{
  "schema_version": "bci-report-foundation-models-update-v1",
  "release_id": "foundation-models-update-20261004",
  "generated_at": "2026-10-04",
  "status": "aggregate_preview",
  "metric_units": "balanced accuracy, its participant-bootstrap interval, macro F1 and window balanced accuracy are proportions in [0,1]; window AUROC is in [0,1]; paired and descriptive changes are differences of proportions; idle figures, people, trials and parameters are whole numbers; null means not run, never zero. The per-protocol CSVs carry balanced accuracy and its interval in percent, as the core results CSVs do.",
  "scope": "New rows beside the core matrix, never merged into experiments.json: eleven further foundation models (sixteen encoder checkpoints) as frozen probes with the published recipe and only the encoder swapped, on the same eight protocols, people and folds; and nine of them adapted on EEGMAT with one fixed recipe. Grouped by family, never ranked; new people, same task and recording setup; no deployment or clinical claim.",
  "results": {
    "foundation-models-v9": {
      "id": "foundation-models-v9",
      "title": "Foundation models, v9: eleven further EEG encoders as frozen probes, and nine adapted on EEGMAT",
      "evaluation_id": "fm-eval-v9-20261004",
      "design": {
        "frozen_probe": "Each encoder replaces LaBraM or CBraMod in the published frozen-row recipe, and nothing else changes: the published 250 Hz microvolt windows and segments (1 s or 2 s; sleep as the mean of 15 two-second segments), the same participant-disjoint folds, the same heads (ridge, alpha 100, on standardised training-fold features, class-balanced except on BETA; idle: a per-person logistic head with a calibration-only threshold and a two-in-a-row trigger) and the same scoring. Only recorded channels are fed: no interpolation, padding or learned upsampling. Each input contract was written, and a label-free smoke test run, before any score; nothing was selected on test folds.",
        "adaptation": "The 2026-10-01 EEGMAT recipe, unchanged: a frozen encoder with a trained linear head against rank-4 LoRA (alpha 8) with the same head; AdamW, learning rate 1e-4, weight decay 0.01, five epochs, batch 32, the final epoch scored, seeds 20260922, 20260923 and 20260924, the same five participant-disjoint folds. A three-seed value is each person's mean over seeds, then the equal-person mean. LoRA targets follow each architecture, so the LoRA budgets differ.",
        "interval": "Descriptive 95% participant bootstrap of the equal-participant mean (10,000 draws, published seeds). It ignores cross-validation dependence and carries no multiplicity correction.",
        "harness": "Before any new score the shared harness reproduced the published LaBraM and CBraMod frozen rows and idle counts exactly, and all 45 published LaBraM EEGMAT adaptation fits.",
        "reading_rule": "Rows are grouped by family, never ranked. A row is called above or below another only when their marginal 95% intervals do not overlap; the same people under different encoders are not tested pairwise here. Balanced or class-matched designs are method comparisons, not detection, false-alarm or latency estimates for real use.",
        "chance": "An interval that includes the protocol's chance level is flagged on its cell; idle has no chance level.",
        "not_run": "A cell that was not run is null with its reason, never zero."
      },
      "protocols": [
        {
          "id": "mi-rest",
          "title": "Motor imagery & rest",
          "dataset": "ds003810",
          "type": "accuracy",
          "people": 10,
          "channels": 15,
          "chance_level": 0.5,
          "file": "foundation-models-mi-rest.csv",
          "core_files": [
            "mi-rest-results.csv",
            "mi-rest-protocol.json"
          ],
          "rights": {
            "name": "ds003810",
            "task": "Rest versus right-hand motor imagery, new people (participant-disjoint folds)",
            "source": "https://doi.org/10.18112/openneuro.ds003810.v2.0.2",
            "version": "OpenNeuro snapshot 2.0.2 · c674303319303b9ffc4ff7e2340b3a4807e8ffb5",
            "license": "CC0-1.0",
            "licenseUrl": "https://creativecommons.org/publicdomain/zero/1.0/",
            "attribution": "Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://doi.org/10.18112/openneuro.ds003810.v2.0.2",
              "https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/"
            ],
            "coreTrack": "mi-rest"
          }
        },
        {
          "id": "idle",
          "title": "Idle & command",
          "dataset": "ds005342",
          "type": "tradeoff",
          "people": 4,
          "channels": 17,
          "chance_level": null,
          "file": "foundation-models-idle.csv",
          "core_files": [
            "idle-results.csv",
            "idle-protocol.json"
          ],
          "rights": {
            "name": "ds005342",
            "task": "Seated motor imagery against idle, cue-gated replay, new people: command detection within 3 s and idle false activations, counted in test trials",
            "source": "https://doi.org/10.18112/openneuro.ds005342.v1.0.3",
            "version": "OpenNeuro snapshot 1.0.3 · 30bb3b67d76ce007b822fed801e496fafe5598a0",
            "license": "CC0-1.0",
            "licenseUrl": "https://creativecommons.org/publicdomain/zero/1.0/",
            "attribution": "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.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://doi.org/10.18112/openneuro.ds005342.v1.0.3",
              "https://eegdash.org/api/dataset/eegdash.dataset.DS005342.html",
              "https://doi.org/10.3389/fninf.2022.961089"
            ],
            "coreTrack": "idle"
          }
        },
        {
          "id": "beta-8ch",
          "title": "SSVEP · 8 channels",
          "dataset": "BETA",
          "type": "accuracy",
          "people": 70,
          "channels": 8,
          "chance_level": 0.025,
          "file": "foundation-models-beta-8ch.csv",
          "core_files": [
            "beta-8ch-results.csv",
            "beta-8ch-protocol.json"
          ],
          "rights": {
            "name": "BETA",
            "task": "40-target SSVEP speller, eight posterior electrodes, new people (participant-disjoint folds)",
            "source": "https://figshare.com/articles/dataset/The_BETA_database/12264401",
            "version": "Hugging Face mirror of BETA database, Figshare record 12264401 v3 · {\"mirror\": \"d4290c0200db8a104e0f557349dc49f90ba79506\", \"upstream\": \"Figshare version 3, 2022-06-15\"}",
            "license": "CC BY 4.0",
            "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
            "attribution": "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.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://figshare.com/articles/dataset/The_BETA_database/12264401",
              "https://doi.org/10.3389/fnins.2020.00627"
            ],
            "coreTrack": "beta-8ch"
          }
        },
        {
          "id": "beta-4ch",
          "title": "SSVEP · 4 channels",
          "dataset": "BETA",
          "type": "accuracy",
          "people": 70,
          "channels": 4,
          "chance_level": 0.025,
          "file": "foundation-models-beta-4ch.csv",
          "core_files": [
            "beta-4ch-results.csv",
            "beta-4ch-protocol.json"
          ],
          "rights": {
            "name": "BETA",
            "task": "40-target SSVEP speller, four posterior electrodes, new people (participant-disjoint folds)",
            "source": "https://figshare.com/articles/dataset/The_BETA_database/12264401",
            "version": "Hugging Face mirror of BETA database, Figshare record 12264401 v3 · {\"mirror\": \"d4290c0200db8a104e0f557349dc49f90ba79506\", \"upstream\": \"Figshare version 3, 2022-06-15\"}",
            "license": "CC BY 4.0",
            "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
            "attribution": "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.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://figshare.com/articles/dataset/The_BETA_database/12264401",
