERP paradigm of the Mobile BCI dataset ↗](https://nemar.org/dataset/nm000201) — NEMAR/MOABB-generated BIDS derivative with an embedded sourcedata provenance record | CC BY 4.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — Processed mirror identifies the canonical Figshare record and repeats CC BY 4.0; upstream version is pinned separately. | CC BY 4.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [ds003810 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) — Direct OpenNeuro snapshot; no mirror in the reviewed acquisition path. | CC0-1.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [TMNRED / ds005383 ↗](https://doi.org/10.18112/openneuro.ds005383.v1.0.0) — Direct OpenNeuro snapshot; publication also points to author-hosted raw/preprocessed data. The license-label mismatch is conservatively resolved in favor of attribution. | OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [ds006593 ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) — Direct OpenNeuro snapshot; NEMAR independently identifies its copy as derived from this OpenNeuro record. | CC0-1.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [EESM19 scalp subset ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) — Processed H5 mirror derived from OpenNeuro ds005185. The mirror card agrees on CC0, but the derived-file transformation is described by the mirror rather than independently certified by upstream. | CC0-1.0 declared by upstream and mirror | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — Direct pinned PhysioNet release. | Open Data Commons Attribution License 1.0 | **Aggregate topic results** — Participant-level scores, demographics and recordings stay outside the site. |

## Sources added 22 September 2026

Each sits outside the eight-protocol matrix, with its own recorded editorial decision and aggregate export. This and every later batch on this page has its own review manifest, whose SHA-256 the releases page lists.

| Dataset | Source terms | Consent and privacy |
| --- | --- | --- |
| [EESM23 · Ear-EEG Sleep Monitoring 2023 ↗](https://doi.org/10.18112/openneuro.ds005178.v1.0.0) — Dataset: Yousef Rezaei Tabar, Kaare Mikkelsen, Laura Birch, Nelly Shenton, Simon L. Kappel, Astrid R. Bertelsen, Reza Nikbakht, Hans O. Toft, Chris H. Henriksen, Martin C. Hemmsen, Mike L. Rank, Marit Otto and Preben Kidmose · Ear-EEG Sleep Monitoring 2023 (EESM23), OpenNeuro ds005178 v1.0.0, doi:10.18112/openneuro.ds005178.v1.0.0. Study: Kaare Bjarke Mikkelsen, Yousef Rezai Tabar, Laura Rævsbæk Birch, Simon Lind Kappel, Christian Bech Christensen, Lars Dalskov Mosgaard, Marit Otto, Martin Christian Hemmsen, Mike Lind Rank and Preben Kidmose · Ear-EEG sleep monitoring data sets, Scientific Data 12, 301 (2025), doi:10.1038/s41597-025-04579-8. Processed export: Zachary1150/EESM23-Processed on Hugging Face, which did not create the original cohort. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | — All subjects gave written, informed consent. Approved by the Central Denmark Region Committees on Biomedical Research Ethics (1-10-72-13-20) and the Danish Medicines Agency (2020012619). Publication was not mentioned in the informed consent form; before release, the GDPR office of Region Midt judged the data set fully anonymized. Consent therefore covered the study, and the public release rests on that anonymization judgment. Only cohort aggregates are published here: no per-person scores, no per-person distribution percentiles, no per-night or per-epoch values. |
| [Phantom EEG with motion, muscle and eye artifacts ↗](https://doi.org/10.18112/openneuro.ds004784.v1.0.4) — Ryan J. Downey and Daniel P. Ferris · Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts, OpenNeuro ds004784 v1.0.4, doi:10.18112/openneuro.ds004784.v1.0.4. Method: doi:10.3390/s23198214. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | — One electrically conductive physical head phantom. No human participants are represented, so consent and ethics review do not apply; no population inference is made. |
| [EEG Alpha Waves dataset ↗](https://doi.org/10.5281/zenodo.2605110) — Grégoire Cattan, Pedro L. C. Rodrigues and Marco Congedo · EEG Alpha Waves dataset, Zenodo, doi:10.5281/zenodo.2605110. Report: Grégoire Cattan, Pedro Luiz Coelho Rodrigues and Marco Congedo · EEG Alpha Waves Dataset, research report, GIPSA-lab, Grenoble, 2018, hal-02086581. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | — The primary report states that all participants provided written informed consent confirming the notification of the experimental process, the data management procedures and the right to withdraw from the experiment at any moment. It names no ethics committee or approval number, and it does not say whether public release was described in the consent; the authors released the recordings openly under CC BY 4.0. The source also ships a per-person age, gender and fatigue table; this project did not download it. Only cohort aggregates are published here: no per-person scores, no per-person distribution percentiles, no per-recording values. |

**Amended 22 September 2026 — EESM19.** Its 2025 data descriptor, which covers EESM23 as well, states that publication was not mentioned in the informed consent form. The public release rests on the GDPR office of Region Midt judging the data fully anonymized. No score, cohort or decision changed.

## Sources added 23 September 2026

Outside the eight-protocol matrix: one published as aggregate scores, two as status only.

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [OpenNeuro ds004584 · Rest eyes open, Parkinson's disease and controls ↗](https://doi.org/10.18112/openneuro.ds004584.v1.0.0) — 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. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Every participant gave written informed consent, and decisional capacity was established, in accordance with the Declaration of Helsinki; all research protocols were approved by the University of Iowa Human Subjects Review Board (IRB 201707828). One site, the University of Iowa Movement Disorders Clinic. The recordings are released CC0 by the authors. This is a clinical population, so the publication boundary is tighter than elsewhere on this site: only two model-level scores with descriptive intervals are published. No participant rows, per-person predictions, clinical or cognitive scores, medication state, or per-group age and sex composition — the last of these is in the reviewed inputs and is deliberately left out, because this site does not publish demographic profiles of a diagnosis-specific group. Nothing published here is diagnostic, and no participant can be distinguished in it. |
| [OpenNeuro ds004902 · Paired sleep-condition recordings ↗](https://openneuro.org/datasets/ds004902/versions/1.0.8) — OpenNeuro ds004902 v1.0.8, cited as the pinned release while this hold stands. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Status only — no scores** — Downloaded in full and identity-verified, but five FDT payloads in the canonical release are shorter than their own headers and sidecars declare, and all five end inside an incomplete channel frame. The frozen 71-person paired protocol cannot pass its intake gate, so there is no score of any kind. Padding, truncating to the shortest common length, or reporting a partial cohort under the frozen protocol were all refused. What is published is the status and the source-defect counts; no accuracy row exists to publish. |
| [L-FAME · six-person expansion ↗](https://huggingface.co/datasets/L-FAME-Dataset-Benchmark/L-FAME/tree/53f4a329eed86a0053b2150cc4e754406662b5a3) — L-FAME Dataset Benchmark · L-FAME, Hugging Face, at revision 53f4a329eed86a0053b2150cc4e754406662b5a3. Noncommercial scope; attribution required; the source card forbids re-identification attempts. | [CC-BY-NC-4.0 ↗](https://creativecommons.org/licenses/by-nc/4.0/) | **Status only — no scores** — An audited relative-spectrum description exists for 60 recordings from 6 people, and it is not a model result. Its per-condition numbers are withheld: the descriptors are summarised over 12 recordings per condition from those 6 people, so a minimum or maximum is one person's recording, which this site does not publish. Electrode-row mapping and physical units also remain unresolved, so a spectral profile cannot be read as a biological finding. The published record is the status and the cohort size. |

**A clinical cohort is held to a higher bar.** Research results on a public data set. Not a diagnosis, not diagnostic accuracy, and not a medical device or medical advice. Not published, although both are aggregates in the reviewed inputs: the per-group age means and sex counts of a diagnosis-specific cohort, which is the demographic profile this policy excludes, and the per-condition band descriptors of the six-person L-FAME expansion, where a range bound is one person’s recording. The export code refuses all of them by field name.

What each batch could not publish is in the [holds register](https://bci.report/releases/#holds), with the date each hold was opened and, where it was, resolved.

## Sources added 27 September 2026

Three published as aggregate scores, one as status only. The asynchronous SSVEP pilot was held until its data paper’s consent and ethics statement had been read, and released the same day. All four are fixed CPU baselines.

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [Cattan PC/VR P300 · a BCI experiment in virtual reality and on a personal computer ↗](https://doi.org/10.5281/zenodo.2605205) — Grégoire Cattan, Anton Andreev, Pedro L. C. Rodrigues and Marco Congedo · Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer, Zenodo, doi:10.5281/zenodo.2605205; documentation arXiv:1903.11297. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The documentation reports that all 21 participants gave written informed consent covering the experimental process, the data management procedures and the right to withdraw at any moment, and that the study was approved by the Ethical Committee of the University of Grenoble Alpes (Comité d'Ethique pour la Recherche Non-Interventionnelle). Only cohort aggregates are published: means and participant-bootstrap intervals. The per-person minimum, maximum, median and quartiles in the reviewed inputs are dropped, because with 21 people each of them is one person's score. |
| [Multimodal gait · EEG during treadmill walking at three speeds ↗](https://doi.org/10.13026/r0ea-7161) — R. Katmah, A. AlShehhi, D. Kosaji, N. Al-Rahmani, M. Abdullah, A. A. V. Hulleck and K. Khalaf · A multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults, PhysioNet v1.0.0 (2026), doi:10.13026/r0ea-7161. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The PhysioNet record states that the study followed the Declaration of Helsinki, was approved by the Institutional Review Board of Khalifa University (protocol H19-038), and that all participants gave written informed consent. Only cohort aggregates are published: two model-level scores with participant-bootstrap intervals. No per-person or per-recording value, and no participant metadata. |
| [YSU asynchronous SSVEP-BCI dataset ↗](https://doi.org/10.6084/m9.figshare.24906300.v3) — Jing Zhao, Qian Zhang, Xinrui Wang, Xueshuo Liu, Jiaxin Li, Fengjie Fan, Zhenhu Liang and Xiaoli Li · An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces, Brain-Apparatus Communication 3(1) (2024), doi:10.1080/27706710.2024.2418650; data at doi:10.6084/m9.figshare.24906300.v3. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The data descriptor (section 2.1, Subjects) reports that before the experiment all 24 subjects thoroughly understood the experimental procedures and signed written informed consent, and that the study was reviewed and approved by the ethics committee of Qinhuangdao First Hospital. This project uses a four-person pilot of the release (the first four participant archives). Only pooled window counts are published. The per-participant ranges in the reviewed inputs are dropped: with four people, each bound is one person's rate. |
| [Stieger longitudinal BCI · one-person pilot ↗](https://doi.org/10.6084/m9.figshare.13123148.v1) — James R. Stieger, Stephen A. Engel and Bin He · Continuous sensorimotor rhythm based brain computer interface learning in a large population, Scientific Data 8, 98 (2021); data at doi:10.6084/m9.figshare.13123148.v1. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Status only — no scores** — One person's eleven sessions were acquired, not the 62-person release. With one person, every score is that person's, which this site does not publish; the execution handoff also asks for no ranked score and no session table. What is published is that the loader and a chronological split work on this source, and how many trials meet the source's own eligibility rules. |

## Reviewed 1 October 2026

No new dataset: the LaBraM adaptation results reuse the 2026-09-20 review of the EEGMAT recordings unchanged, and the LaBraM Base checkpoint’s MIT-licensed repository was reviewed for aggregate evaluation on the same date. The checkpoint, adapters and trained heads are not redistributed. The second experiment ran on a source whose editorial hold stands, so it is listed as status only.

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [EEGMAT · LaBraM adaptation, three matched update rules ↗](https://physionet.org/content/eegmat/1.0.0/) — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate scores** — Paper reports Bioethics Commission approval (15 August 2018) and written consent from every participant. EDF dates are normalized, but subject-info.csv contains age, gender, occupation, and recording date; those fields must not be published. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: cohort means, descriptive person-bootstrap intervals, per-seed cohort means, and counts of people helped, harmed or unchanged out of 36. No per-person or per-fold value. |
| [BNCI2015-001 · same person, the next day ↗](https://bnci-horizon-2020.eu/database/data-sets) — Institute for Knowledge Discovery, Graz University of Technology · BNCI Horizon 2020 data set 001-2015, Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. | [CC BY-NC-ND 4.0 (as displayed by the official catalogue) ↗](https://creativecommons.org/licenses/by-nc-nd/4.0/) | **Status only — no scores** — Run and independently replayed on 2026-09-22. The source's 2026-09-20 editorial hold stands: the official catalogue applies CC BY-NC-ND 4.0, and the dataset description gives written informed consent but no ethics-board approval. Status only until that review is complete; the hold is not a finding that aggregate statistics are prohibited. |

Two pieces of released wording were also corrected on this date, a method description and a dataset credit, without changing any released file; both are listed on the [releases page](https://bci.report/releases/#corrections).

## Reviewed 2 October 2026

Both published as aggregate scores. The asynchronous SSVEP extension scores the remaining twenty people of the same figshare release and reuses by reference the consent and ethics record read from its data paper for the 27 September pilot. LTRSVP is a new source: the PhysioNet record carries no consent statement, so it was read from the original publication, which reports approval by the Ethics Committee of the University of Essex and written consent from every participant. Both are fixed classical baselines. Not published: the independent audits behind both, which carry private storage paths and are pinned by hash in the review manifest instead.