              "https://doi.org/10.3389/fnins.2020.00627"
            ],
            "coreTrack": "beta-4ch"
          }
        },
        {
          "id": "arithmetic-rest",
          "title": "Arithmetic & rest",
          "dataset": "EEGMAT",
          "type": "accuracy",
          "people": 36,
          "channels": 19,
          "chance_level": 0.5,
          "file": "foundation-models-arithmetic-rest.csv",
          "core_files": [
            "arithmetic-rest-results.csv",
            "arithmetic-rest-protocol.json"
          ],
          "rights": {
            "name": "EEGMAT",
            "task": "Rest versus serial-subtraction mental arithmetic, new people (participant-disjoint folds); also the EEGMAT adaptation recipe",
            "source": "https://physionet.org/content/eegmat/1.0.0/",
            "version": "PhysioNet EEG During Mental Arithmetic Tasks 1.0.0 · 1.0.0",
            "license": "Open Data Commons Attribution License 1.0",
            "licenseUrl": "https://opendatacommons.org/licenses/by/1-0/",
            "attribution": "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.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://physionet.org/content/eegmat/1.0.0/",
              "https://www.mdpi.com/2306-5729/4/1/14",
              "https://opendatacommons.org/licenses/by/1-0/"
            ],
            "coreTrack": "arithmetic-rest"
          }
        },
        {
          "id": "p300-target",
          "title": "P300 target ERP",
          "dataset": "ds006593",
          "type": "accuracy",
          "people": 21,
          "channels": 19,
          "chance_level": 0.5,
          "file": "foundation-models-p300-target.csv",
          "core_files": [
            "p300-target-results.csv",
            "p300-target-protocol.json"
          ],
          "rights": {
            "name": "ds006593",
            "task": "Visual target versus nontarget events (1-second windows), new people (participant-disjoint folds)",
            "source": "https://doi.org/10.18112/openneuro.ds006593.v1.0.0",
            "version": "OpenNeuro snapshot 1.0.0 · b3fa345b310abfcf442dd4e2867f6652336c3fe4",
            "license": "CC0-1.0",
            "licenseUrl": "https://creativecommons.org/publicdomain/zero/1.0/",
            "attribution": "OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://doi.org/10.18112/openneuro.ds006593.v1.0.0",
              "https://nemar.org/dataset/on006593"
            ],
            "coreTrack": "p300-target"
          }
        },
        {
          "id": "semantic-target",
          "title": "Semantic target ERP",
          "dataset": "TMNRED / ds005383",
          "type": "accuracy",
          "people": 30,
          "channels": 30,
          "chance_level": 0.5,
          "file": "foundation-models-semantic-target.csv",
          "core_files": [
            "semantic-target-results.csv",
            "semantic-target-protocol.json"
          ],
          "rights": {
            "name": "TMNRED / ds005383",
            "task": "Reading, target versus nontarget events (1-second windows), new people (participant-disjoint folds)",
            "source": "https://doi.org/10.18112/openneuro.ds005383.v1.0.0",
            "version": "OpenNeuro snapshot 1.0.0 · ad78f3db430e636595b1b1c08417492f3067a25e",
            "license": "OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0",
            "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
            "attribution": "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.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://doi.org/10.18112/openneuro.ds005383.v1.0.0",
              "https://www.nature.com/articles/s41597-025-05036-2"
            ],
            "coreTrack": "semantic-target"
          }
        },
        {
          "id": "sleep-scalp",
          "title": "Sleep staging",
          "dataset": "EESM19 scalp subset",
          "type": "accuracy",
          "people": 20,
          "channels": 6,
          "chance_level": 0.2,
          "file": "foundation-models-sleep-scalp.csv",
          "core_files": [
            "sleep-scalp-results.csv",
            "sleep-scalp-protocol.json"
          ],
          "rights": {
            "name": "EESM19 scalp subset",
            "task": "Five-stage sleep staging on a balanced scalp-EEG sample, new people (participant-disjoint folds)",
            "source": "https://doi.org/10.18112/openneuro.ds005185.v1.0.2",
            "version": "Hugging Face processed mirror of OpenNeuro ds005185 1.0.2 · {\"mirror\": \"fb045012b58a0b755f9fedf5931dff9b64a9797f\", \"upstream\": \"0857858f7a2ba1582930f23eca3ec56f90a96da9\"}",
            "license": "CC0-1.0 declared by upstream and mirror",
            "licenseUrl": "https://creativecommons.org/publicdomain/zero/1.0/",
            "attribution": "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.",
            "privacyReview": "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.",
            "reviewedAt": "2026-10-04",
            "reviewBasis": [
              "https://doi.org/10.18112/openneuro.ds005185.v1.0.2",
              "https://www.nature.com/articles/s41598-019-53115-3",
              "https://doi.org/10.1038/s41597-025-04579-8"
            ],
            "coreTrack": "sleep-scalp"
          }
        }
      ],
      "models": [
        {
          "id": "reve-base",
          "name": "REVE Base",
          "parameters_encoder": 69189632,
          "parameters_note": "encoder parameters, strict load of 140 tensors",
          "checkpoint_sha256": "8ecc650619598748286c2457f81f5c6bd12e8bb59db44f7b02af1955c44de8fe",
          "revision": "brain-bzh/reve-base @ dc2a075c",
          "paper": "https://arxiv.org/abs/2510.21585",
          "weights_licence": "REVE Responsible Use License v1.0 (owner-approved 2026-10-04)",
          "licence_note": "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.",
          "row_footnote": "Input removes the per-segment per-channel mean before the published /100 scaling (declared before scoring); a no-mean-removal sensitivity run scores P300 0.71 percentage points higher and sleep 1.40 lower.",
          "family": "foundation",
          "input_family": "montage",
          "panel": "matrix",
          "masking_ablation": false,
          "mode": "Frozen encoder + ridge head",
          "idle_mode": "Frozen encoder + linear head",
          "adaptation": "run",
          "rights_review": "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.",
          "notes": [
            "REVE input removes the per-segment per-channel mean (declared before scoring; the published LaBraM/CBraMod rows did not); a no-mean-removal sensitivity run is stated in the row footnote."
          ]
        },
        {
          "id": "reve-large",
          "name": "REVE Large",
          "parameters_encoder": 389978496,
          "parameters_note": "encoder parameters; paper Table 6 says 408M / dim 1250, checkpoint is dim 1216",
          "checkpoint_sha256": "f519428404aab27f7db816722c7fb8c73d08daf10986c9c21b0066be9e93bb31",
          "revision": "brain-bzh/reve-large @ 317531c7",
          "paper": "https://arxiv.org/abs/2510.21585",
          "weights_licence": "REVE Responsible Use License v1.0 (owner-approved 2026-10-04)",
          "licence_note": "REVE Responsible Use License v1.0 (as REVE Base).",
          "row_footnote": "Input removes the per-segment per-channel mean (declared before scoring); the no-mean-removal sensitivity run scores P300 2.06 percentage points lower and sleep 0.93 lower.",
          "family": "foundation",
          "input_family": "montage",
          "panel": "matrix",
          "masking_ablation": false,
          "mode": "Frozen encoder + ridge head",
          "idle_mode": "Frozen encoder + linear head",
          "adaptation": "run",
          "rights_review": "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.",
          "notes": [
            "REVE input removes the per-segment per-channel mean (declared before scoring); a no-mean-removal sensitivity run is stated in the row footnote."