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [YSU asynchronous SSVEP-BCI dataset ↗](https://doi.org/10.6084/m9.figshare.24906300.v3) — Jing Zhao, Qian Zhang, Xinrui Wang, Xueshuo Liu, Jiaxin Li, Fengjie Fan, Zhenhu Liang and Xiaoli Li · An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces, Brain-Apparatus Communication 3(1) (2024), doi:10.1080/27706710.2024.2418650; data at doi:10.6084/m9.figshare.24906300.v3. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Same release, same consent record: the data descriptor (Brain-Apparatus Communication 3(1), 2024, section 2.1, Subjects), read by the maintainer for the 2026-09-27 release, reports that all 24 subjects understood the procedures and signed written informed consent, and that the study was reviewed and approved by the ethics committee of Qinhuangdao First Hospital. Reused by reference from the 2026-09-27 manifest, not re-read. Published here: cohort means with participant-bootstrap intervals, pooled window counts, and the number of people helped, harmed or tied out of twenty. The per-participant ranges in the aggregate are dropped: each bound is one person's rate. |
| [LTRSVP · EEG Signals from an RSVP Task ↗](https://physionet.org/content/ltrsvp/1.0.0/) — Ana Matran (University of Essex) · EEG Signals from an RSVP Task 1.0.0, PhysioNet (2017), doi:10.13026/C2KX0P. Original publication: Ana Matran-Fernandez and Riccardo Poli, Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces, PLoS ONE 12(5): e0178498 (2017), doi:10.1371/journal.pone.0178498. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate scores** — The original publication (Materials and methods, Participants and setup) states that the study received approval from the Ethics Committee of the University of Essex and that consent was obtained from all participants in written form before the experiment began; its data availability statement names this PhysioNet record. The PhysioNet record itself carries no consent statement. Published here: participant-mean balanced accuracy and AUROC for each of the nine training-rate and test-rate cells with participant-bootstrap intervals, one paired difference, and how many of the nine people were lower, higher or tied. No per-person, per-recording or per-image value, and none of the ages, sexes or handedness the paper reports. |

## Reviewed 3 October 2026

Both published as aggregate scores, each with two fixed classical baselines. The Dreem Open Datasets are a new source. The licence recorded is MIT, as declared in the Zenodo deposit’s archived publisher metadata. The upstream code licence could not be fetched, and nothing upstream is redistributed. The dataset paper gives its two cohorts different statements, and each is published as it stands (owner decision, 3 October 2026): for DOD-H the paper names an ethics approval and has no informed-consent sentence; for DOD-O it states written informed consent and names no ethics committee — the same basis on which Alpha Waves was published on 22 September. The physical units of the Dreem recordings are unresolved (the stored metadata says millivolts, the publisher’s converter microvolts), so models that need absolute amplitude are held. OpenBMI is the second new source: CC0 1.0 per the DataCite record GigaDB registered and GigaDB’s repository policy (the GPL-3.0 seen in third-party catalogues is the licence of the OpenBMI code, not of the data), and the paper states approval by the Korea University Institutional Review Board and written informed consent. Not published: the independent audits behind both, which carry private storage paths and are pinned by hash in the review manifest instead.

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [Dreem Open Datasets (DOD-H and DOD-O) ↗](https://zenodo.org/records/15900394) — Antoine Guillot, Fabien Sauvet, Emmanuel H. During and Valentin Thorey · Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging, IEEE Transactions on Neural Systems and Rehabilitation Engineering (2020), doi:10.1109/TNSRE.2020.3011181; preprint arXiv:1911.03221. Data: Dreem Open Datasets, Zenodo, doi:10.5281/zenodo.15900394. Official repository: github.com/Dreem-Organization/dreem-learning-open, revision 8b332d6827f5ae6a22f4bb97b4deef4273238ec3. | [MIT ↗](https://opensource.org/licenses/MIT) | **Aggregate scores** — The two cohorts carry different statements, and each is published as it stands. DOD-H, 25 healthy volunteers recorded at the French Armed Forces Biomedical Research Institute (IRBA): the paper states that the study was approved by the Committees of Protection of Persons (CPP), declared to the French National Agency for Medicines and Health Products Safety, and carried out in compliance with the French Data Protection Act, ICH standards and the Declaration of Helsinki; its DOD-H description has no informed-consent sentence. DOD-O, 55 people with obstructive sleep apnoea recorded at the Stanford Sleep Medicine Center (clinical trial NCT03657329): the paper states that all trial participants gave informed written consent before taking part; no ethics committee or review board is named for DOD-O. The authors present both as publicly available datasets, and the deposit declares open access. Published here: cohort-level participant-equal means with whole-night bootstrap intervals, cohort-level stage support counts, and paired differences. No per-night value, record name, fold assignment or demographic. |
| [OpenBMI motor imagery (Lee et al. 2019) ↗](https://doi.org/10.5524/100542) — Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli and Seong-Whan Lee · EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy, GigaScience (2019), giz002, doi:10.1093/gigascience/giz002. Data: Supporting data, GigaScience Database, doi:10.5524/100542. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — The paper's Ethical Approval section states that the study was approved by the Korea University Institutional Review Board (1040548-KUIRB-16-159-A-2) and that written informed consent was obtained from all participants before the experiments. Published here: participant-equal means with whole-participant bootstrap intervals for each arm and calibration budget, paired contrasts, and how many of the 51 people declined. The release's per-person summaries (worst observed change, medians, 10th percentiles) are dropped, and no participant, session file or trial identifier is published. |

**Dreem, cohort by cohort, as the paper states it.** DOD-H — ethics approval: Approved by the Committees of Protection of Persons (CPP); declared to the French National Agency for Medicines and Health Products Safety; carried out in compliance with the French Data Protection Act, International Conference on Harmonization (ICH) standards and the Declaration of Helsinki (1964, as revised in 2013). Informed consent: not stated. The paper's DOD-H description contains no informed-consent sentence. DOD-O — ethics approval: not stated. No ethics committee or institutional review board is named for DOD-O in the paper. Informed consent: All trial participants gave informed written consent before taking part. Read from arXiv:1911.03221v4 (27 April 2020), II. Materials and Methods, A. Datasets, read 2026-10-03.

## Reviewed 4 October 2026

Two reviews: route 1 of the decision-research roadmap, and the foundation-model evaluation.

### Route 1, reliable decisions

Route 1 adds no new dataset and no new model: it rescored the saved test scores of models this site already publishes on EEGMAT, BETA and ds003810, whose rights records from the 2026-09-20 review are reused unchanged, and every full-coverage accuracy equals the published one. Published: pooled coverage and selective error, probability quality and paired contrasts with whole-person bootstrap intervals, and counts of people and of outer folds. Not published: per-trial, per-person and per-fold values, including the person-level percentiles and the median temperature over folds the release candidate carries; the route’s idle-protocol figures, which are in another unit; every figure of its BNCI2015-001 arm, whose editorial hold stands; and the audits behind the release, which carry private storage paths and are pinned by hash in the review manifest instead. ds003810 is shown as crude.

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate scores** — Paper reports Bioethics Commission approval (15 August 2018) and written consent from every participant. EDF dates are normalized, but subject-info.csv contains age, gender, occupation, and recording date; those fields must not be published. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: pooled coverage and selective error, probability-quality levels and paired contrasts with whole-person bootstrap intervals, and counts of people (with zero accepted windows, with a degenerate personal temperature) out of 36. No per-person, per-fold or percentile value. |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Paper reports Tsinghua University ethics approval 20190002 and written consent; parents consented for participants under 16. Ages span 9-64. Only cohort aggregates should be shown. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: pooled coverage and selective error, probability-quality levels and paired contrasts with whole-person bootstrap intervals, counts of people out of 70 and counts of outer folds out of 7. No per-person, per-fold or percentile value. |
| [ds003810 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) — Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/ | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Study paper reports ethics approval by CEySTE CCT-CONICET Santa Fe, Declaration of Helsinki compliance, and signed consent. Twelve adults participated; ten were retained. Public participants.tsv includes exact age, sex, handedness, and pseudonymous ID, which must not be surfaced. Reused unchanged from the 2026-09-20 review of the same recordings. Published here, in a panel labelled crude: the published balanced accuracy and the error when everything is accepted; under the fixed threshold, pooled coverage and the selective error (a count of wrong among accepted, flagged unstable, where fewer than ten were accepted), each with its whole-person bootstrap interval where defined, and how many of the ten people had nothing accepted; the learned-reject contrasts with whole-person bootstrap intervals; and the certified-risk rule's settings (delta and target) and fold counts, with nothing accepted: a coverage of zero, all ten people with nothing accepted, and so no error and no error relative to the target. Two people per test fold, so no per-fold block and no person-level spread. |

### Foundation models

No new dataset, but new models. Eleven further EEG foundation models — sixteen encoder checkpoints — were run as frozen probes on the eight core protocols with the published recipe and only the encoder swapped, and nine of them were adapted on EEGMAT. The seven datasets behind those protocols keep their rights records from the 2026-09-20 review, reused unchanged. The new models’ weights carry their own terms, reviewed on the same date and listed below with the owner’s decision for each: the REVE Responsible Use License, accepted by the owner; LUNA’s CC BY-ND 4.0, approved for internal probing and adaptation with aggregate scores only and no endorsement implied; ERP-FM’s CC BY-NC-SA 4.0, non-commercial; and ZUNA 1.1’s model card, research use only, not for diagnosis or clinical use. No weights, adapted weights or LoRA deltas were kept or shared, and no model’s authors endorse these results. Pretraining exposure is what each model’s authors publish as their pretraining list, checked on 4 October 2026 and linked on the protocol and method pages: not proof that a recording was never seen. Not published: per-trial, per-person and per-fold values; features; timings and memory, which were measured on a shared GPU; and the aggregate, the group summaries and the six independent audits, which carry private storage paths and are pinned by hash in the review manifest instead. Release [`foundation-models-update-20261004`](https://bci.report/releases/#foundation-models-update-20261004).

| Dataset · protocol | Source terms | What is published |
| --- | --- | --- |
| [ds003810 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) — Motor imagery & rest · [mi-rest](https://bci.report/protocols/mi-rest/#foundation-v9) — Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/ | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Study paper reports ethics approval by CEySTE CCT-CONICET Santa Fe, Declaration of Helsinki compliance, and signed consent. Twelve adults participated; ten were retained. Public participants.tsv includes exact age, sex, handedness, and pseudonymous ID, which must not be surfaced. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [ds005342 ↗](https://doi.org/10.18112/openneuro.ds005342.v1.0.3) — Idle & command · [idle](https://bci.report/protocols/idle/#foundation-v9) — OpenNeuro ds005342 contributors · version 1.0.3, doi:10.18112/openneuro.ds005342.v1.0.3; associated study doi:10.3389/fninf.2022.961089. Original author credits are retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — README reports voluntary signed consent and Universidad Antonio Nariño ethics approval. participants.tsv links pseudonymous IDs to exact age, sex, handedness, and academic major. The current evaluated subset has only four people. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, test-trial counts of idle false activations (of 60) and commands detected within 3 s (of 60), how many of the 4 people always abstained, and the cohort-mean window balanced accuracy and AUROC. No interval, no per-person or per-trial value. |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — SSVEP · 8 channels · [beta-8ch](https://bci.report/protocols/beta-8ch/#foundation-v9) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Paper reports Tsinghua University ethics approval 20190002 and written consent; parents consented for participants under 16. Ages span 9-64. Only cohort aggregates should be shown. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — SSVEP · 4 channels · [beta-4ch](https://bci.report/protocols/beta-4ch/#foundation-v9) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Paper reports Tsinghua University ethics approval 20190002 and written consent; parents consented for participants under 16. Ages span 9-64. Only cohort aggregates should be shown. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — Arithmetic & rest · [arithmetic-rest](https://bci.report/protocols/arithmetic-rest/#foundation-v9) — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate scores** — Paper reports Bioethics Commission approval (15 August 2018) and written consent from every participant. EDF dates are normalized, but subject-info.csv contains age, gender, occupation, and recording date; those fields must not be published. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. The EEGMAT adaptation rows add three-seed cohort means with participant-bootstrap intervals, per-seed cohort means, paired changes with how many of the 36 people were helped, harmed or tied, and trainable-parameter counts. |
| [ds006593 ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) — P300 target ERP · [p300-target](https://bci.report/protocols/p300-target/#foundation-v9) — OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Dataset metadata/README report IRB approval #10-05-2022 and written informed consent. Participant metadata contains pseudonymous IDs and study descriptors without demographic fields. Eye-tracking and raw signals remain off-site. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [TMNRED / ds005383 ↗](https://doi.org/10.18112/openneuro.ds005383.v1.0.0) — Semantic target ERP · [semantic-target](https://bci.report/protocols/semantic-target/#foundation-v9) — Yanru Bai, Qi Tang et al. · TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments (2025), doi:10.1038/s41597-025-05036-2. OpenNeuro ds005383 v1.0.0. | [OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Paper reports Tianjin University ethics approval TJUE-2024-402, written consent, deidentification, and explicit participant permission for open sharing. Local participants.tsv appears column-shifted/malformed, so it must not feed public metadata. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [EESM19 scalp subset ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) — Sleep staging · [sleep-scalp](https://bci.report/protocols/sleep-scalp/#foundation-v9) — Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed. | [CC0-1.0 declared by upstream and mirror ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Paper reports Central Denmark Region ethics approval 1-10-72-413-17, Danish Medicines Agency approval 2017111085, registration, and written consent. Upstream includes exact age/sex plus sleep diaries, questionnaires, actigraphy, and timing; none should be exposed. Amended 2026-09-22: the 2025 data descriptor covering EESM19 and EESM23 (Mikkelsen et al., Scientific Data 12, 301, doi:10.1038/s41597-025-04579-8) states that publication was not mentioned in the informed consent form; before release, the GDPR office of Region Midt judged the data fully anonymized. Consent therefore covered the study, and the public release rests on that anonymization judgment. Reused unchanged from the 2026-09-20 review of the same recordings. Published here: for each new frozen encoder, the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |

The new models’ weights, one entry per model family, with every checkpoint and its revision:

| Model and checkpoints | Weights licence and terms | Owner’s review, 4 October 2026 |
| --- | --- | --- |
| [REVE Base · REVE Large](https://bci.report/methods/reve/) — REVE Base: `brain-bzh/reve-base @ dc2a075c` — REVE Large: `brain-bzh/reve-large @ 317531c7` | REVE Responsible Use License v1.0: aggregate scientific results only; no re-identification, model inversion, membership inference or non-consensual profiling; cite the REVE paper and name the model version; no adapted weights shared. — [Paper ↗](https://arxiv.org/abs/2510.21585) | — Accepted by the owner on 2026-10-04. Aggregate scientific results only; no re-identification, model inversion, membership inference or non-consensual profiling; the REVE paper is cited and the model version named; no adapted weights are shared. |
| [LUNA Base · LUNA Large](https://bci.report/methods/luna/) — LUNA Base: `PulpBio/LUNA @ 999c1af0` — LUNA Large: `PulpBio/LUNA @ 999c1af0` | CC BY-ND 4.0 weights: scores from internal probing/adaptation only; no modified weights, LoRA or adapter deltas are shared; no implied endorsement by the LUNA authors; the LUNA name is not used to suggest an official release. — [Paper ↗](https://arxiv.org/abs/2510.22257) | — Owner-approved on 2026-10-04 for internal probing and adaptation with aggregate scores only. No modified weights, LoRA or adapter deltas are shared; no endorsement by the LUNA authors is implied, and the LUNA name is not used to suggest an official release. |
| [BrainOmni Base](https://bci.report/methods/brainomni/) — BrainOmni Base: `OpenTSLab/BrainOmni @ 9a4d3c70` | MIT. — [Paper ↗](https://arxiv.org/abs/2505.18185) | — Permissive weights licence; aggregate scores only, no weights redistributed. |
| [CodeBrain](https://bci.report/methods/codebrain/) — CodeBrain: `YjMajy/CodeBrain @ bef08d2f` | Apache-2.0. — [Paper ↗](https://arxiv.org/abs/2506.09110) | — Permissive weights licence; aggregate scores only, no weights redistributed. |
| [EEGMamba](https://bci.report/methods/eegmamba/) — EEGMamba: `weighting666/EEGMamba @ 0b060d87` | MIT. — [Paper ↗](https://doi.org/10.1016/j.neunet.2025.107816) | — Permissive weights licence; aggregate scores only, no weights redistributed. |
| [ST-EEGFormer Base · ST-EEGFormer Large](https://bci.report/methods/st-eegformer/) — ST-EEGFormer Base: `official GitHub release checkpoint-288.pth (code @ 542ee17)` — ST-EEGFormer Large: `official GitHub release large_weights_only_196.pth` | MIT (repository LICENSE; the README reserves the paper, diagrams and the ST-EEGFormer name). — [Paper ↗](https://openreview.net/forum?id=5Xwm8e6vbh) | — MIT repository licence; its README reserves the paper, the diagrams and the ST-EEGFormer name, none of which is reproduced. Aggregate scores only. |
| [eeg-fm-masking MAE (r=9 cm, L=2) · eeg-fm-masking JEPA (r=9 cm, L=2) · eeg-fm-masking MAE (r=one channel, L=1) · eeg-fm-masking JEPA (r=one channel, L=1)](https://bci.report/methods/eeg-fm-masking/) — eeg-fm-masking MAE (r=9 cm, L=2): `PierreGtch/eeg-fm-masking_mae_r9cm_L2 @ c861ade2` — eeg-fm-masking JEPA (r=9 cm, L=2): revision not in the release — eeg-fm-masking MAE (r=one channel, L=1): revision not in the release — eeg-fm-masking JEPA (r=one channel, L=1): revision not in the release | CC-BY-4.0 weights: attribution required (cite arXiv:2609.33487). — [Paper ↗](https://arxiv.org/abs/2609.33487) | — Attribution licence; the paper is cited beside every row. Aggregate scores only. |
| [ERP-FM Base](https://bci.report/methods/erp-fm/) — ERP-FM Base: `Google Drive file 1IXYnCOpDmY6raIn0M8_ggBOVey_A27l1 (no revision id; sha256 is the identity)` · SHA-256 `856564c4208e9cfe30cfeb5052038071c4951b7078de2bc9e461dead7eb511d4` | CC BY-NC-SA 4.0: non-commercial; aggregate scores only; attribution required; no weights redistributed. — [Paper ↗](https://arxiv.org/abs/2609.32796) | — Non-commercial share-alike weights. This site is personal noncommercial research and publishes aggregate scores only, with attribution; no weights are redistributed. |
| [SingLEM](https://bci.report/methods/singlem/) — SingLEM: `ttlabtuat/SingLEM @ 6db5b5af` | MIT. — [Paper ↗](https://arxiv.org/abs/2509.17920) | — Checkpoint committed in the MIT repository; aggregate scores only. |
| [ZUNA 1.1](https://bci.report/methods/zuna/) — ZUNA 1.1: `Zyphra/ZUNA1.1 @ 10b9d6c8` | Apache-2.0 weights; model card states research use only, not for diagnosis or clinical use. — [Paper ↗](https://arxiv.org/abs/2607.27308) | — Apache-2.0 weights. The model card says research use only, not for diagnosis or clinical use; that sentence travels with its rows. |

## Reviewed 7 October 2026

Route 2 of the decision-research roadmap: one representation and several questions. One new source, BOAS, and two records reused. OpenBMI keeps its rights record from the 3 October review, and EESM19 its record from the 20 September review with the 22 September amendment; route 2 uses every stored epoch of the full EESM19 release that passes the published quality rules, with the first scorer’s stages at their natural mix: a different protocol from the balanced scalp subset in the core matrix. Not published: per-window, per-person, per-night and per-fold values, including the per-person percentiles the release candidate carries for OpenBMI; measured compute totals (GPU-hours, wall time per fit and per block, training steps per outer fold), while the cost ledger publishes, labelled indicative, the two times measured interleaved on a shared GPU; and the aggregates and the three independent audits, which carry private storage paths and are pinned by hash in the review manifest instead. Release [`shared-representation-update-20261007`](https://bci.report/releases/#shared-representation-update-20261007).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [OpenBMI motor imagery (Lee et al. 2019) ↗](https://doi.org/10.5524/100542) — Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli and Seong-Whan Lee · EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy, GigaScience (2019), giz002, doi:10.1093/gigascience/giz002. Data: Supporting data, GigaScience Database, doi:10.5524/100542. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — The paper's Ethical Approval section states that the study was approved by the Korea University Institutional Review Board (1040548-KUIRB-16-159-A-2) and that written informed consent was obtained from all participants before the experiments. Published here: participant-equal means with whole-participant bootstrap intervals for each arm and calibration budget, paired contrasts, and how many of the 51 people declined. The release's per-person summaries (worst observed change, medians, 10th percentiles) are dropped, and no participant, session file or trial identifier is published. Reused unchanged from the 2026-10-03 review of the same recordings. Published here for route 2: participant-equal means with person-level bootstrap intervals for each arm and question, cohort-level AUROC and log loss, paired contrasts with their intervals and per-seed points, cohort-level window counts and question prevalences, and the canary's cohort-level balanced accuracy on the first fold's 40 training people. The per-person 10th, 50th and 90th percentiles route 2 computed are dropped, as the 2026-10-03 release dropped its own. |
| [BOAS, the Bitbrain Open Access Sleep dataset ↗](https://openneuro.org/datasets/ds005555/versions/1.1.3) — Eduardo López-Larraz, María Sierra-Torralba, Sergio Clemente, Galit Fierro, David Oriol, Javier Minguez, Luis Montesano and Jens G. Klinzing · The Bitbrain Open Access Sleep (BOAS) dataset, OpenNeuro ds005555, version 1.1.3 (2026), doi:10.18112/openneuro.ds005555.v1.1.3. Polysomnography EEG and the human-consensus stage labels only; the headband recordings and the publisher's automatic labels are not used. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Released on OpenNeuro under CC0 by its authors (ds005555, version 1.1.3). The dataset description states approval by the Comité de Ética de la Investigación de la Comunidad Autónoma de Aragón (C.I. PI24/046). The README states that participants, adult members of the general population, provided written informed consent. Neither says whether the consent covered public sharing. Both statements come from the publisher's dataset description and README; no peer-reviewed paper describes BOAS. The ethics reference was added in version 1.1.1 (May 2025) with no approval date. Published here: cohort-level participant-equal means with person-level bootstrap intervals, paired contrasts with their intervals and per-seed points, log R with its interval, cohort-level AUROC, log loss and Cohen's kappa, cohort-level stage counts and question prevalences, the share of test windows where two heads disagree, and the canary's cohort-level balanced accuracy on the first fold's 80 training people; every cell pools at least 20 people (the smallest, 71). No per-person, per-night or per-fold value, no per-person percentiles, no identifier, date or demographic. Only the polysomnography EEG and the human-consensus labels are used. |
| [EESM19, full release (first scorer) ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) — Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed. | [CC0-1.0 declared by upstream and mirror ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Paper reports Central Denmark Region ethics approval 1-10-72-413-17, Danish Medicines Agency approval 2017111085, registration, and written consent. Upstream includes exact age/sex plus sleep diaries, questionnaires, actigraphy, and timing; none should be exposed. Amended 2026-09-22: the 2025 data descriptor covering EESM19 and EESM23 (Mikkelsen et al., Scientific Data 12, 301, doi:10.1038/s41597-025-04579-8) states that publication was not mentioned in the informed consent form; before release, the GDPR office of Region Midt judged the data fully anonymized. Consent therefore covered the study, and the public release rests on that anonymization judgment. Reused unchanged from the 2026-09-20 review of the same release, with its 2026-09-22 amendment. Published here for route 2, in a panel labelled crude: every stored epoch of the first scorer that passes the published core quality rules, at the natural stage mix (a different protocol from the balanced scalp subset), and for the three questions participant-equal means of each arm, paired contrasts with person-level bootstrap intervals, cohort-level epoch counts and the canary's cohort-level balanced accuracy on the first fold's 16 training people. One seed; no per-person, per-night or per-fold value. |

**BOAS, published with three stated gaps (owner decision, 7 October 2026).** Its authors released it on OpenNeuro under CC0, name an ethics committee with a reference number and state written informed consent. Three gaps remain, and they are stated wherever a BOAS figure appears: The consent statement does not say whether participants agreed to public sharing or secondary use. The ethics and consent statements come from the publisher's dataset description and README. No peer-reviewed paper describes BOAS. The ethics reference was added to the release in version 1.1.1 (May 2025), and the release does not say when it was granted relative to the recordings. The owner approved publishing cohort aggregates only, with every cell pooling at least 20 people, no demographics and no per-person distributions. Participants are pseudonymised in the public release. No result here evaluates Bitbrain's headband or its automatic sleep scoring; neither is used. Credit: Eduardo López-Larraz, María Sierra-Torralba, Sergio Clemente, Galit Fierro, David Oriol, Javier Minguez, Luis Montesano and Jens G. Klinzing · The Bitbrain Open Access Sleep (BOAS) dataset, OpenNeuro ds005555, version 1.1.3 (2026), doi:10.18112/openneuro.ds005555.v1.1.3. Polysomnography EEG and the human-consensus stage labels only; the headband recordings and the publisher's automatic labels are not used.

## Later sessions: three sources

Four results on one question — does a decoder trained on an earlier session still work later? — each from its own approved release, and each the same person in a later session with no labels from it. The three sources, all published as aggregate scores, are WBCIC-SHU motor imagery, the longitudinal RSVP dataset (its Group A only) and Forenzo and He’s continuous-tracking dataset. Each licence was read from the repository’s public API record and each ethics and consent statement from the paper’s full text, on 8 October 2026. The continuous-tracking results count cohort records, which are not proven to be unique people, and keep the two cohorts apart. Not published: per-person and per-record values, including every minimum and percentile and every median other than the four cursor-tracking ridge medians printed beside their means; WBCIC-SHU confusion matrices; the cursor-tracking results split by recorded decoder; measured compute; and the handoffs, release decisions and independent audits, which carry private storage paths and are pinned by hash in the review manifest instead. Release [`later-sessions-update-20261008`](https://bci.report/releases/#later-sessions-update-20261008).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [WBCIC-SHU motor imagery (Yang et al. 2025) ↗](https://doi.org/10.25452/figshare.plus.22671172.v5) — Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi and Xiaorong Gao · A multi-day and high-quality EEG dataset for motor imagery brain-computer interface, Scientific Data 12, 488 (2025), doi:10.1038/s41597-025-04826-y. Data: Banghua Yang and Fenqi Rong, WBCIC-SHU Motor Imagery Dataset, Figshare+, doi:10.25452/figshare.plus.22671172.v5, CC BY 4.0. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The data paper states that written informed consent was obtained after the participants were informed about the procedure, and that the study was approved by the Tsinghua University Medical Ethics Committee (approval number 20190002) and adhered to the Declaration of Helsinki. Published here: participant-equal means with paired whole-participant bootstrap intervals for each cohort, the paired differences, secondary accuracy and macro-F1 means, and trial counts. No per-person value, participant or session-file identifier, confusion matrix or demographic is published. |
| [Longitudinal ERP dataset, RSVP face task (Yang et al. 2025) ↗](https://doi.org/10.6084/m9.figshare.27201003.v1) — Chen Yang, Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang and Xiaorong Gao · A longitudinal EEG dataset of event-related potential, figshare, doi:10.6084/m9.figshare.27201003.v1, CC0. Paper: Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang, Xiaorong Gao and Chen Yang, A longitudinal EEG dataset of event-related potential, Scientific Data 12, 1069 (2025), doi:10.1038/s41597-025-05378-x. Derived analysis by BCI Report; not endorsed by the authors. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — The data paper states that the experiment was approved by the Tsinghua Institutional Review Board (No. 20230058) and that each participant read and signed an informed consent form before the experiment. Published here: participant-equal visit means for the 15 Group A participants, the AUROC interval at each visit, the paired Day 200 minus Day 7 contrast with its interval, how many participants declined by 0.05 AUROC or more, and event counts per visit. No per-person value, participant identifier, prediction or demographic is published. |
| [Forenzo & He continuous-tracking EEG-BCI dataset (KiltHub) ↗](https://doi.org/10.1184/R1/25360300.v1) — Dylan Forenzo and Bin He · EEG-BCI Dataset for “Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface”, KiltHub, Carnegie Mellon University, doi:10.1184/R1/25360300.v1, CC BY 4.0. Citation the record requests: Dylan Forenzo, Hao Zhu, Jenn Shanahan, Jaehyun Lim and Bin He, Continuous tracking using deep learning-based decoding for noninvasive brain–computer interface, PNAS Nexus 3(4), pgae145 (2024), doi:10.1093/pnasnexus/pgae145. Derived analysis by BCI Report; not endorsed by the authors and not a reproduction of the paper's online results. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The associated paper states that the study was approved by the Institutional Review Board at Carnegie Mellon University and that each subject gave written consent to the protocol before participating; it gives no approval number. Published here: for each cohort and response arm, record-equal means of the primary error for the spectral ridge and the source-mean comparator with bootstrap intervals, the ridge's median, the paired difference with its interval and how many admitted records the ridge did worse on, secondary means with undefined values kept null, and coverage counts. No other per-record value, no record, file or participant identifier, prediction or demographic is published. |

**Ethics and consent, as each paper states them.** WBCIC-SHU motor imagery (Yang et al. 2025) — ethics approval: Approved by the Tsinghua University Medical Ethics Committee (approval number 20190002); carried out in line with the Declaration of Helsinki. Informed consent: Written informed consent was obtained from the participants after they were informed about the procedure, purpose, requirements and motor-imagery techniques. Read from [its paper’s full text ↗](https://europepmc.org/article/PMC/PMC11930978). Longitudinal ERP dataset, RSVP face task (Yang et al. 2025) — ethics approval: Approved by the Tsinghua Institutional Review Board (No. 20230058). Informed consent: Each participant read and signed an informed consent form before the experiment. Read from [its paper’s full text ↗](https://europepmc.org/article/PMC/PMC12185715). Forenzo & He continuous-tracking EEG-BCI dataset (KiltHub) — ethics approval: Approved by the Institutional Review Board at Carnegie Mellon University; no approval number is given. Informed consent: Each subject provided written consent to the protocol before participating. Read from [its paper’s full text ↗](https://europepmc.org/article/PMC/PMC11060102).