          ]
        },
        {
          "id": "luna-base",
          "name": "LUNA Base",
          "parameters_encoder": 6572857,
          "parameters_note": "encoder parameters after dropping decoder head and channel_emb",
          "checkpoint_sha256": "482839ad9152b6948ff166d2a1638637c54b4d91f1cefadbce6312831ba9b11d",
          "revision": "PulpBio/LUNA @ 999c1af0",
          "paper": "https://arxiv.org/abs/2510.22257",
          "weights_licence": "CC BY-ND 4.0 (owner-approved: internal adaptation, aggregate scores only, no adapted weights or LoRA deltas shared)",
          "licence_note": "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.",
          "row_footnote": "Resampled to 256 Hz and truncated to whole 40-sample patches; 1-2 s windows and 4-8 channel montages are outside the authors' tested regime.",
          "family": "foundation",
          "input_family": "montage",
          "panel": "matrix",
          "masking_ablation": false,
          "mode": "Frozen encoder + ridge head",
          "idle_mode": "Frozen encoder + linear head",
          "adaptation": "run",
          "rights_review": "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.",
          "notes": [
            "LUNA resamples to 256 Hz and keeps whole 40-sample patches: 1 s keeps 240/256 samples, 2 s keeps 480/512."
          ]
        },
        {
          "id": "luna-large",
          "name": "LUNA Large",
          "parameters_encoder": 40449577,
          "parameters_note": "encoder parameters",
          "checkpoint_sha256": "03e0321a1e1a30dc074f7bfe512a901138200494f7694f31ab633e42b105c0e4",
          "revision": "PulpBio/LUNA @ 999c1af0",
          "paper": "https://arxiv.org/abs/2510.22257",
          "weights_licence": "CC BY-ND 4.0 (owner-approved terms as LUNA Base)",
          "licence_note": "CC BY-ND 4.0 weights (as LUNA Base).",
          "row_footnote": "As LUNA Base. Frozen probes only.",
          "family": "foundation",
          "input_family": "montage",
          "panel": "matrix",
          "masking_ablation": false,
          "mode": "Frozen encoder + ridge head",
          "idle_mode": "Frozen encoder + linear head",
          "adaptation": "not run by design: LUNA Large is frozen probes only in the v9 stage specification",
          "rights_review": "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.",
          "notes": [
            "LUNA resamples to 256 Hz and keeps whole 40-sample patches: 1 s keeps 240/256 samples, 2 s keeps 480/512."
          ]
        },
        {
          "id": "brainomni-base",
          "name": "BrainOmni Base",
          "parameters_encoder": 31836992,
          "parameters_note": "parameters on the encoder path (tokenizer encoder + 11 LM blocks)",
          "checkpoint_sha256": "435db24e57a55df05aa7e16355def7b7ecbedb22aa1ec16063e7d14efd2386d0",
          "revision": "OpenTSLab/BrainOmni @ 9a4d3c70",
          "paper": "https://arxiv.org/abs/2505.18185",
          "weights_licence": "MIT",
          "licence_note": "MIT.",
          "row_footnote": "p300-target and semantic-target not run: the 2 s tokenizer window cannot be met by 1 s segments without zero padding. Upstream common average reference kept on 4-8 channel montages.",
          "family": "foundation",
          "input_family": "montage",
          "panel": "matrix",
          "masking_ablation": false,
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          {
            "protocol": "beta-8ch",
            "all_pairwise_marginal_intervals_overlap": true,
            "non_overlapping_pairs": []
          },
          {
            "protocol": "beta-4ch",
            "all_pairwise_marginal_intervals_overlap": true,
            "non_overlapping_pairs": []
          },
          {
            "protocol": "arithmetic-rest",
            "all_pairwise_marginal_intervals_overlap": true,
            "non_overlapping_pairs": []
          },
          {
            "protocol": "p300-target",
            "all_pairwise_marginal_intervals_overlap": true,
            "non_overlapping_pairs": []
          },
          {
            "protocol": "semantic-target",
            "all_pairwise_marginal_intervals_overlap": true,
            "non_overlapping_pairs": []
          },
          {
            "protocol": "sleep-scalp",
            "all_pairwise_marginal_intervals_overlap": false,
            "non_overlapping_pairs": [
              [
                "eeg-fm-masking/jepa-r9cm-L2",
                "eeg-fm-masking/mae-rone-L1"
              ]
            ]
          }
        ]
      },
      "pretraining_exposure": {
        "checked_on": "2026-10-04",
        "definition": "'exposed' means the same recordings, or a superset or derivative of them, were in the model's self-supervised pretraining data. Use of a dataset for downstream fine-tuning or probing by the model's own authors is recorded separately as author_downstream_use and is not counted as exposure.",
        "status_vocabulary": {
          "exposed": "A primary source names the dataset (or a superset/derivative) in the pretraining corpus.",
          "not_exposed": "A primary source enumerates the pretraining corpus (paper, official code or dataset card) and the dataset is absent, or the source explicitly excludes it.",
          "unknown": "The primary sources do not enumerate the corpus closely enough to decide; 'lean' gives the direction and the reason."
        },
        "statements": {
          "exposed": "in the authors' published pretraining list",
          "not_exposed": "not in the authors' published pretraining list (checked 2026-10-04)",
          "unknown": "unknown: the authors do not list their pretraining data closely enough to decide"
        },
        "wording_rule": "Owner decision, 2026-10-04: a model's exposure is stated as what its authors' published pretraining list shows, checked on a date and linked to the source; never as proven absence of overlap. Recording-level audits were not done: exposure is decided per dataset. A cell's confidence rates how closely its source enumerates the corpus, not how certain it is that the recordings were never seen.",
        "method": [
          "Read each model's primary source on 2026-10-04: the paper's pretraining section or appendix (arXiv HTML; an Internet Archive copy of the OpenReview PDF for ST-EEGFormer), the official repository code and README, and the HF model or dataset cards.",
          "Matched each core dataset by OpenNeuro accession, title, first author and reference, and checked known aliases (MAT/MentalArithmetic, TMNRED, BETA/Liu2020, EESM19/ds005185, TrianaGuzman2024).",
          "Checked hosting for repository-level corpora: all 1,909 public OpenNeuro datasets were listed by name through the OpenNeuro GraphQL API (no copy of EEGMAT or BETA), and MOABB loader commit dates were checked for BETA and ds005342 (both first added in March 2026).",
          "Used publication dates only as supporting evidence, never as the sole basis."