## Questions in language: sources, text encoders and wordings

Route 3 of the decision-research roadmap, where one small head over frozen EEG features is asked each question by a question number, a label template or a description. No new dataset: BETA, BOAS, EESM19, OpenBMI motor imagery and the wearable SSVEP dataset keep their rights records, reused unchanged. New here are three frozen text encoders, which turn the wordings into vectors, and the wordings themselves. Not published: per-person, per-night, per-recording and per-fold values, per-window scores, features, head weights, text-encoder files and fold assignments; the per-frequency and interior-only SSVEP breakdowns, which no independent audit covers; any value of a pre-run check, which is published as pass or fail and counts only; demographics, recording dates and clock times; measured compute; and the handoff, the aggregates, the protocol and the four independent audits, which carry private storage paths and are pinned by hash in the review manifest instead. Release [`questions-in-language-update-20261008`](https://bci.report/releases/#questions-in-language-update-20261008).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Cohort aggregates over the 70 people, with the published 2-s windows and participant folds: paired contrasts with their intervals and both flags, per-arm levels with chance-corrected values, generalisation costs, flip rates and the per-rotation TPL - CCA rows. No per-person, per-fold or per-frequency value. |
| [BOAS, the Bitbrain Open Access Sleep dataset ↗](https://openneuro.org/datasets/ds005555/versions/1.1.3) — Eduardo López-Larraz, María Sierra-Torralba, Sergio Clemente, Galit Fierro, David Oriol, Javier Minguez, Luis Montesano and Jens G. Klinzing · The Bitbrain Open Access Sleep (BOAS) dataset, OpenNeuro ds005555, version 1.1.3 (2026), doi:10.18112/openneuro.ds005555.v1.1.3. Polysomnography EEG and the human-consensus stage labels only; the headband recordings and the publisher's automatic labels are not used. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Cohort aggregates only, each stating its n: 100 people, or 71 where the N3 stage needs at least five N3 windows per person (the per-stage AUROC for N3 and the "not N3" negation cell). No per-person, per-night, per-recording or per-fold value, no demographic, date or clock time; every pre-run check on BOAS as pass or fail and counts only. Read from route 2's frozen CBraMod and REVE-L features. |
| [EESM19 ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) — Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed. | [CC0-1.0 declared by upstream and mirror ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Cohort aggregates over 20 people (S1, a replication of the primary sleep entries), each with its flags; declared likely inconclusive before the run. |
| [OpenBMI motor imagery ↗](https://doi.org/10.5524/100542) — Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli and Seong-Whan Lee · EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy, GigaScience (2019), giz002, doi:10.1093/gigascience/giz002. Data: Supporting data, GigaScience Database, doi:10.5524/100542. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Cohort aggregates over the published 51-person cohort (S2), each with its flags. Not gated by a triviality canary: none is declared for motor imagery. |
| [Wearable SSVEP BCI dataset (dry and wet electrodes) ↗](https://figshare.com/articles/dataset/An_Open_Dataset_for_Wearable_SSVEP-Based_Brain-Computer_Interfaces/13560281) — Zhu, F., Jiang, L., Dong, G., Gao, X., & Wang, Y. (2021). An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces (Version 4) [Data set]. Figshare. https://doi.org/10.6084/m9.figshare.13560281.v4 — Zhu, F., Jiang, L., Dong, G., Gao, X., & Wang, Y. (2021). An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces. Sensors, 21(4), 1256. https://doi.org/10.3390/s21041256 | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Cohort aggregates over 102 people for each sensor type (S12), computed from the official Tsinghua author mirror acquired on 2026-09-20; no physical unit is claimed and no participant row is published. |

**Three text encoders, frozen at pinned commits and never redistributed.** [BAAI/bge-small-en-v1.5 ↗](https://huggingface.co/BAAI/bge-small-en-v1.5) (MIT, commit `5c38ec7c405ec4b44b94cc5a9bb96e735b38267a`); [sentence-transformers/all-MiniLM-L6-v2 ↗](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) (Apache-2.0, commit `1110a243fdf4706b3f48f1d95db1a4f5529b4d41`); [intfloat/multilingual-e5-small ↗](https://huggingface.co/intfloat/multilingual-e5-small) (MIT, commit `614241f622f53c4eeff9890bdc4f31cfecc418b3`). Each is used as released.

**REVE Large, for secondary results.** Frozen REVE Large features (`brain-bzh/reve-large @ 317531c7`) feed secondary results on BETA and BOAS, under the REVE Responsible Use License v1.0, accepted by the owner with the other foundation models above: aggregate results only, the [REVE paper ↗](https://arxiv.org/abs/2510.21585) cited and the model version named.

**The wordings are this site’s own text.** [questions-in-language-wordings.json](https://bci.report/data/questions-in-language-wordings.json) holds every text the questions were asked with, in English and Chinese, written by two agents from a fixed brief before any score, with the held-out frequencies and the derangements of the shuffled control. It is published under [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/).

**BOAS, extended to route 3 (owner decision D11).** The owner extended BOAS’s approval of 7 October 2026 to route 3’s aggregate BOAS results, under the same conditions:, stated wherever a BOAS figure appears; participants pseudonymised in the public release; cohort aggregates only, every cell pooling at least 20 people; no demographics and no per-person distributions; every pre-run check on BOAS published as pass or fail and counts only. No result here evaluates Bitbrain's headband or its automatic sleep scoring; neither is used.

## Beyond scalp EEG: five sources

Locally executed analyses beyond scalp EEG, each its own species, signal and metric, read on its own and outside the core matrix. Three sources have results — human fNIRS from Bak et al., one macaque’s motor-cortex spiking from MC_RTT, and a numerical coverage check of the ds007420 fNIRS recordings — and two are prepared and published as status only, with counts and no score: FALCON H2 human intracortical handwriting and Rabut et al. human functional ultrasound. Each licence was read from the repository’s public record, and each ethics and consent statement from the source named beside it. Bak’s statement comes from an Internet Archive capture of the publisher’s article page, because the publisher refused automated requests; nothing was bypassed. Rabut et al.’s data are licensed for non-commercial use only. Not published: per-person, per-recording, per-channel, per-trial, per-fold and per-row values; confusion matrices; signals, features, predictions, coefficients, labels and timestamps; measured compute and storage; demographics; and the handoff, its reviews, the protocols and the independent audits, which carry private storage paths and server names and are pinned by hash in the review manifest instead. Release [`beyond-scalp-update-20261012`](https://bci.report/releases/#beyond-scalp-update-20261012).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [Bak 2019 fNIRS dataset · unilateral finger and foot tapping ↗](https://doi.org/10.6084/m9.figshare.9783755.v2) — human · fNIRS — Sujin Bak, Jinwoo Park, Jaeyoung Shin and Jichai Jeong · Open Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping, figshare, doi:10.6084/m9.figshare.9783755.v2, CC BY 4.0. Paper: Sujin Bak, Jinwoo Park, Jaeyoung Shin and Jichai Jeong, Open-Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping, Electronics 8(12), 1486 (2019), doi:10.3390/electronics8121486. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The data paper states that the participants' written consent, approved by the Korea University Institutional Review Board (KUIRB-2019-0254-01), was submitted before data recording, and that the experiments followed the Declaration of Helsinki. Published here: the participant-macro balanced accuracy of a fixed classifier and of a training-prior comparator over the 30 people, the classifier's descriptive participant-bootstrap interval, and class recalls over all held-out predictions. No per-person value, participant identifier, confusion matrix or demographic is published. |
| [MC_RTT · macaque motor cortex during self-paced reaching (Neural Latents Benchmark) ↗](https://doi.org/10.48324/dandi.000129/0.241017.1444) — macaque (Macaca mulatta) · intracortical spiking — O'Doherty, Joseph (2024) MC_RTT: macaque motor cortex spiking activity during self-paced reaching (Version 0.241017.1444) [Data set]. DANDI archive. https://doi.org/10.48324/dandi.000129/0.241017.1444, CC BY 4.0. Recorded by Joseph E. O'Doherty, Mariana M. B. Cardoso, Joseph G. Makin and Philip N. Sabes in the Sabes Lab at UCSF (Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology, Zenodo, doi:10.5281/zenodo.3854034; procedure: Makin et al., Journal of Neural Engineering 15(2), 026010 (2018), doi:10.1088/1741-2552/aa9e95). Prepared for the Neural Latents Benchmark: Felix Pei, Joel Ye et al., Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity, NeurIPS 2021 Track on Datasets and Benchmarks, arXiv:2109.04463. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Animal data: one rhesus macaque. The Neural Latents Benchmark paper states that all its datasets were collected under procedures approved by the Institutional Animal Care and Use Committees at the respective institutions, and that MC_RTT was collected in the Sabes Lab at UCSF. No human took part, so informed consent does not apply. Published here: five validation metrics of a fixed ridge decoder and of a training-mean comparator, with trial, neuron and row counts. With one animal, every figure is that animal's own. No coefficient, prediction or per-trial value is published. |
| [ds007420 · multi-distance fNIRS during ball squeezing and motion-artifact tasks ↗](https://doi.org/10.18112/openneuro.ds007420.v1.0.5) — human · fNIRS — Shibo Zhou, De’Ja Rogers, David A. Boas, Meryem A. Yücel and Yuanyuan Gao · Open-access Multi-Distance fNIRS Dataset Generated During Ball-Squeezing and Motion-Artifact Induction Tasks, OpenNeuro, doi:10.18112/openneuro.ds007420.v1.0.5, CC0. The dataset asks to acknowledge: Gao, Y., Rogers, D., von Lühmann, A., Ortega-Martinez, A., Boas, D.A., & Yücel, M.A. (2023). Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy. Neurophotonics, 10(2), 025007. https://doi.org/10.1117/1.NPh.10.2.025007. Derived analysis by BCI Report; not endorsed by the authors. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Counts only — no score** — The dataset description names the Institutional Review Board of Boston University (Protocol #4502), and the associated paper states that the study protocol was approved by that board and that all participants provided written informed consent. Published here: whole-dataset counts from a label-independent numerical check of the optical values. No decoding score, per-recording or per-channel value, participant identifier or demographic is published. |
| [FALCON H2 · human intracortical handwriting ↗](https://doi.org/10.48324/dandi.000950/0.241029.1403) — human · intracortical spiking — Fan, Chaofei; Hahn, Nick; Kamdar, Foram; Avansino, Donald; Wilson, Guy; Hochberg, Leigh; Shenoy, Krishna V; Henderson, Jaime; Willett, Frank (2024) FALCON Benchmark H2: Human Handwriting iBCI (Version 0.241029.1403) [Data set]. DANDI archive. https://doi.org/10.48324/dandi.000950/0.241029.1403, CC BY 4.0. Described in: Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication, NeurIPS 2023. Part of the FALCON benchmark. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Status only — no scores** — The dataset description states that the participant gave informed consent and was enrolled in the BrainGate2 pilot clinical trial (ClinicalTrials.gov NCT00912041) under an FDA Investigational Device Exemption, with permission from the Institutional Review Boards of Stanford University (protocol #20804). Published here: status only, with trial, file and symbol counts from the preparation audit. No score exists; no neural value, cue text, recording date or participant detail is published. |
| [Rabut et al. human functional ultrasound through a cranial window ↗](https://doi.org/10.22002/f3y3k-em558) — human · functional ultrasound imaging — Claire Rabut, Sumner Norman, Whitney Griggs, Kay Jann, Jonathan Russin, Vasileios Christopoulos, Charles Liu, Richard A. Andersen and Mikhail G. Shapiro · Dataset for: A window to the brain: ultrasound imaging of human neural activity through an acoustically transparent cranial prosthetic, CaltechData, doi:10.22002/f3y3k-em558, CC BY-NC 4.0. Paper: Claire Rabut, Sumner L. Norman, Whitney S. Griggs, Jonathan J. Russin, Kay Jann, Vasileios Christopoulos, Charles Liu, Richard A. Andersen and Mikhail G. Shapiro, Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window, Science Translational Medicine 16, eadj3143 (2024), doi:10.1126/scitranslmed.adj3143. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY-NC 4.0 ↗](https://creativecommons.org/licenses/by-nc/4.0/) | **Status only — no scores** — The paper states that all procedures of the human study were approved by the Institutional Review Boards of the University of Southern California, Caltech (IR19-0902) and Rancho Los Amigos National Rehabilitation Hospital, and that the participant's consent was obtained. Published here: status only, with run and frame counts from the label audit. No image, score or participant detail is published. The dataset licence is noncommercial. |

**Ethics and consent, as each source states them.** Bak 2019 fNIRS dataset · unilateral finger and foot tapping — ethics approval: Approved by the Korea University Institutional Review Board (KUIRB-2019-0254-01); the experiments followed the criteria of the Declaration of Helsinki. Informed consent: The participants' written consent, approved by that board, was submitted before data recording. Read from [doi.org ↗](https://doi.org/10.3390/electronics8121486). ds007420 · multi-distance fNIRS during ball squeezing and motion-artifact tasks — ethics approval: Approved by the Institutional Review Board of Boston University (Protocol #4502, as the dataset description records it). Informed consent: All participants provided written informed consent to take part in the study. Read from [europepmc.org ↗](https://europepmc.org/article/PMC/PMC10203730). FALCON H2 · human intracortical handwriting — ethics approval: Permission from the Institutional Review Boards of Stanford University (protocol #20804); the BrainGate2 pilot trial ran under FDA Investigational Device Exemption #G090003 (ClinicalTrials.gov NCT00912041). Informed consent: The participant gave informed consent and was enrolled in the BrainGate2 trial. Read from [api.dandiarchive.org ↗](https://api.dandiarchive.org/api/dandisets/000950/versions/0.241029.1403/). Rabut et al. human functional ultrasound through a cranial window — ethics approval: Approved by the Institutional Review Boards of the University of Southern California, Caltech (IR19-0902) and Rancho Los Amigos National Rehabilitation Hospital. Informed consent: The participant's consent was obtained. Read from [www.vis.caltech.edu ↗](https://www.vis.caltech.edu/documents/28341/scitranslmed.adj3143.pdf).

**Animal data: an animal-care statement in place of consent.** MC_RTT · macaque motor cortex during self-paced reaching (Neural Latents Benchmark) records one rhesus macaque. Animal-care approval, as the Neural Latents Benchmark paper states it: Collected under procedures and experiments approved by the Institutional Animal Care and Use Committees at the respective institutions; MC_RTT was collected in the Sabes Lab at UCSF. Informed consent: Not applicable: animal data, no human participant. The DANDI record’s own ethics field is empty. Read from [the paper ↗](https://arxiv.org/abs/2109.04463). Its credit, above, names the people who recorded it and the Neural Latents Benchmark that prepared it.