        ],
        "datasets": [
          {
            "key": "ds003810",
            "name": "ds003810 Motor Imagery vs Rest (low-cost EEG; Peterson et al.)",
            "protocols": [
              "mi-rest"
            ],
            "url": "https://doi.org/10.18112/openneuro.ds003810.v2.0.2"
          },
          {
            "key": "eegmat",
            "name": "EEGMAT (PhysioNet EEG During Mental Arithmetic Tasks; Zyma et al. 2019)",
            "protocols": [
              "arithmetic-rest"
            ],
            "url": "https://physionet.org/content/eegmat/1.0.0/"
          },
          {
            "key": "ds006593",
            "name": "ds006593 cBCI Matrix Multimodal Dataset (Celik et al.)",
            "protocols": [
              "p300-target"
            ],
            "url": "https://doi.org/10.18112/openneuro.ds006593.v1.0.0"
          },
          {
            "key": "ds005383",
            "name": "ds005383 TMNRED (Bai et al. 2025, Sci. Data 12:701)",
            "protocols": [
              "semantic-target"
            ],
            "url": "https://doi.org/10.18112/openneuro.ds005383.v1.0.0"
          },
          {
            "key": "eesm19",
            "name": "EESM19 Ear-EEG Sleep Monitoring 2019 (OpenNeuro ds005185; Mikkelsen et al.)",
            "protocols": [
              "sleep-scalp"
            ],
            "url": "https://doi.org/10.18112/openneuro.ds005185.v1.0.2"
          },
          {
            "key": "beta",
            "name": "BETA 40-target SSVEP (Liu et al. 2020, Front. Neurosci. 14:627)",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "url": "https://figshare.com/articles/dataset/The_BETA_database/12264401"
          },
          {
            "key": "ds005342",
            "name": "ds005342 EEG offline/online MI for standing and sitting (Triana-Guzman et al.)",
            "protocols": [
              "idle"
            ],
            "url": "https://doi.org/10.18112/openneuro.ds005342.v1.0.3"
          }
        ],
        "models": [
          {
            "key": "st-eegformer",
            "name": "ST-EEGFormer (small / base / large; largeV2)",
            "variants_covered": "small, base, large share one MAE corpus; largeV2 = same + further pretraining on HBN (README)",
            "evaluated_in_v9": true,
            "corpus_summary": "11 public datasets + the authors' own in-house multi-paradigm lab recordings (App. E.2: 'an in-house EEG dataset covering multiple paradigms (e.g., MI and SSVEP)'), 128 Hz, >8M segments: EEG-MI-BCI (Cho 2017), HGD (Schirrmeister 2017), BCI-Comp-IV-2a, BCI-Comp-IV-2b, Large-MI-Classic and Large-MI-5F (Kaya 2018), P300 (Won 2022), SSVEP (Liu 2020 = BETA), Online MI BCI (Stieger 2021), KUL auditory (Bollens 2023), SEED-V (Liu 2022). Paper App. E.2 (p. 25) and E.4 (p. 26). The secondary summaries quoted in v8 ('11 public datasets incl. TUEG and SHHS') were wrong: TUEG and SHHS appear only in the paper's Table C.1 describing other models.",
            "corpus_listing": "enumerated in paper appendix (plus unnamed in-house lab data)",
            "primary_sources": [
              "https://openreview.net/forum?id=5Xwm8e6vbh",
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "notes": "OpenReview PDF/API return a bot challenge (403) to scripted access; it was not bypassed. The paper was read from the Internet Archive copy of the same OpenReview PDF (snapshot 2026-06-09, sha256 0dc081210d8357405ba3778e2d75fb6a8289c414c58d1a8a6b08551b7564af27, 39,367,956 bytes).",
            "checked_on": "2026-10-04"
          },
          {
            "key": "luna",
            "name": "LUNA (Base / Large / Huge)",
            "variants_covered": "all three sizes",
            "evaluated_in_v9": true,
            "corpus_summary": "TUEG (14,987 subjects, ~21,787 h) + Siena Scalp EEG Database (14 subjects, ~141 h); >21,900 h. arXiv Sec 4.1 and Table 11.",
            "corpus_listing": "enumerated in paper",
            "primary_sources": [
              "https://arxiv.org/abs/2510.22257",
              "https://huggingface.co/PulpBio/LUNA"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "zuna-1.1",
            "name": "ZUNA1.1",
            "variants_covered": "ZUNA1.1 (380M); ZUNA1 lineage used as context",
            "evaluated_in_v9": true,
            "corpus_summary": "ZUNA1: TUH EEG Corpus + 'a large collection of publicly available datasets hosted on OpenNeuro', 208 datasets with resolvable 3D electrode positions, ~2M channel-hours; no per-dataset list (arXiv 2602.18478 Sec II). ZUNA1.1: ~3.5M channel-hours of 'processed and cleaned EEG data'; the corpus grew through quality-aware loading and by ingesting 'pre-epoched datasets the original pipeline could not use' (arXiv 2607.27308 Sec I and III-E). The ZUNA1.1 paper names no sources and gives no dataset list. Repository configs show only 'ds*' (OpenNeuro) and 'tuh*' shard patterns. Held-out evaluation sets named: ANPHY-Sleep, BerlinBCI (BCI Comp III V), BCI2000 (PhysioNet eegmmidb), 255-ch AAD.",
            "corpus_listing": "repository-level only (no per-dataset list for ZUNA1 or ZUNA1.1)",
            "primary_sources": [
              "https://arxiv.org/abs/2607.27308",
              "https://arxiv.org/abs/2602.18478",
              "https://huggingface.co/Zyphra/ZUNA1.1",
              "https://github.com/Zyphra/zuna/blob/main/src/zuna/inference/AY2l/lingua/apps/AY2latent_bci/configs/config_bci_eval.yaml"
            ],
            "notes": "The v8 note 'whitepaper coming soon' is out of date: arXiv 2607.27308 (2026-07-29) is the ZUNA1.1 paper, but it still lists no datasets.",
            "checked_on": "2026-10-04"
          },
          {
            "key": "reve-base",
            "name": "REVE Base",
            "variants_covered": "REVE Base",
            "evaluated_in_v9": true,
            "corpus_summary": "61,415 h, 92 datasets, 24,274 subjects (Table 7: TUH 1, PhysioNet 2, OpenNeuro 56, MOABB 27, Other 6). App. B names 25 MOABB sets, Siena + I-CARE, 55 OpenNeuro accessions, NMT, HMS, SparrKULee, Inria Large, THINGS2, TDBRAIN and TUH. The open-subset dataset card adds ds004262 (so OpenNeuro = 56, matching Table 7) and Schalk 2004/PhysioNet MI; one MOABB slot of 27 remains unnamed. Sec 3.1.1: recordings 'used in downstream tasks' were removed (downstream includes MAT = EEGMAT). The paper trains the Small/Base/Large suite on this corpus.",
            "corpus_listing": "enumerated in paper App. B (one MOABB slot unnamed)",
            "primary_sources": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset",
              "https://huggingface.co/brain-bzh/reve-base"
            ],
            "notes": "The v8 'p300 overlap' flag concerned BNCI2014-008/009, which are in REVE's corpus but are not the p300-target source; p300-target is ds006593.",
            "checked_on": "2026-10-04"
          },
          {
            "key": "reve-large",
            "name": "REVE Large",
            "variants_covered": "REVE Large",
            "evaluated_in_v9": true,
            "corpus_summary": "61,415 h, 92 datasets, 24,274 subjects (Table 7: TUH 1, PhysioNet 2, OpenNeuro 56, MOABB 27, Other 6). App. B names 25 MOABB sets, Siena + I-CARE, 55 OpenNeuro accessions, NMT, HMS, SparrKULee, Inria Large, THINGS2, TDBRAIN and TUH. The open-subset dataset card adds ds004262 (so OpenNeuro = 56, matching Table 7) and Schalk 2004/PhysioNet MI; one MOABB slot of 27 remains unnamed. Sec 3.1.1: recordings 'used in downstream tasks' were removed (downstream includes MAT = EEGMAT). The paper trains the Small/Base/Large suite on this corpus.",
            "corpus_listing": "enumerated in paper App. B (one MOABB slot unnamed)",
            "primary_sources": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset",
              "https://huggingface.co/brain-bzh/reve-large"
            ],
            "notes": "The v8 'p300 overlap' flag concerned BNCI2014-008/009, which are in REVE's corpus but are not the p300-target source; p300-target is ds006593.",
            "checked_on": "2026-10-04"
          },
          {
            "key": "codebrain",
            "name": "CodeBrain",
            "variants_covered": "released checkpoint",
            "evaluated_in_v9": true,
            "corpus_summary": "TUEG only: 1,109,545 samples (~9,246 h) after preprocessing (arXiv 2506.09110 v4, pretraining section).",
            "corpus_listing": "enumerated in paper (single source)",
            "primary_sources": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "eegmamba",
            "name": "EEGMamba (Wang et al. 2025)",
            "variants_covered": "released checkpoint (HF weighting666/EEGMamba); Neural Networks 2025 model from the CBraMod group, not the unrelated arXiv 2407.20254 EEGMamba",
            "evaluated_in_v9": true,
            "corpus_summary": "Abstract: 16,724 h 'from five datasets'. The official repo's pretrain_main.py lists exactly five: TUEG (200 Hz processed), PhysioNet/CinC 2018 sleep PSG, 'Raw EEG Data' (64-ch BioSemi .bdf, 60 Hz notch; matches the Trujillo 2020 'Raw EEG Data' set that LaBraM also used), Siena Scalp EEG, and 'szbd' (62-ch resting-state FIF files from a '62_rest_4classes' set, a clinical resting-state collection). Paper full text is paywalled and was not read.",
            "corpus_listing": "enumerated in official code; count matches abstract; two entries identified from preprocessing scripts, not from the paper",
            "primary_sources": [
              "https://doi.org/10.1016/j.neunet.2025.107816",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/",
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://arxiv.org/abs/2405.18765"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "erp-fm",
            "name": "ERP-FM (base)",
            "variants_covered": "base model (ERP-only pretraining)",
            "evaluated_in_v9": true,
            "corpus_summary": "38 task-level ERP datasets from 22 families (App. A Table 11): AUD-MAB ds005907, AUD-PS ds004515, AVSPP ds003190, AVSS ds002893, CCT-MJAH ds004860, EPSD ds003474, ERP CORE x7 (OSF), Go-Nogo ds002680, HBN-EEG CCD/SUS/SYS (releases 1-11), HeartBEAM ds006480, IMS-ODD ds003570, MMPST x4 (ds004315, ds004317), MRI-AODD ds003061, mTBI DPX/ODD/VWM (ds005114, ds003522, ds003523), NAFPS ds005565, PLAF x2 (ds003822, ds003753), PSTCC ds004532, Runabout ds003620, SICE (Dryad), SIMCC x2 ds003518, TABG ds003458, VWMCC ds003519. Table 12 non-ERP sets (BACA-RS, CAUEEG, P-ADIC, TDBrain, TUEP) are ablation-only.",
            "corpus_listing": "enumerated in paper and README",
            "primary_sources": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "singlem",
            "name": "SingLEM",
            "variants_covered": "all three released checkpoints: singlem_downstream_excluded.pt (primary, 68 sets), singlem_downstream_included.pt (71 sets), singlem_no_feature_embedding.pt (68 sets)",
            "evaluated_in_v9": true,
            "corpus_summary": "71 public datasets, >10,200 multichannel h (arXiv v3 Table I). The primary checkpoint drops only the three downstream sources (Dreyer_MI_25, WBCIC_MI_23, ATTEN_28 = Shin 2018); every other Table I dataset is in all three checkpoints (README 'Checkpoints').",
            "corpus_listing": "enumerated in paper (name/reference level; accession numbers mostly not given)",
            "primary_sources": [
              "https://arxiv.org/abs/2509.17920v3",
              "https://github.com/ttlabtuat/SingLEM"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "brainomni",
            "name": "BrainOmni (tiny / base) + BrainTokenizer",
            "variants_covered": "tiny and base",
            "evaluated_in_v9": true,
            "corpus_summary": "1,997 h EEG + 656 h MEG (Sec 3.1; App. H). EEG: Go-Nogo, MusicEEG, HFO (paediatric epilepsy sleep EEG), SRM, RestCog, HBN EO/EC, Features-EEG, PEARL-Neuro, Kymata-SOTO (EEG part), HBN-EEG, Awakening. ds005697 (EEG) and ds005261 (MEG) are held out (constant.py).",
            "corpus_listing": "enumerated in paper appendix",
            "primary_sources": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "eeg-fm-masking",
            "name": "eeg-fm-masking encoders (MAE / JEPA, REVE-Small backbone)",
            "variants_covered": "all 58 released encoders (one shared corpus)",
            "evaluated_in_v9": true,
            "corpus_summary": "Open-licence subset of the REVE pretraining corpus, ~4.4 TB (App. B lists every source: 56 OpenNeuro sets incl. ds004902 and HBN releases, MOABB-style BCI sets incl. Schalk 2004 PhysioNet-MI, Cho 2017, Lee 2019 x3, Kalunga, Brain Invaders, Sosulski, plus I-CARE).",
            "corpus_listing": "enumerated in paper appendix and HF dataset card",
            "primary_sources": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "notes": "",
            "checked_on": "2026-10-04"
          },
          {
            "key": "labram",
            "name": "LaBraM",
            "variants_covered": "LaBraM-Base checkpoint from the official repo",
            "evaluated_in_v9": false,
            "corpus_summary": "~2,534.78 h (App. D): BCI Competition IV-1, Emobrain, Grasp and Lift, Inria BCI Challenge, PhysioNet eegmmidb, Raw EEG Data (Trujillo 2020), Resting State (Trujillo 2017), SEED series, Siena, SPIS, Target vs Non-Target (Brain Invaders), TUAR, TUEP, TUSZ, TUSL, and the authors' self-collected SJTU data (62-ch NeuroScan, 342 h).",
            "corpus_listing": "enumerated in paper appendix",
            "primary_sources": [
              "https://arxiv.org/abs/2405.18765",
              "https://github.com/935963004/LaBraM"
            ],
            "notes": "On the site now. ds005342, ds005383, ds006593 and EESM19 (ds005185) became public on OpenNeuro after the LaBraM preprint (2024-05-29), which independently rules them out.",
            "checked_on": "2026-10-04"
          },
          {
            "key": "cbramod",
            "name": "CBraMod",
            "variants_covered": "official released checkpoint",
            "evaluated_in_v9": false,
            "corpus_summary": "TUEG only (Sec 3.1 'Pre-training Dataset').",
            "corpus_listing": "enumerated in paper (single source)",
            "primary_sources": [
              "https://arxiv.org/abs/2412.07236",
              "https://github.com/wjq-learning/CBraMod"
            ],
            "notes": "On the site now.",
            "checked_on": "2026-10-04"
          }
        ],
        "cells": [
          {
            "model": "st-eegformer",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 11 enumerated public pretraining datasets (App. E.2); the only other source is the authors' own in-house lab recordings, which cannot be this third-party public dataset. largeV2 adds only HBN.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "st-eegformer",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 11 enumerated public pretraining datasets (App. E.2); the only other source is the authors' own in-house lab recordings, which cannot be this third-party public dataset. largeV2 adds only HBN.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "st-eegformer",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 11 enumerated public pretraining datasets (App. E.2); the only other source is the authors' own in-house lab recordings, which cannot be this third-party public dataset. largeV2 adds only HBN.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "st-eegformer",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 11 enumerated public pretraining datasets (App. E.2); the only other source is the authors' own in-house lab recordings, which cannot be this third-party public dataset. largeV2 adds only HBN.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "st-eegformer",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 11 enumerated public pretraining datasets (App. E.2); the only other source is the authors' own in-house lab recordings, which cannot be this third-party public dataset. largeV2 adds only HBN.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "st-eegformer",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "exposed",