## Pilot studies: four sources and one model

Small pilots on four sources, each from its own approved aggregate export and read on its own, outside the core matrix. The Orthosis error-detection dataset is one cohort published in three configurations and counted once; BINGO is a cross-person, cross-language inner-speech pilot near its prior; REFED is a stored-clip computational diagnostic; the handwriting dataset’s mask-aware EEG analysis, five people, is published with its rights gap stated, by the owner’s decision. Each licence was read from the source’s own record, and each ethics and consent statement from the source named beside it. Not published: per-person, per-field, per-folder, per-clip and per-record values; the Orthosis response-clean subset’s scores, of which only counts are published; individual permutation and label-shuffle replicates, of which the mean and observed range are published; confusion matrices; identifiers, labels, features, predictions and fitted parameters; measured compute; demographics; and the handoffs, approval records, QA records and independent audits, which carry private storage paths and are pinned by hash in the review manifest instead. Status only: Orthosis EMG, TinyMyo and EEG–EMG fusion; handwriting fNIRS and fusion; REFED physiological and fusion evaluation; and a meditation dataset, in which RR means heartbeat RR intervals, not respiratory rate. Release [`pilot-studies-update-20261012`](https://bci.report/releases/#pilot-studies-update-20261012).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [Orthosis error-detection EEG and EMG dataset (Kueper et al. 2024) ↗](https://doi.org/10.5281/zenodo.8345429) — Niklas Kueper, Kartik Chari, Judith Bütefür, Julia Habenicht, Tobias Rossol, Su Kyoung Kim, Marc Tabie, Frank Kirchner and Elsa Andrea Kirchner · EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI’23 CC6 Competition), Zenodo, doi:10.5281/zenodo.8345429, CC BY 4.0. Paper: Niklas Kueper et al., EEG and EMG dataset for the detection of errors introduced by an active orthosis device, Frontiers in Human Neuroscience 18 (2024), doi:10.3389/fnhum.2024.1304311. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The data paper states that the study was approved by the Ethics Committee of the Department of Computer Science and Applied Cognitive Science of the Faculty of Engineering at the University of Duisburg-Essen, with no approval number given, and that the participants gave written informed consent. Published here: means over the 8 people with person-bootstrap intervals for three configurations of one cohort, the paired EEGPT-minus-classical difference with its interval, permutation-control means with their observed ranges, and cohort counts. No per-person value, participant or run identifier, response-clean subset score or demographic is published. |
| [BINGO cross-linguistic inner-speech EEG dataset (Greek and English) ↗](https://bingo-project.gr/datasets/) — BINGO (Brain Imagined-Speech Communication) project, CERTH · cross-linguistic inner-speech EEG dataset, https://bingo-project.gr/datasets/; no licence is stated by the source. Documentation: Fotis P. Kalaganis, Kostas Georgiadis, Maria Kyrou, Spiros Nikolopoulos and Ioannis Kompatsiaris, D3.1 – The BINGO Benchmarking Framework, version 2.0, public deliverable (2025), https://bingo-project.gr/download/397. Derived analysis by BCI Report; not endorsed by the authors. | No licence stated | **Aggregate scores** — The source states no licence, and the cross-language dataset’s own documentation has no ethics-approval or consent statement. The project’s public deliverable D3.1, version 3.0, reports that participants signed a consent form; it says so for the project’s NATO-alphabet recordings in the same CERTH laboratory. No ethics-approval reference is published. Aggregates are published on that basis by the owner’s decision. Published here: means over the 17 source-defined subject fields with descriptive intervals, the cross-language contrast, controls and counts. No per-field value, field identifier, confusion matrix, prediction or source file is published, and no right to redistribute the data is implied. |
| [EEG-fNIRS-based Handwriting Trajectory Dataset (Hugging Face) ↗](https://huggingface.co/datasets/lasfk/EEG-fNIRS-based-Handwriting-Trajectory-Dataset) — lasfk on Hugging Face · EEG-fNIRS-based Handwriting Trajectory Dataset, revision 4036680b5fb0015472d55589da2f34c5a87a3c10, CC BY-NC-SA 4.0 as declared on the dataset card, gated access. Derived analysis by BCI Report; not endorsed by the uploader. | [CC BY-NC-SA 4.0 ↗](https://creativecommons.org/licenses/by-nc-sa/4.0/) | **Aggregate scores** — No ethics-approval or informed-consent statement was found; the aggregates are published with that gap stated, by the owner’s decision. Published here: means over the 5 people with descriptive person-bootstrap intervals (none for the mask-only control, whose bounds would show individual people’s scores), three paired contrasts, label-shuffle means with observed ranges, the channel-availability distribution over trials and cohort counts. No per-person value, identifier, label, prediction or source file is published, and no right to redistribute the data is implied. |
| [REFED: real-time dynamic labelled EEG-fNIRS emotion dataset (Ning et al. 2025) ↗](https://huggingface.co/datasets/REFED2025/REFED-dataset) — Xiaojun Ning, Jing Wang, Zhiyang Feng, Tianzuo Xin, Shuo Zhang, Shaoqi Zhang, Zheng Lian, Yi Ding, Youfang Lin and Ziyu Jia · REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset, NeurIPS 2025 Datasets and Benchmarks Track. Data: REFED2025/REFED-dataset on Hugging Face, revision 0298355c2823946ac5800b2ec12a082bf2dcd01b, CC BY-NC-SA 4.0, gated. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY-NC-SA 4.0 ↗](https://creativecommons.org/licenses/by-nc-sa/4.0/) | **Aggregate scores** — The paper states that the local Institutional Review Board approved the study (its datasheet names IRB-CASIA) and that all participants gave informed consent to the recording of their physiological signals and ratings, for non-commercial research use. Published here: equal-axis mean absolute errors over the 30 stored clips for four fixed arms in two conditions, counts and the method. No per-folder, per-clip or per-video value, target, prediction or demographic is published. |

**Ethics and consent, as each source states them.** Orthosis error-detection EEG and EMG dataset (Kueper et al. 2024) — ethics approval: Approved by the Ethics Committee of the Department of Computer Science and Applied Cognitive Science of the Faculty of Engineering at the University of Duisburg-Essen; no approval number is given. Informed consent: The participants gave written informed consent and were told they could stop at any time. Read from [europepmc.org ↗](https://europepmc.org/article/PMC/PMC10839100). REFED: real-time dynamic labelled EEG-fNIRS emotion dataset (Ning et al. 2025) — ethics approval: Approved by the local Institutional Review Board; the paper’s datasheet names the approval IRB-CASIA. Informed consent: All participants gave informed consent to the recording of their physiological signals and rating scales, and agreed that the data would be used only for non-commercial scientific research. Read from [papers.neurips.cc ↗](https://papers.neurips.cc/paper_files/paper/2025/file/2bf0ed7c35d9d84128e7f7c72ab76402-Paper-Datasets_and_Benchmarks_Track.pdf).

**BINGO, published with its rights as they stand (owner decision, 12 October 2026).** The project’s datasets page, which the analysed file comes from, states no licence, terms, ethics or consent; it gives an access link only. The cross-language dataset’s own documentation, [D3.1 – The BINGO Benchmarking Framework, version 2.0 ↗](https://bingo-project.gr/download/397) (Public (PU)), describes the protocol and the preprocessing and has no ethics or consent statement. Consent is as the project’s public deliverable [D3.1 – The BINGO Benchmarking Framework, version 3.0 ↗](https://bingo-project.gr/download/402) reports it: “After signing a consent form, each participant attended three experimental sessions”. That sentence is about the project’s NATO-alphabet dataset, recorded in the same CERTH laboratory, not about the cross-language dataset. No ethics-approval reference is published by the source. The owner decided to publish the aggregates on that basis, with these rights stated wherever the result is. Nothing upstream is redistributed, and no right to reuse the data is implied.

**The handwriting dataset, published with its rights gap stated (owner decision, 12 October 2026).** No ethics-approval or informed-consent statement was found. The dataset card has none; a participating team’s challenge paper, which identifies the competition as the ACM MM 2026 Multimodal Brain-Computer Interface Grand Challenge, has none; and the organisers’ paper is listed there as forthcoming. The aggregates are published with this gap stated, by the owner’s decision, to be rechecked when the organisers’ paper appears. The challenge paper read: [FRED: Frequency-Decorrelated Temporal Ensembles for EEG–fNIRS Imagined-Handwriting Decoding ↗](https://arxiv.org/abs/2608.03176v2). The dataset card declares CC BY-NC-SA 4.0 and gated access. Like BOAS and BINGO, this is an owner decision on stated gaps; the BNCI2015-001 hold is unchanged, because its review turns on its CC BY-NC-ND catalogue licence as well. Nothing upstream is redistributed, and no right to reuse the data is implied.

**EEGPT in the pilot studies.** Frozen EEGPT with a fixed supervised probe, against matched classical features on the Orthosis error-detection dataset, is published from its approved export, after an independent check of that exact file. The earlier hold on EEGPT aggregate scores was this project’s own conservative choice, not a publisher’s rule. This comparison was the first EEGPT result on the site’s pages; its core-matrix cells and pretraining-attribution rows followed on 13 October 2026, in the section [EEGPT on the core matrix](https://bci.report/data-use/#eegpt-core) below. Some of them had been in audit records in the public repository from its first release, `research-preview-20260920`: balanced accuracy for mi-rest, EEGMAT and both BETA protocols (`research/benchmark_expansion_v1/`), and EEGPT’s pretraining-attribution controls on mi-rest and EEGMAT (`research/deployment_validation_20260920/`). Corrected 12 October 2026: this paragraph first said the comparison was the only EEGPT figure published. The code licence, Apache-2.0, covers the repository’s source code only; the checkpoint’s licence is unresolved: the checkpoint’s own licence and terms could not be read from its hosting record, and the repository’s code licence is not evidence that it covers the weights. No original or converted weights are distributed: the checkpoint is named and its SHA-256 given (`eegpt_mcae_58chs_4s_large4E.ckpt`, `9d63ebc80bba1ad5e3277df4eecfa05d2745e6243a12ff743c4c0a93930dfac0`) to identify it, and that grants nothing. Repository [BINE022/EEGPT ↗](https://github.com/BINE022/EEGPT) at `a0e0a8fad729e2ecf4eedb3a81548a6e6d48a705`; paper [EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals ↗](https://openreview.net/forum?id=XNiFuoqTuD).

## EEGPT on the core matrix

By the owner’s decisions of 12 and 13 October 2026: Publish EEGPT’s eight core-matrix cells as computed, without a re-run, as a frozen row beside the core matrix, and its pretraining-attribution rows beside the published LaBraM and CBraMod rows. No new dataset: the cells are the recorded aggregates of the same runs as the published LaBraM rows and, except on idle, the CBraMod rows, with only the encoder swapped, checked read-only against the saved predictions. The seven datasets behind the protocols keep their rights records from the 2026-09-20 review, reused unchanged. Release [`eegpt-core-update-20261013`](https://bci.report/releases/#eegpt-core-update-20261013).

| Dataset · protocol | Source terms | What is published |
| --- | --- | --- |
| [ds003810 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) — Motor imagery & rest · [mi-rest](https://bci.report/protocols/mi-rest/#eegpt) — Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/ | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [ds005342 ↗](https://doi.org/10.18112/openneuro.ds005342.v1.0.3) — Idle & command · [idle](https://bci.report/protocols/idle/#eegpt) — OpenNeuro ds005342 contributors · version 1.0.3, doi:10.18112/openneuro.ds005342.v1.0.3; associated study doi:10.3389/fninf.2022.961089. Original author credits are retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: test-trial counts of idle false activations and commands detected within 3 s, how many people always abstained, and the cohort-mean window balanced accuracy and AUROC; no interval; no per-person, per-fold or per-trial value. |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — SSVEP · 8 channels · [beta-8ch](https://bci.report/protocols/beta-8ch/#eegpt) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — SSVEP · 4 channels · [beta-4ch](https://bci.report/protocols/beta-4ch/#eegpt) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — Arithmetic & rest · [arithmetic-rest](https://bci.report/protocols/arithmetic-rest/#eegpt) — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [ds006593 ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) — P300 target ERP · [p300-target](https://bci.report/protocols/p300-target/#eegpt) — OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [TMNRED / ds005383 ↗](https://doi.org/10.18112/openneuro.ds005383.v1.0.0) — Semantic target ERP · [semantic-target](https://bci.report/protocols/semantic-target/#eegpt) — Yanru Bai, Qi Tang et al. · TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments (2025), doi:10.1038/s41597-025-05036-2. OpenNeuro ds005383 v1.0.0. | [OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |
| [EESM19 scalp subset ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) — Sleep staging · [sleep-scalp](https://bci.report/protocols/sleep-scalp/#eegpt) — Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed. | [CC0-1.0 declared by upstream and mirror ↗](https://creativecommons.org/publicdomain/zero/1.0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for EEGPT: the cohort balanced accuracy with its participant-bootstrap interval, macro F1 and the cohort size; no per-person, per-fold or per-trial value. |

**The model’s terms.** Same reasoning as the 2026-10-12 pilot decision: independently computed aggregate results are a separate output from the weights; no publisher prohibition was found; the checkpoint’s licence is unresolved; no original or converted weights are distributed; Apache-2.0 covers the repository’s code only. The checkpoint is the one the pilot comparison above identifies by its SHA-256; its licence is unresolved: the checkpoint’s own licence and terms could not be read from its hosting record, and the repository’s code licence is not evidence that it covers the weights. No model’s authors endorse these results.

**How EEGPT differs from EEG-DINO.** EEG-DINO stays catalogue-only by the owner’s decision of 2026-10-04: its upstream weights carry no licence, and it was neither downloaded nor run. EEGPT differs: it was evaluated in the same runs as the published rows and was held only because its checkpoint’s licence could not be read, a hold the 2026-10-12 reassessment found to be this project’s own choice. EEG-DINO is not re-reviewed by this decision.

**The public repository’s records.** Four of the eight cells (mi-rest, EEGMAT and both BETA protocols) and EEGPT’s attribution controls have been in audit records in the public repository from its first release, `research-preview-20260920`; this release puts them on the site’s pages with the other four cells. The public attribution audit (research/deployment_validation_20260920/attribution-independent-audit.json) still labels the four EEGPT contrasts “hold-unresolved-weight-license”: that label records 2026-09-20. With this release they are published. The file keeps its bytes.

**Pretraining exposure.** None of the seven core datasets is in the pretraining list EEGPT’s authors publish, checked on 2026-10-12; BETA’s relation to the Tsinghua benchmark that is in the list is stated beside the BETA cells.