            "confidence": "high",
            "evidence": "App. E.2 item 8 'SSVEP (Liu et al., 2020)': 70 participants, 40 targets 8-15.8 Hz, 20 trials x 5 s per target; the reference is 'BETA: A large BEnchmark database toward SSVEP-BCI application', Front. Neurosci. 14:627 (2020). App. E.4: 2-s windows with 0.125-s hop were cut from the 5-s stimulation epochs (the same 2-s window length as beta-8ch/beta-4ch). 20% of each dataset was held out for validation, but the split unit is not stated, so all 70 BETA participants are treated as exposed. Applies to all released variants.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://openreview.net/forum?id=5Xwm8e6vbh"
            ],
            "statement": "in the authors' published pretraining list",
            "basis": "named in pretraining list"
          },
          {
            "model": "st-eegformer",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 11 enumerated public pretraining datasets (App. E.2); the only other source is the authors' own in-house lab recordings, which cannot be this third-party public dataset. largeV2 adds only HBN.",
            "urls": [
              "https://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh",
              "https://github.com/LiuyinYang1101/STEEGFormer"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "luna",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Corpus is TUEG + Siena only (Sec 4.1, Table 11); this dataset is in neither.",
            "urls": [
              "https://arxiv.org/abs/2510.22257"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "zuna-1.1",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "OpenNeuro is a named ZUNA1 source (208 datasets kept if 3D positions resolved) and no per-dataset list exists for ZUNA1 or ZUNA1.1. This dataset was public on OpenNeuro before ZUNA1 (2026-02-09), and its scalp channels carry standard 10-20/10-10 labels (the published LaBraM/CBraMod rows already map them), so it would pass the position filter. It can be neither confirmed nor excluded.",
            "urls": [
              "https://arxiv.org/abs/2602.18478",
              "https://arxiv.org/abs/2607.27308",
              "https://github.com/Zyphra/zuna/blob/main/src/zuna/inference/AY2l/lingua/apps/AY2latent_bci/configs/config_bci_eval.yaml",
              "https://doi.org/10.18112/openneuro.ds003810.v2.0.2"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "plausible (meets the stated inclusion rule)"
          },
          {
            "model": "zuna-1.1",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "EEGMAT is distributed by PhysioNet. A name scan of all 1,909 public OpenNeuro datasets (GraphQL API, 2026-10-04) found no copy, and it is not part of TUH; those were ZUNA1's only named sources. ZUNA1.1 names no sources at all, so absence cannot be confirmed. The ZUNA1.1 paper also uses a 'Mental Arithmetic' set as a downstream classification probe (NeuralBench tables in the appendix) without saying which copy; that is evaluation use, not pretraining.",
            "urls": [
              "https://arxiv.org/abs/2607.27308",
              "https://arxiv.org/abs/2602.18478",
              "https://openneuro.org/crn/graphql",
              "https://physionet.org/content/eegmat/1.0.0/"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "unlikely (not on either repository ZUNA1 drew from)",
            "author_downstream_use": "ZUNA1.1 paper reports a 'Mental Arithmetic' downstream probe (copy not identified)"
          },
          {
            "model": "zuna-1.1",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "OpenNeuro is a named ZUNA1 source (208 datasets kept if 3D positions resolved) and no per-dataset list exists for ZUNA1 or ZUNA1.1. This dataset was public on OpenNeuro before ZUNA1 (2026-02-09), and its scalp channels carry standard 10-20/10-10 labels (the published LaBraM/CBraMod rows already map them), so it would pass the position filter. It can be neither confirmed nor excluded.",
            "urls": [
              "https://arxiv.org/abs/2602.18478",
              "https://arxiv.org/abs/2607.27308",
              "https://github.com/Zyphra/zuna/blob/main/src/zuna/inference/AY2l/lingua/apps/AY2latent_bci/configs/config_bci_eval.yaml",
              "https://doi.org/10.18112/openneuro.ds006593.v1.0.0"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "plausible (meets the stated inclusion rule)"
          },
          {
            "model": "zuna-1.1",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "OpenNeuro is a named ZUNA1 source (208 datasets kept if 3D positions resolved) and no per-dataset list exists for ZUNA1 or ZUNA1.1. This dataset was public on OpenNeuro before ZUNA1 (2026-02-09), and its scalp channels carry standard 10-20/10-10 labels (the published LaBraM/CBraMod rows already map them), so it would pass the position filter. It can be neither confirmed nor excluded.",
            "urls": [
              "https://arxiv.org/abs/2602.18478",
              "https://arxiv.org/abs/2607.27308",
              "https://github.com/Zyphra/zuna/blob/main/src/zuna/inference/AY2l/lingua/apps/AY2latent_bci/configs/config_bci_eval.yaml",
              "https://doi.org/10.18112/openneuro.ds005383.v1.0.0"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "plausible (meets the stated inclusion rule)"
          },
          {
            "model": "zuna-1.1",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "OpenNeuro is a named ZUNA1 source (208 datasets kept if 3D positions resolved) and no per-dataset list exists for ZUNA1 or ZUNA1.1. This dataset was public on OpenNeuro before ZUNA1 (2026-02-09), and its scalp channels carry standard 10-20/10-10 labels (the published LaBraM/CBraMod rows already map them), so it would pass the position filter. It can be neither confirmed nor excluded.",
            "urls": [
              "https://arxiv.org/abs/2602.18478",
              "https://arxiv.org/abs/2607.27308",
              "https://github.com/Zyphra/zuna/blob/main/src/zuna/inference/AY2l/lingua/apps/AY2latent_bci/configs/config_bci_eval.yaml",
              "https://doi.org/10.18112/openneuro.ds005185.v1.0.2"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "plausible (meets the stated inclusion rule)"
          },
          {
            "model": "zuna-1.1",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "BETA is distributed by Figshare and the Tsinghua BCI site. A name scan of all 1,909 public OpenNeuro datasets (GraphQL API, 2026-10-04) found no copy, and it is not part of TUH; those were ZUNA1's only named sources. But ZUNA1.1 names no sources and says its corpus grew partly by ingesting 'pre-epoched datasets the original pipeline could not use', and BETA is distributed only as pre-epoched 5-s trials. Absence cannot be confirmed.",
            "urls": [
              "https://arxiv.org/abs/2607.27308",
              "https://arxiv.org/abs/2602.18478",
              "https://openneuro.org/crn/graphql",
              "https://figshare.com/articles/dataset/The_BETA_database/12264401"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "unlikely (not on either repository ZUNA1 drew from)"
          },
          {
            "model": "zuna-1.1",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "unknown",
            "confidence": "low",
            "evidence": "OpenNeuro is a named ZUNA1 source (208 datasets kept if 3D positions resolved) and no per-dataset list exists for ZUNA1 or ZUNA1.1. This dataset was public on OpenNeuro before ZUNA1 (2026-02-09), and its scalp channels carry standard 10-20/10-10 labels (the published LaBraM/CBraMod rows already map them), so it would pass the position filter. It can be neither confirmed nor excluded.",
            "urls": [
              "https://arxiv.org/abs/2602.18478",
              "https://arxiv.org/abs/2607.27308",
              "https://github.com/Zyphra/zuna/blob/main/src/zuna/inference/AY2l/lingua/apps/AY2latent_bci/configs/config_bci_eval.yaml",
              "https://doi.org/10.18112/openneuro.ds005342.v1.0.3"