**Not published.**

- Per-trial predictions and scores, per-person, per-fold and per-class metrics, features, fitted heads, idle thresholds and calibration grids, and every identifier. How many people had a negative attribution difference is not published either.
- The per-seed balanced accuracies of the three constructor-random encoders and their spread: as for LaBraM and CBraMod, only their mean is published.
- BETA’s descriptive duration-group summaries (S1–15 against S16–70): the published BETA rows show none.
- The idle sensitivity counts of the review (how many windows or pairs lie near a threshold), its pooled AUROC, which is not the protocol’s definition, and the Monte Carlo spreads of the independent recomputation.
- Timings, devices, memory and software environments.
- EEGPT results outside the eight core protocols (BNCI2014-001 and the other parallel-batch datasets).
- The checkpoint and its tensor conversion: no original or converted weights are distributed; their SHA-256 identifies them and grants nothing.
- The run summaries, protocols and independent audits, the batch state and the review’s verification records themselves: each is pinned by SHA-256 in the release manifest and checked by the export, never published, because they carry private storage paths.

## Data quality: three sources checked end to end

Three public EEG datasets, each read from its own files and read on its own, outside the core matrix and every leaderboard. Shin2022 is a new source, published as aggregate scores: a participant-held-out diagnostic with three arms, beside the coverage chain that counts its runs and windows. eldBETA is a new source, published as counts only: one participant’s recordings pass the reader and the quality checks, and with one person every score is that person’s, so the accuracy is held by the owner’s decision, the rule the Stieger longitudinal pilot follows. ZuCo 2.0 is status only: a timing hold on one converted run, with no figure. Each licence was read from the source’s own record and each ethics and consent statement from the paper named beside it, and the export records each read with its capture’s SHA-256. Not published: participant rows of any kind: identifiers, run and file names, fold assignments, features, fitted models, decision values and predictions; raw signals from any of the three datasets; per-participant figures from the Shin2022 evidence: window counts per participant and their ranges, and which participants had flat windows; the Shin2022 feedback-duration distribution (quantiles and mean) and the per-channel counts of the signal census; eldBETA's one-participant accuracy and its fixed first-frequency comparator: held by the owner's decision under the one-person rule; ZuCo 2.0's converted-run code, row-level timing values and marker counts; no ZuCo 2.0 data are redistributed; MOABB's catalog trial and run counts for Shin2022: register content, in the open register file; compute times, storage sizes, hosts and storage locations; the research exports, audits, reviews, protocols and handoffs themselves: each is pinned by SHA-256 in the release manifest and checked by the export, never published, because several carry private storage paths. Release [`data-quality-update-20261020`](https://bci.report/releases/#data-quality-update-20261020).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [Shin2022 · Data from: Closed-loop motor imagery EEG simulation for brain-computer interfaces (Shin, Suma and He) ↗](https://doi.org/10.6084/m9.figshare.20383716.v1) — Figshare record 20383716, version 1, the only version the record lists — Hyonyoung Shin, Daniel Suma and Bin He · Data from: Closed-loop motor imagery EEG simulation for brain-computer interfaces, figshare, doi:10.6084/m9.figshare.20383716.v1, CC BY 4.0. Paper: Hyonyoung Shin, Daniel Suma and Bin He, Closed-loop motor imagery EEG simulation for brain-computer interfaces, Frontiers in Human Neuroscience 16:951591 (2022), doi:10.3389/fnhum.2022.951591. Funding: NIH AT009263. Modifications: canonical deduplication of the byte-identical runs, first-second window selection and signal-validity filtering. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Aggregate scores** — The study was reviewed and approved by the Institutional Review Board of Carnegie Mellon University; the paper gives no approval number. The participants gave written informed consent to take part in the study. The data record states that all identifying information has been removed. Published here: each arm’s participant-mean balanced accuracy with its participant-bootstrap interval and pooled secondary value, and aggregate run and window counts; no per-participant value, identifier, prediction or signal. |
| [eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population (Liu, Wang, Gao and Chen) ↗](https://doi.org/10.6084/m9.figshare.18032669.v3) — Figshare record 18032669, version 3, the latest of the 3 versions the record lists — Bingchuan Liu, Yijun Wang, Xiaorong Gao and Xiaogang Chen · eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population, figshare, doi:10.6084/m9.figshare.18032669.v3, CC BY 4.0. Data descriptor: Scientific Data, doi:10.1038/s41597-022-01372-9. Funding: National Natural Science Foundation of China (No. 62171473). Modifications: decoding of the original recording format with the header calibration, and fixed channel and window selection; no score from these data is published. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) | **Counts only — score held** — The protocol followed the Declaration of Helsinki and was approved by the institutional review board of Tsinghua University, approval number 20210032. The participants were familiarised with the protocol and gave full written consent before the experiment. Published here: counts for one participant’s recordings (blocks, targets, trials, trials dropped and non-finite values) and no score; no identifier, per-trial value or signal. |
| [ZuCo 2.0 ↗](https://data.nemar.org/nm000279/v1.0.0/) — NEMAR nm000279, version 1.0.0, doi:10.82901/nemar.nm000279.v1.0.0 — Hollenstein, N., Troendle, M., Zhang, C., & Langer, N. (2020). ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation. LREC 2020. | CC-BY-4.0 — As the dataset_description.json of that version states it. Unverified: the original terms on OSF; terms for the text excerpts the stimuli quote. | **Status only — no score** — In one converted run on NEMAR, some fixation times fall outside the recording; the run’s stimulus markers lie within it. No EEG–text score is computed until the converter's timing rules and a source-checksummed fixture are available. The hold covers that one converted run; no timing was corrected and no row removed. |

**Ethics and consent, as each source states them.** Shin2022 — ethics approval: The study was reviewed and approved by the Institutional Review Board of Carnegie Mellon University; the paper gives no approval number. Informed consent: The participants gave written informed consent to take part in the study. Read from [www.frontiersin.org ↗](https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2022.951591/full). eldBETA — ethics approval: The protocol followed the Declaration of Helsinki and was approved by the institutional review board of Tsinghua University, approval number 20210032. Informed consent: The participants were familiarised with the protocol and gave full written consent before the experiment. Read from [www.nature.com ↗](https://www.nature.com/articles/s41597-022-01372-9). ZuCo 2.0 — No consent or ethics statement for this dataset was read in this release.

**Terms the records state beside the licence.** Shin2022: “All identifying information have been removed.” (Figshare record description) “Please cite the above paper if you use any data included in this dataset.” (Figshare record description) eldBETA: “The eldBETA database is open-access for research and can be downloaded from the website” (data descriptor, abstract) The papers carry their own licences, which cover the articles; each data licence is its record’s. Shin2022’s attribution names the modifications made here, and so does eldBETA’s.

## Clinical groups: IESSEEG

An infant case-control pilot on one public dataset, published as aggregate scores in its own section of the clinical-groups page, with age and recording-setting controls and frozen NeuroLM probes. IESSEEG is a new source, and it adds no dataset page and no count. The clinical bar above applies to it: The licence and the attribution come from the release card and LICENSE at the pinned revision, and the requested citation is the card’s: “IESSEEG: An Open EEG Dataset Towards Better Understanding of Infantile Epileptic Spasms Syndrome”. Not published: participant rows of any kind: identifiers, ages, diagnoses, recordings, fold assignments, features, out-of-fold predictions and probabilities; per-fold metrics for the five folds and for the age <=2 band within each fold; per-group age means, medians and quartiles, and sex counts: aggregates, but a demographic profile of a diagnosis-specific group, which this site does not publish. The age <=2 column gives only the band's size and its case and control counts; intervals and significance tests: none was specified before scoring, and none is computed after it; EEG signals, model code, the NeuroLM checkpoint and its features: none is redistributed; runtimes, hosts, storage locations and the private seal and score files (named by SHA-256 only). Release [`clinical-iesseeg-update-20261020`](https://bci.report/releases/#clinical-iesseeg-update-20261020).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [IESSEEG · awake case-control pilot ↗](https://huggingface.co/datasets/Capur/IESSEEG/tree/1c32ee3a425a324808a9d275b60ce85625af35c8) — Hugging Face Capur/IESSEEG at revision 1c32ee3a425a324808a9d275b60ce85625af35c8 — 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. | [CC-BY-4.0 ↗](https://creativecommons.org/licenses/by/4.0/) — For the dataset and annotations; [LICENSE ↗](https://huggingface.co/datasets/Capur/IESSEEG/blob/1c32ee3a425a324808a9d275b60ce85625af35c8/LICENSE) | **Aggregate scores** — 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. |

**Ethics and consent, as the release card states them.** “The retrospective study received institutional review board approval under protocol 17-000026, with waivers of informed consent and HIPAA research authorization. Do not attempt to re-identify participants.” Read from [the release card at revision `1c32ee3a` ↗](https://huggingface.co/datasets/Capur/IESSEEG/blob/1c32ee3a425a324808a9d275b60ce85625af35c8/README.md). Informed consent and HIPAA research authorization were waived for this retrospective study, so the published aggregates rest on the board’s approval and the card’s licence.

**Prior reuse, as the publisher declares it.** The release card declares that 400 case segments were previously analysed by Rajaraman et al. (2024), “Computational EEG attributes predict response to therapy for epileptic spasms”. The participant-level crosswalk between that analysis and this release is unresolved; the cohort adds no dataset to any count.

**NeuroLM in this section.** NeuroLM-B is a frozen feature extractor read by supervised logistic probes; its weights are unchanged, and no weights are distributed. The checkpoint is the one the NeuroLM pilot below identifies by its SHA-256 (`ffe098bc138b89f8817d3710a3604498d8ecd15135080e2ca27735d05c6d29ab`), under the terms listed there. Its training stage and its prior exposure to these participants are unknown.

## NeuroLM on EEGMAT: a separate pilot panel

NeuroLM-B’s frozen features on EEGMAT’s arithmetic and rest, published as aggregate scores in a panel of its own on the pilot-studies page, outside the pilot count and every leaderboard. No new dataset: EEGMAT’s rights record from the 2026-09-20 review is reused unchanged, with what this release publishes on it in the table below. EEGMAT keeps its released record: Open Data Commons Attribution License 1.0, aggregate results only. NeuroLM’s code is MIT at the pinned commit. The NeuroLM-B checkpoint is CC BY 4.0 as the licence tag of its Hugging Face repository states, and its 30-byte model card adds no terms. No weights are distributed, and no endorsement by NeuroLM’s authors is implied. Pretraining exposure is unknown. EEGMAT is NeuroLM’s Workload task, one of the six instruction tasks in its pinned code, and the training stage of this checkpoint is unresolved: its metadata (epoch 19, iteration 83,660, initialised from GPT-2, learning rate 0.0006) fits a pretraining checkpoint, and instruction tuning is unverified. The backbone may have seen these people, so exposure is shown as unknown. Not published: per-person, per-fold and per-window values of any kind, fold assignments, participant identifiers, condition labels, features, fitted heads, predictions and the sealed prediction files: only equal-person means, paired differences with their intervals and cohort counts are published; demographics from the source dataset’s participant file; measured compute: worker times, memory and process counts from the handoff, the receipts and the audits; the checkpoint and the NeuroLM source: no weights are distributed; the SHA-256 identifies the checkpoint used and grants nothing; the generation-mask adapter’s checks: engineering evidence that took no part in these scores; the IESSEEG intake and the watchlist leads of the same handoff: IESSEEG has its own release, and the leads have no results; the handoff, protocol, producer, receipts, reviews and input manifest themselves: each is pinned by SHA-256 in this manifest and checked by the export, and none is published, because several carry private storage paths. Release [`neurolm-eegmat-update-20261020`](https://bci.report/releases/#neurolm-eegmat-update-20261020).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — PhysioNet EEG During Mental Arithmetic Tasks 1.0.0 · 1.0.0 — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for NeuroLM’s frozen features: equal-person balanced accuracy and macro-F1 of three representations, three paired differences with their participant-bootstrap intervals, and cohort counts; no per-person, per-fold or per-window value. |

The model’s terms, part by part:

| Part | Terms | Read from |
| --- | --- | --- |
| Code — [935963004/NeuroLM ↗](https://github.com/935963004/NeuroLM/tree/0cda9876d8ce6ee07ed0c43eee5e9a6f5c24b177) at `0cda9876d8ce6ee07ed0c43eee5e9a6f5c24b177` | MIT — NeuroLM’s source code at the pinned commit | [LICENSE ↗](https://github.com/935963004/NeuroLM/blob/0cda9876d8ce6ee07ed0c43eee5e9a6f5c24b177/LICENSE) — SHA-256 `21bddd47eec5ad9a56bc9bec45f91426680e373f957b840fde984252b6718185` |
| Checkpoint — [Weibang/NeuroLM ↗](https://huggingface.co/Weibang/NeuroLM/tree/eddfff5c64a4139442f826d6c67c8369fd00f45a) at `eddfff5c64a4139442f826d6c67c8369fd00f45a`: `checkpoints/NeuroLM-B.pt`, SHA-256 `ffe098bc138b89f8817d3710a3604498d8ecd15135080e2ca27735d05c6d29ab` | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) — As the licence tag in the Hugging Face repository metadata at the pinned revision; its model card is 30 bytes and adds no terms. No weights are distributed; the SHA-256 identifies the checkpoint used and grants nothing. | Checked on 2026-10-16 |

**The model, as its authors publish it.** [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals ↗](https://arxiv.org/abs/2409.00101). NeuroLM is used here as a frozen feature extractor, and the transfer is supervised: the frozen NeuroLM outputs feed a linear head trained on the other folds’ labels. No endorsement by its authors is implied.

## New SSVEP frequencies: cards 8 to 14

Cards 8 to 14 of the new-SSVEP-frequencies page: follow-ups on SSVEP frequencies a decoder never trained on, each read from its own audited aggregate and published as aggregate results, outside every leaderboard. No new dataset: BETA’s and Wearable-102’s released records are reused unchanged, with what these cards publish on each in the table below. BETA keeps its released record: CC BY 4.0, cohort aggregates only, with participant consent described for noncommercial scientific research. Wearable-102 keeps its released record: CC BY 4.0, cohort aggregates only. Filter-bank CCA is credited to Chen et al. 2015; the code that ran is the project’s own SciPy realisation, and no code or weights are distributed. Not published: per-person, per-fold, per-rotation, per-seed and per-window values of any kind, predictions, scores, features, fitted heads and the sealed run files: only cohort levels, paired contrasts with their intervals, gap statistics, decision-change shares, check counts and CPU component medians are published; per-frequency values of any kind, including the reference check’s candidate frequencies: the check is published as counts; the cross-spectral per-seed MLP accuracies, the canary accuracies and the random-pairing accuracies of cards 8 and 9; the fixed-decay retrospective gap-to-gain ratio table and its dry reconstruction; all-trial decision counts: the harmonic-gap decision changes on BETA held-out are published as shares of the 11,200 decisions; the Wearable-102 decision changes and the fixed-decay and rising-weight transitions are not published; the harmonic-gap Delta means and the margins against the strongest joint-CCA distractor; run times of whole evaluations and audits, memory and process identifiers; the CPU-time readings of the component measurements and every host, processor, system and runtime label; predictions from the comment thread on cards 8 to 14: each new card states its prespecified criteria only, and the thread’s prediction stays on card 4 of the released file; the commenter’s name; the handoffs, protocols, freezes, receipts, reviews and approvals themselves: each is pinned by SHA-256 in the release manifest and checked by the export; several carry private storage paths. Release [`ssvep-followups-update-20261028`](https://bci.report/releases/#ssvep-followups-update-20261028).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [BETA ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) — Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. — Rights reviewed on 2026-09-20; reused unchanged from the released record. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) — Attribution required. Participant consent described use for non-commercial scientific research, a narrower operational boundary than the copyright license; minors required parental consent. Cohort aggregates only; no participant records, EEG signals, or individual predictions. | **Aggregate results** — Cohort aggregates over the 70 people for cards 8 to 14: levels and paired contrasts with their intervals, gap statistics in squared-correlation units, decision-change shares and check counts; and CPU component medians measured on BETA windows. No per-person, per-fold, per-rotation, per-window or per-frequency value. |
| [Wearable-102 · An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces ↗](https://figshare.com/articles/dataset/An_Open_Dataset_for_Wearable_SSVEP-Based_Brain-Computer_Interfaces/13560281) — Zhu, F., Jiang, L., Dong, G., Gao, X., & Wang, Y. (2021). An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces (Version 4) [Data set]. Figshare. https://doi.org/10.6084/m9.figshare.13560281.v4 — Official Tsinghua BCI Lab author mirror snapshot acquired 2026-09-20; the mirror itself has no separate DOI or version number. | CC BY 4.0 — As the [Figshare record ↗](https://doi.org/10.6084/m9.figshare.13560281.v4) states it. The paper describes consent for noncommercial scientific research. | **Aggregate results** — Cohort aggregates over the 102 people, by sensor, for cards 10, 12, 13 and 14: levels and paired contrasts with their intervals, gap statistics and check counts. No per-person, per-window, per-frequency or questionnaire value. |

**Filter-bank CCA.** Credited to [Chen X, Wang Y, Gao S, Jung T-P, Gao X. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface. Journal of Neural Engineering 12(4):046008, 2015. ↗](https://doi.org/10.1088/1741-2560/12/4/046008) Used here as filter-bank CCA (seven Chebyshev sub-bands, five harmonics), our SciPy realisation of the published recipe; no code is distributed. Route 3’s questions-in-language page runs another filter-bank recipe on the same task, with 5 Butterworth sub-bands.

## NeuroRVQ on three tasks: a separate pilot panel

NeuroRVQ’s released backbone, kept frozen and read by one fixed ridge head, on EEGMAT’s arithmetic and rest, on motor imagery against rest and on P300 target against non-target, published as aggregate scores in a panel of its own on the pilot-studies page, outside the pilot count and every leaderboard; the three protocol pages point to it with no figure. No new dataset: each task’s released rights record is reused unchanged, in the table below. NeuroRVQ’s code and weights are CC BY-NC 4.0, the licence file of both repositories at the pinned revisions; only aggregate figures are published, no weights are distributed, and no endorsement by NeuroRVQ’s authors is implied. EEGMAT, ds003810 and ds006593 keep their released records. Pretraining exposure is unknown: none of the three datasets is named in the paper’s list of 13 pretraining corpora, 1 of them proprietary (checked on 2026-10-25). Not published: per-person, per-fold and per-window values of any kind, fold assignments, participant identifiers, labels, extracted values, fitted heads, predictions and the sealed files: only participant-mean scores, one paired difference per task with its interval, and cohort counts are published; how many people each arm scored above 50%, which the sources give as descriptive point-estimate counts; per-arm intervals and macro-F1 differences: none were computed, so each arm has a point estimate only; measured compute: workflow, audit and CPU times, memory, budgets and process counts from the handoffs and receipts. None of them measures inference speed; the checkpoint and the authors’ source: no weights are distributed; the checkpoint SHA-256 identifies the file used and grants nothing. No hash of a source or Hugging Face file is published beyond the checkpoint SHA-256 and the two revisions; third-party conversions of the checkpoint and the licence tags they carry: only the authors’ repositories are cited; the handoffs, protocols, configurations, input queues, receipts and reviews themselves: each is pinned by SHA-256 in this manifest and checked by the export, and none is published, because several carry private storage paths. Release [`neurorvq-update-20261028`](https://bci.report/releases/#neurorvq-update-20261028).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — PhysioNet EEG During Mental Arithmetic Tasks 1.0.0 · 1.0.0 — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) — Personal noncommercial research; aggregate results only | **Aggregate scores** — Reused unchanged from the 2026-09-20 review. Paper reports Bioethics Commission approval (15 August 2018) and written consent from every participant. EDF dates are normalized, but subject-info.csv contains age, gender, occupation, and recording date; those fields must not be published. |
| [ds003810 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) — OpenNeuro snapshot 2.0.2 · c674303319303b9ffc4ff7e2340b3a4807e8ffb5 — Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/ | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) — Personal noncommercial research; aggregate results only | **Aggregate scores** — Reused unchanged from the 2026-09-20 review. Study paper reports ethics approval by CEySTE CCT-CONICET Santa Fe, Declaration of Helsinki compliance, and signed consent. Twelve adults participated; ten were retained. Public participants.tsv includes exact age, sex, handedness, and pseudonymous ID, which must not be surfaced. |
| [ds006593 ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) — OpenNeuro snapshot 1.0.0 · b3fa345b310abfcf442dd4e2867f6652336c3fe4 — OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source. | [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) — Personal noncommercial research; aggregate results only | **Aggregate scores** — Reused unchanged from the 2026-09-20 review. Dataset metadata/README report IRB approval #10-05-2022 and written informed consent. Participant metadata contains pseudonymous IDs and study descriptors without demographic fields. Eye-tracking and raw signals remain off-site. |

The model’s terms, part by part:

| Part | Terms | Read from |
| --- | --- | --- |
| Code — [KonstantinosBarmpas/NeuroRVQ ↗](https://github.com/KonstantinosBarmpas/NeuroRVQ/tree/926e770d9d16b6aa308404280fa0cc0211a6f9fb) at `926e770d9d16b6aa308404280fa0cc0211a6f9fb` | [CC BY-NC 4.0 ↗](https://creativecommons.org/licenses/by-nc/4.0/) — NeuroRVQ’s source code at the pinned revision | [LICENSE ↗](https://github.com/KonstantinosBarmpas/NeuroRVQ/blob/926e770d9d16b6aa308404280fa0cc0211a6f9fb/LICENSE) — Checked on 2026-10-25 |
| Checkpoint — [ntinosbarmpas/NeuroRVQ ↗](https://huggingface.co/ntinosbarmpas/NeuroRVQ/tree/d944b87f44ae0ba2923b2f10d0518f23f6803b76) at `d944b87f44ae0ba2923b2f10d0518f23f6803b76`: `pretrained_models/foundation_models/NeuroRVQ_EEG_foundation_model_v1.pt`, SHA-256 `d14ef4eae2d4594d5f1f709226a3bb5f362afd84b68050d5da0fa0586aea886a` | [CC BY-NC 4.0 ↗](https://creativecommons.org/licenses/by-nc/4.0/) — the NeuroRVQ checkpoints in the Hugging Face repository at the pinned revision, under the same licence file as the code. No weights are distributed; the SHA-256 identifies the checkpoint used and grants nothing. | [LICENSE ↗](https://huggingface.co/ntinosbarmpas/NeuroRVQ/blob/d944b87f44ae0ba2923b2f10d0518f23f6803b76/LICENSE) — Checked on 2026-10-25 |

**The model, as its authors publish it.** [arXiv 2510.13068 ↗](https://arxiv.org/abs/2510.13068). NeuroRVQ is used here frozen, and the transfer is supervised: the frozen backbone’s pooled values feed a ridge head fitted on the other folds’ labels. The authors’ affine inference wrapper fails a strict restore: eight normalisation tensors it expects are missing from the checkpoint. What ran is the released backbone with parameter-free branch normalisation and mean-token pooling.

## EEGMAT in ten-second windows: two frozen encoders

EEG-Captioner’s encoder and AXON, each kept frozen and read by one fixed ridge head on ten-second windows of EEGMAT’s arithmetic and rest, published as aggregate scores in a panel of its own on the pilot-studies page, outside the pilot count and every leaderboard. No new dataset: EEGMAT’s released record is reused unchanged, with what this release publishes on it in the table below. EEGMAT keeps its released record: Open Data Commons Attribution License 1.0, aggregate results only. EEG-Captioner’s source code is MIT. EEG-Captioner’s encoder weights are Apache-2.0 as its Hugging Face weight metadata states. AXON’s checkpoint is Apache-2.0 as its Hugging Face licence tag states; its Braindecode module carries per-file Apache-2.0 terms within a BSD-3-Clause project. No weights are distributed, and no endorsement by either model’s authors is implied. Pretraining exposure is unknown for both encoders. Not published: per-person, per-fold and per-window values of any kind: per-fold balanced accuracy, fold assignments, participant identifiers, features, fitted heads, predictions and the sealed files. Only participant-mean balanced accuracy, paired differences with their intervals, cohort counts and the source-QC totals are published; confusion matrices and pooled macro-F1: window-pooled summaries, a different aggregation from the participant means shown; the source-QC breakdown beyond the totals: the count of flagged channel-record instances and the counts by condition and by fold; workflow accounting: forward counts, head-fit counts, CPU and wall time and memory, from the handoffs, ledgers and audits; a difference between the two encoders: none is computed; a score on a cohort that meets EEG-Captioner’s source QC: no such study was selected, and one would need a new prospective protocol; AXON’s paper reference: not cited until checked; checkpoints and weights: none is distributed; each SHA-256 identifies the file used and grants nothing; the handoffs, protocols, approvals, reviews, audits and receipts themselves: each is pinned by SHA-256 in the release manifest and checked by the export, and none is published, because several carry private storage paths. Release [`eegmat-ten-second-update-20261028`](https://bci.report/releases/#eegmat-ten-second-update-20261028).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [EEGMAT ↗](https://physionet.org/content/eegmat/1.0.0/) — PhysioNet EEG During Mental Arithmetic Tasks 1.0.0 · 1.0.0 — Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z. | [Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) — Personal noncommercial research; aggregate results only | **Aggregate scores** — Reused unchanged from the review of the same recordings for the core matrix. Published here for two frozen encoders on ten-second windows: participant-mean balanced accuracy of each encoder and of Welch band power, two paired differences with their participant-bootstrap intervals, cohort counts and the source-QC totals; no per-person, per-fold or per-window value. |

The two encoders’ terms, part by part:

| Part | Terms | As stated by |
| --- | --- | --- |
| EEG-Captioner code — [lyJinAoki/EEG-Captioner ↗](https://github.com/lyJinAoki/EEG-Captioner) at `088d9af2dedcb181534f7fdcff7c913a19e10f04` | MIT | EEG-Captioner’s source code at the pinned commit |
| EEG-Captioner encoder weights — [LyJinAoki/EEG-Captioner ↗](https://huggingface.co/LyJinAoki/EEG-Captioner) at `1d56a371d68a108dd56c362cc41205c5b511cc76`, SHA-256 `060a5c0d9ac11543470038adbcbc5d9ba5828c1dc7c7d5097549435fbe28e989` | [Apache-2.0 ↗](https://www.apache.org/licenses/LICENSE-2.0) | the portable weight metadata of the Hugging Face repository at the pinned revision |
| AXON checkpoint — [MannasAI/axon-eeg ↗](https://huggingface.co/MannasAI/axon-eeg) at `fe7f85239e6018f393039a10a103bf7075c93aa6`: `model.safetensors`, SHA-256 `89c864a3ed947588ffc740cd4b342a2158661b13898c0dd9a86abebd33ffc882` | [Apache-2.0 ↗](https://www.apache.org/licenses/LICENSE-2.0) — Not gated | the licence tag of the Hugging Face repository at the pinned revision |
| AXON module — [braindecode/braindecode ↗](https://github.com/braindecode/braindecode) at `8c618f15a702f63d4899e3e8bc2e6f10a5f54151`: `braindecode.models.AXON` | Apache-2.0 | the per-file terms of the AXON module in Braindecode |
| Braindecode project | BSD-3-Clause | the licence at the Braindecode project root |

**Weights.** No checkpoint or weights are distributed: each SHA-256 identifies the file used and grants nothing.

## Dataset checks: Kim beta-range, MIND, Damm and NeBULA

Four public EEG datasets on the data-quality page, each read from its own files, outside the core matrix and every leaderboard. Kim beta-range SSVEP is published as aggregate scores: training-free CCA on all its participants, with a dataset page. MIND is published as aggregate scores on record IDs, as a conditional case, with the licence statements of its sources listed in the table below. Damm finger motor imagery and NeBULA have one participant each and are published as design, counts and file facts; their scores are held, as eldBETA’s is. The licences of Kim beta-range, Damm and NeBULA were read from each source’s own record, and the export records each read with its capture’s SHA-256; for MIND, the table says how each statement was read. Each ethics and consent statement comes from the document named beside it. Not published: values per person, per record ID, per fold, per run or per trial: identifiers, file and run names, fold assignments, features, fitted models, decision values and predictions; raw signals from any of the four datasets; every Damm and NeBULA score: accuracies, balanced accuracies, control values, differences, paired correctness counts and diagnostic fractions, held by the owner’s decision under the one-person rule; and any comparison of NeBULA’s order diagnostic with its EEG or EMG arms; MIND’s pooled metrics, confusion matrices and macro-F1, the secondary panel’s schedule census and differences, the overfull runs’ marker counts and the model-fit counts; Kim’s per-participant accuracies, the fixture identities of the independent audit and the stored-value ranges of the schema record; compute times, CPU and memory accounting, storage sizes, hosts and storage locations; the research aggregates, audits, reviews, protocols and handoffs themselves: each is pinned by SHA-256 in the release manifest and checked by the export, never published, because several carry private storage paths. Release [`dataset-checks-update-20261028`](https://bci.report/releases/#dataset-checks-update-20261028).