            ],
            "statement": "unknown: the authors do not list their pretraining data closely enough to decide",
            "lean": "plausible (meets the stated inclusion rule)"
          },
          {
            "model": "reve-base",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7 count) and not in MOABB, PhysioNet (Siena, I-CARE) or the 'Other' sources.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-base",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Used by the authors as a downstream set (MAT, App. C.3), and Sec 3.1.1 says recordings used in downstream tasks were removed from pretraining. Not in the App. B list.",
            "urls": [
              "https://arxiv.org/abs/2510.21585"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "explicitly excluded as downstream",
            "author_downstream_use": "REVE paper App. C.3 (MAT, mental stress detection)"
          },
          {
            "model": "reve-base",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7) and not a MOABB dataset. It was first public on 2025-08-23, after REVE's NeurIPS 2025 submission. The P300 sets REVE did use are BNCI2014-008/009, EPFLP300, BI2014a/b, BI2015a/b, Lee2019ERP and Sosulski2019.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-base",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7 count) and not in MOABB, PhysioNet (Siena, I-CARE) or the 'Other' sources.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-base",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7 count) and not in MOABB, PhysioNet (Siena, I-CARE) or the 'Other' sources.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-base",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in App. B or the open-subset card (no Liu 2020 / Tsinghua SSVEP entry). The one unnamed MOABB slot cannot be BETA: MOABB's BETA loader (Liu2020) was first committed on 2026-03-05, after REVE's release (arXiv 2025-10-24, NeurIPS 2025).",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset",
              "https://github.com/NeuroTechX/moabb/commits/develop/moabb/datasets/ssvep_liu2020.py"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-base",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (55 in App. B + ds004262 on the open-subset card = Table 7 count). The unnamed MOABB slot cannot be it: MOABB's TrianaGuzman2024 loader was first committed on 2026-03-16, after REVE's release.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset",
              "https://github.com/NeuroTechX/moabb/commits/develop/moabb/datasets/triana_guzman2024.py"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-large",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7 count) and not in MOABB, PhysioNet (Siena, I-CARE) or the 'Other' sources.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-large",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Used by the authors as a downstream set (MAT, App. C.3), and Sec 3.1.1 says recordings used in downstream tasks were removed from pretraining. Not in the App. B list.",
            "urls": [
              "https://arxiv.org/abs/2510.21585"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "explicitly excluded as downstream",
            "author_downstream_use": "REVE paper App. C.3 (MAT, mental stress detection)"
          },
          {
            "model": "reve-large",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7) and not a MOABB dataset. It was first public on 2025-08-23, after REVE's NeurIPS 2025 submission. The P300 sets REVE did use are BNCI2014-008/009, EPFLP300, BI2014a/b, BI2015a/b, Lee2019ERP and Sosulski2019.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-large",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7 count) and not in MOABB, PhysioNet (Siena, I-CARE) or the 'Other' sources.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-large",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (App. B + open-subset card reconcile with Table 7 count) and not in MOABB, PhysioNet (Siena, I-CARE) or the 'Other' sources.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-large",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in App. B or the open-subset card (no Liu 2020 / Tsinghua SSVEP entry). The one unnamed MOABB slot cannot be BETA: MOABB's BETA loader (Liu2020) was first committed on 2026-03-05, after REVE's release (arXiv 2025-10-24, NeurIPS 2025).",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset",
              "https://github.com/NeuroTechX/moabb/commits/develop/moabb/datasets/ssvep_liu2020.py"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "reve-large",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 56 OpenNeuro accessions (55 in App. B + ds004262 on the open-subset card = Table 7 count). The unnamed MOABB slot cannot be it: MOABB's TrianaGuzman2024 loader was first committed on 2026-03-16, after REVE's release.",
            "urls": [
              "https://arxiv.org/abs/2510.21585",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset",
              "https://github.com/NeuroTechX/moabb/commits/develop/moabb/datasets/triana_guzman2024.py"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "codebrain",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "codebrain",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent",
            "author_downstream_use": "Mental Arithmetic is a CodeBrain downstream task (fine-tuning only)"
          },
          {
            "model": "codebrain",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "codebrain",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "codebrain",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "codebrain",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "codebrain",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only; this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2506.09110"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eegmamba",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent"
          },
          {
            "model": "eegmamba",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent",
            "author_downstream_use": "Repo ships a MentalArithmetic fine-tuning loader (stress_dataset.py); paper's 6 downstream sets not verified"
          },
          {
            "model": "eegmamba",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent"
          },
          {
            "model": "eegmamba",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent"
          },
          {
            "model": "eegmamba",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent"
          },
          {
            "model": "eegmamba",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent"
          },
          {
            "model": "eegmamba",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "medium",
            "evidence": "Not among the five pretraining sources in the official pretrain_main.py (count matches the abstract). The two loosely named entries do not fit this dataset: 'Raw EEG Data' is 64-ch BioSemi BDF with a 60 Hz notch, and 'szbd' is 62-ch 4-class resting-state data. Residual risk: the paywalled paper's dataset table was not read.",
            "urls": [
              "https://github.com/wjq-learning/EEGMamba/blob/main/pretrain_main.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_raw_for_pretraining.py",
              "https://github.com/wjq-learning/EEGMamba/blob/main/preprocessing/preprocessing_for_pretraining/preprocessing_szbd_for_pretraining.py",
              "https://pubmed.ncbi.nlm.nih.gov/40714477/"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated in code; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "erp-fm",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the 38 ERP pretraining sets (App. A Table 11; README) or the 5 ablation-only non-ERP sets (Table 12).",
            "urls": [
              "https://arxiv.org/abs/2609.32796",