| Dataset | Source terms | What is published |
| --- | --- | --- |
| [Kim beta-range SSVEP: 40 targets from 14.0 to 21.8 Hz (Kim, Won, Ahn and Jun, Scientific Data) ↗](https://doi.org/10.6084/m9.figshare.28806815.v3) — Figshare record 28806815, version 3, the latest of the 3 versions the record lists — Heegyu Kim · SSVEP 40class dataset, figshare, doi:10.6084/m9.figshare.28806815.v3, CC BY 4.0. Paper: Heegyu Kim, Kyungho Won, Minkyu Ahn and Sung Chan Jun, A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications, Scientific Data 12:1751 (2025), doi:10.1038/s41597-025-06032-2. Modifications: the 40 task files only; the mean of M1 and M2 subtracted, 11 channels, a 2-second window and a band-pass filter inside it; no data redistributed. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) — As the licence field of Figshare record 10.6084/m9.figshare.28806815.v3 states it. The paper’s Data availability links version 2; the research used version 3 (the latest), which adds a questionnaire. Both versions carry CC BY 4.0 in the licence field. The paper’s own licence, Creative Commons Attribution 4.0 International License, covers the data descriptor article; the data licence is the Figshare record’s. | **Aggregate scores** — Ethics: Institutional Review Board at Gwangju Institute of Science and Technology (GIST), approval 20211201-HR-64-02-04. Consent: signed consent forms, including consent for the use and open sharing of the data. The confidentiality clause of the consent forms excludes questionnaire-related personal data; the research acquired the 40 task files only and excluded the questionnaire and rest files. Published here: the mean participant accuracy with its participant-bootstrap interval and aggregate counts; no per-participant value, identifier, prediction or signal. |
| [MIND: A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery (ScienceDB 10.57760/sciencedb.34326) ↗](https://doi.org/10.57760/sciencedb.34326) — ScienceDB record 10.57760/sciencedb.34326, record version 4.0.0 — Lufeng Feng, Baomin Xu, Haoran Zhang, Bihai Lin, Zuxuan Deng, Sidi Tao, Chenyu Liu, Shifan Jia, Li Duan and Ziyu Jia · MIND: A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery, Science Data Bank, doi:10.57760/sciencedb.34326. Paper: arXiv 2602.04299. Modifications: EEG only, filtered over each whole run, resampled and cut into stored-marker windows; no data redistributed. Derived analysis by BCI Report; not endorsed by the authors. | CC BY 4.0 — the paper (quoted from the research intake receipt of the paper page); CC BY 4.0 — the archive dataset description (as the research protocol records the dataset description inside the downloaded archive); CC BY-NC 4.0 — the MOABB adapter (the licence field of the MOABB dataset adapter); [CC BY-NC 4.0 ↗](https://creativecommons.org/licenses/by-nc/4.0/) — the ScienceDB record page, read on 2026-10-28 (the schema.org metadata the record page serves) — These statements are set side by side, not reconciled. ScienceDB’s terms-of-use page was not read. Aggregate values only; no data are redistributed. | **Aggregate scores, conditional** — The paper states that the study protocol was approved by an ethics committee it does not name, with no approval number, and that participants signed written informed consent. Published here: record-ID mean balanced accuracies, record-ID resampling ranges and aggregate counts; no per-record-ID value, identifier, prediction or signal. |
| [Damm finger motor imagery: Random and Sequential Order Finger Motor Imagery (OpenNeuro ds008446) ↗](https://doi.org/10.18112/openneuro.ds008446.v1.0.1) — OpenNeuro ds008446 at its latest snapshot, read at a pinned commit of its mirror — Laura Marie Damm, Dai Jiang and Andreas Demosthenous · Random and Sequential Order Finger Motor Imagery, OpenNeuro ds008446, doi:10.18112/openneuro.ds008446.v1.0.1, CC0. Funding: Engineering and Physical Sciences Research Council (EPSRC) grant EP/S022139/1. Modifications: the first participant’s two random-order runs only; no score from these data is published. Derived analysis by BCI Report; not endorsed by the authors. | CC0 — As the License field of [dataset_description.json at the pinned commit ↗](https://raw.githubusercontent.com/OpenNeuroDatasets/ds008446/8d8448e2dcd731b251f2f3c85b98cce5847cb000/dataset_description.json) states it. The README cites a LICENSE file that the tree at the pinned commit does not hold; the only licence statement is CC0 in dataset_description.json. | **Held — no figure** — Ethics: UCL Humanities, Arts and Sciences Research Ethics Committee, approval 26907/001. No consent statement was found in the dataset files or its metadata, and no paper is named. Published here: design, counts and file facts for one participant’s recordings and no score; no identifier, per-trial value or signal. |
| [NeBULA (Neuromechanical Biomarkers for Upper Limb Assessment): an EEG-EMG dataset from a standardized reaching task (Garro et al., Scientific Data 12, 831) ↗](https://doi.org/10.6084/m9.figshare.27301629.v1) — Figshare record 27301629, version 1, the only version the record lists — Florencia Garro, Elena Fenoglio, Indya Ceroni, Inna Forsiuk, Michele Canepa, Michael Mozzon, Agnese Bruschi, Francesco Zippo, Matteo Laffranchi, Lorenzo De Michieli, Stefano Buccelli, Michela Chiappalone and Marianna Semprini · An EEG-EMG Dataset from a Standardized Reaching Task for Biomarker Research in Upper Limb Assessment, figshare, doi:10.6084/m9.figshare.27301629.v1, CC BY 4.0. Paper: Florencia Garro, Elena Fenoglio, Indya Ceroni, Inna Forsiuk, Michele Canepa, Michael Mozzon, Agnese Bruschi, Francesco Zippo, Matteo Laffranchi, Lorenzo De Michieli, Stefano Buccelli, Michela Chiappalone and Marianna Semprini, An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment, Scientific Data 12:831 (2025), doi:10.1038/s41597-025-05042-4. Modifications: the first participant’s files only; no score from these data is published. Derived analysis by BCI Report; not endorsed by the authors. | [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) — As the licence field of Figshare record 10.6084/m9.figshare.27301629.v1 states it. The paper’s own licence, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, covers the data descriptor article only; the data licence is the Figshare record’s. The files’ own dataset description names Zenodo 13627307, which is no registered public record: the Zenodo API, the record page, DataCite and doi.org each return 404. The paper names Figshare alone. | **Held — no figure** — Ethics: local ethical committee CER Liguria, approval 784/2022 (database id 12044). Consent: informed consent before participation, including consent to share the data openly. Published here: design, counts and file facts for one participant’s recordings and no score; no identifier, per-trial value or signal. |

**Ethics and consent, as each source states them.** Kim beta-range SSVEP — ethics approval: “The Institutional Review Board at Gwangju Institute of Science and Technology (GIST) approved this experiment (20211201-HR-64-02-04)” Informed consent: “all subjects were informed of all experimental procedures and signed consent forms” Read from [www.nature.com ↗](https://www.nature.com/articles/s41597-025-06032-2). MIND — ethics approval: “The study protocol was approved by the Ethics Committee. All procedures followed institutional review board (IRB) guidelines and relevant privacy regulations.” Informed consent: “After confirming that they understood the instructions, all participants voluntarily signed written informed consent forms.” Quoted from the research intake receipt of the paper page, [arxiv.org ↗](https://arxiv.org/html/2602.04299v1). The committee is not named and no approval number is given in the paper. Damm finger motor imagery — ethics approval: “UCL Humanities, Arts and Sciences Research Ethics Committee; Approval Number: 26907/001” Informed consent: not stated. No consent statement in README.md, dataset_description.json, CHANGES or the OpenNeuro dataset metadata at the pinned commit; neither the README nor the metadata names a paper, so no paper was read. Read from [raw.githubusercontent.com ↗](https://raw.githubusercontent.com/OpenNeuroDatasets/ds008446/8d8448e2dcd731b251f2f3c85b98cce5847cb000/dataset_description.json). NeBULA (Neuromechanical Biomarkers for Upper Limb Assessment) — ethics approval: “The study’s experimental procedures were approved by the local ethical committee (CER Liguria:784/2022 - DB id 12044).” Informed consent: “All participants gave consent to participate and openly share the data collected.” Read from [www.nature.com ↗](https://www.nature.com/articles/s41597-025-05042-4).

## The open register

The open register lists every EEG dataset we know of, and every EEG model and paper we track, with the furthest step checked on its ladder and what that step showed. Listing is open: a row can record only that a dataset exists, where it is hosted and what licence its host states, and the register also lists sources that give a reader nothing to run, such as L-FAME, because it records what was checked. The publication rules on this page are unchanged: a score enters a table on this site only after its source review, as an aggregate result, with nothing upstream redistributed. The two ladders are on [the dataset register](https://bci.report/datasets/register/) and [the model register](https://bci.report/methods/register/), and the rows are downloadable as [open-register-20261028.json](https://bci.report/data/open-register-20261028.json) and [open-register-20261028.csv](https://bci.report/data/open-register-20261028.csv), release [`open-register-update-20261028`](https://bci.report/releases/#open-register-update-20261028).

**The register’s rules.** Gated data, or data that needs a signed or requested agreement, stays at step 1 unless we accept its terms. Suspected re-uploads stay at step 1 whatever their gating. They are compared by metadata only, and none of their files is downloaded. A finding about a re-upload is stated only when an independent check confirms it and a captured page of the original terms backs it. Rows state facts. Every row has a correction route. A row states what its host record, its files or its source’s own documents say, each with its source, and its step is the furthest one whose evidence is on file, with its gaps listed. Repository ids and usernames appear in a row’s text, never in a link or an anchor; an uploader’s series of repositories is one family row; card text, download counts, modification dates and demographics are not shown.

**Correcting a row.** Every row links a mail to [contact@bci.report](mailto:contact@bci.report?subject=Register%20row) whose subject names the row’s opaque id and nothing else. Anyone can ask for a row to be corrected, or for a finding to be checked again, including the uploader or the authors. A corrected row appears in the next dated register file; the earlier file keeps its bytes and its hash on the releases page, as every release does.

## What stays local

- Recordings, timestamps, subject mappings, labels and individual predictions remain outside the website.
- We do not publish diagnosis-specific small groups, identifiable example traces, or demographic profiles. Aggregate scores describe decoder performance, not a person’s health or ability.
- Source files with unclear provenance, incompatible terms or unresolved physical calibration do not enter published comparisons.

Removing a name or replacing it with a code does not by itself anonymize EEG. Open licenses address specified intellectual-property rights; they do not generally clear privacy and personality rights.

**An average over four people is close to its parts.** The idle protocol has a cohort of four, and at that size a published mean can be turned back into a count: 1.7% of 60 command trials is one detection, and since two of the four participants selected an always-abstain threshold, that detection belongs to one of the remaining two. We publish the abstention counts anyway, because a false-activation rate of zero produced by people who never activate is the more misleading number. What a reader cannot recover is which person is which — participants are unidentified here and in the public source recordings, and no demographic, session or ordering information is published that would let anyone line them up.

**One animal, two folders, five and eight people.** The same arithmetic holds for the smallest cohorts beyond the core matrix. MC_RTT is one macaque: every figure is that animal’s own, and no interval is reported; it is animal data, with no participant to identify. REFED’s two nominal participant folders share the same videos, and each published error is a mean over all of their clips together: no per-folder or per-clip error is published, because with two folders one of them and the published mean would give away the other. The Orthosis pilot has eight people: its intervals resample whole people, how many of them had EEG above the confound probe is published as a count, and no per-person minimum, maximum or range is. BINGO’s subject fields, as the source defines them, are not proven distinct people, and only means over all of them, with descriptive intervals, are published. The handwriting analysis has five people: its intervals resample whole people, and each bound is a percentile of resampled five-person means, not one person’s score. Its mask-only control is the exception: its scores sit so close to chance that its bounds would show individual people’s scores, so that interval is not published. The status-only sources with one participant each, FALCON H2 and the functional-ultrasound recordings, publish counts of trials, files, runs and frames that describe the public files, not the person; so do the Damm and NeBULA cases on the data-quality page, with counts of cues, trials, channels and markers.

**A median over an odd number of records is one record’s value.** The later-sessions page prints the cursor-tracking ridge’s median error beside its mean for each Forenzo cohort and response arm, and the Main cohort has an odd number of admitted records, so each of its printed medians is the error of one record. We publish those medians anyway, because a mean pulled far upwards by a few very large errors is the more misleading number without them. It does not identify anyone: the records are not proven to be distinct people, the page does not say which record the median is, and no participant, record or file identifier is published.

## How to interpret the comparisons

Each protocol states the cohort, selected electrodes, preprocessing, split and training budget. Frozen foundation encoders, scratch-trained networks and classical references are labeled separately. The stable matrix keeps its fixed published seeds; the deployment extension separately reports five three-seed EEGNet sensitivity groups. These comparisons and descriptive intervals do not establish optimal performance or clinical validation.

Pretraining exposure is stated as what each model’s authors publish as their pretraining list, checked on 4 October 2026 (EEGPT’s on 12 October 2026) and linked to the source on the protocol and method pages: a dataset absent from the list is “not in the authors’ published pretraining list”, which is not proof that its recordings were never seen, and where the authors publish no list (ZUNA 1.1) it is unknown. Four- and eight-electrode subsets of laboratory recordings do not validate a physical four- or eight-channel headset.

## Corrections and changes of use

Scientific corrections, benchmark-method questions, and rights, privacy or attribution concerns are all welcome, including ones that would withdraw a published result. Affected results can be withheld while an issue is reviewed.

**Report an issue**

Write to [contact@bci.report](mailto:contact@bci.report?subject=%5BBCI%20Report%20correction%5D%20) for scientific corrections and benchmark questions, or [privacy@bci.report](mailto:privacy@bci.report) for privacy, licensing and withdrawal requests.

This domain receives mail but does not send it, so a reply will come from the maintainer’s own mailbox rather than from @bci.report. Replying to the address above still reaches the same place.

When reporting, please include the page URL, the dataset and model, the release or protocol ID, a short description, and a public evidence link. **Do not send raw EEG, participant names, health records, passwords or identity documents**: an email address cannot refuse attachments. Identity proof is not requested. Published corrections are redacted scientific notes; the original message and sender details are not published.

Advertising, sponsorship, paid access, new hosting jurisdictions, data uploads or clinical applications require a new scope review. A noncommercial source label is not a blanket authorization for those uses.

## Website privacy

The application does not run analytics, advertising trackers or EEG processing. Hosting and access-control providers may process connection and sign-in information independently. External source links lead to sites with their own privacy practices.

This site is not a medical service and provides no diagnosis, treatment advice or participant-level assessment.

## Useful references

[Hugging Face terms](https://huggingface.co/terms-of-service) · [Creative Commons rights and attribution](https://creativecommons.org/licenses/by/4.0/legalcode.en)

Core sources reviewed: 2026-09-20.

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Markdown copy of https://bci.report/data-use/, generated from the published page. Figures are aggregate results; terms of use: https://bci.report/data-use/