              "https://github.com/DL4mHealth/ERP-FM#datasets"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "singlem",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in Table I. The listed Nieto 2022 inner-speech set (ref [71]) comes from the same group (Peterson, Spies) but is a different dataset (OpenNeuro ds003626). Matching is by name and reference, since Table I gives few accession numbers.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "singlem",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in Table I. SAM40 (Ghosh 2022, ref [76]) includes an arithmetic task but is a different dataset. Matching is by name and reference, since Table I gives few accession numbers.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "singlem",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in Table I. The listed P300 set is Won 2022 (ref [72]), a different dataset. Matching is by name and reference, since Table I gives few accession numbers.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "singlem",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "exposed",
            "confidence": "high",
            "evidence": "Table I row 'Bai (2025) [106], Reading, 30 subjects, 12.92 h'; reference [106] is 'TMNRED, a Chinese language EEG dataset for fuzzy semantic target identification in natural reading environments', Sci. Data 12:701 (2025), the publication of OpenNeuro ds005383. TMNRED is not one of the three sources excluded from the primary checkpoint, so it is in all three released checkpoints.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3",
              "https://github.com/ttlabtuat/SingLEM"
            ],
            "statement": "in the authors' published pretraining list",
            "basis": "named in pretraining list"
          },
          {
            "model": "singlem",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in Table I. Listed sleep sets are Xiang 2024 (ds004902), Babayan 2019, Wei 2024 (ANPHY-Sleep) and Lopez-Larraz 2025 (Bitbrain, ds005555); none is EESM19. Matching is by name and reference, since Table I gives few accession numbers.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "singlem",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in Table I. Only eldBETA (Liu 2022, ref [91], 100 older adults) and Gu 2024 SSVEP (ref [82]) are listed; eldBETA is a different database from BETA. Matching is by name and reference, since Table I gives few accession numbers.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "singlem",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in Table I (no Triana-Guzman / standing-sitting MI entry). Matching is by name and reference, since Table I gives few accession numbers.",
            "urls": [
              "https://arxiv.org/abs/2509.17920v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "brainomni",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. H pretraining datasets (the only sleep EEG there is the paediatric epilepsy HFO set).",
            "urls": [
              "https://arxiv.org/abs/2505.18185v3"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. B sources (open subset of REVE's corpus, itself enumerated and assembled before MOABB added BETA/ds005342 loaders in 2026-03).",
            "urls": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not in App. B. 'Arithmetic (Zyma et al., 2019)' is one of the 12 OpenEEGBench downstream sets (App. C.1), and the Limitations paragraph says PhysioNet-MI is the only downstream set in the pretraining corpus and 'the other eleven are unseen'.",
            "urls": [
              "https://arxiv.org/abs/2609.33487"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "explicitly stated unseen",
            "author_downstream_use": "OpenEEGBench 'Arithmetic' linear-probe set (App. C.1)"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. B sources (open subset of REVE's corpus, itself enumerated and assembled before MOABB added BETA/ds005342 loaders in 2026-03).",
            "urls": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. B sources (open subset of REVE's corpus, itself enumerated and assembled before MOABB added BETA/ds005342 loaders in 2026-03).",
            "urls": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. B sources (open subset of REVE's corpus, itself enumerated and assembled before MOABB added BETA/ds005342 loaders in 2026-03).",
            "urls": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. B sources (open subset of REVE's corpus, itself enumerated and assembled before MOABB added BETA/ds005342 loaders in 2026-03).",
            "urls": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "eeg-fm-masking",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. B sources (open subset of REVE's corpus, itself enumerated and assembled before MOABB added BETA/ds005342 loaders in 2026-03).",
            "urls": [
              "https://arxiv.org/abs/2609.33487",
              "https://huggingface.co/datasets/brain-bzh/reve-dataset"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "labram",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Not among the App. D pretraining datasets; the only unlisted-by-name source is the authors' self-collected SJTU data.",
            "urls": [
              "https://arxiv.org/abs/2405.18765"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "cbramod",
            "dataset": "ds003810",
            "protocols": [
              "mi-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "cbramod",
            "dataset": "eegmat",
            "protocols": [
              "arithmetic-rest"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent",
            "author_downstream_use": "MentalArithmetic is a CBraMod downstream task (fine-tuning only)"
          },
          {
            "model": "cbramod",
            "dataset": "ds006593",
            "protocols": [
              "p300-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "cbramod",
            "dataset": "ds005383",
            "protocols": [
              "semantic-target"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "cbramod",
            "dataset": "eesm19",
            "protocols": [
              "sleep-scalp"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "cbramod",
            "dataset": "beta",
            "protocols": [
              "beta-8ch",
              "beta-4ch"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          },
          {
            "model": "cbramod",
            "dataset": "ds005342",
            "protocols": [
              "idle"
            ],
            "status": "not_exposed",
            "confidence": "high",
            "evidence": "Pretrained on TUEG only (Sec 3.1); this dataset is not part of TUEG.",
            "urls": [
              "https://arxiv.org/abs/2412.07236"
            ],
            "statement": "not in the authors' published pretraining list (checked 2026-10-04)",
            "basis": "corpus enumerated; absent"
          }
        ],
        "counts": {
          "exposed": 2,
          "not_exposed": 82,
          "unknown": 7
        },
        "caveats": [
          "SingLEM and LaBraM lists are matched by dataset name and reference; most rows carry no accession number.",
          "EEGMamba's list comes from the official code (five entries, matching the abstract); the paywalled paper was not read, so confidence is medium.",
          "REVE's Table 7 counts 27 MOABB datasets but names 25 (26 with the open-subset card); the unnamed slot cannot be BETA or ds005342 because MOABB added those loaders only in March 2026.",
          "ST-EEGFormer's in-house data is not described in detail; it is the authors' own lab recordings, so it cannot be any of the seven third-party datasets.",
          "Recording-level audits (which subjects or sessions) were not done; exposure is decided at the dataset level."
        ]
      },
      "directory_status": [
        {
          "name": "REVE Base",
          "suggested_status": "Evaluated",
          "replaces": "Access gated (released) / Licence review pending (2026-10-02 override)",
          "note": "User accepted the REVE Responsible Use License on 2026-10-04; frozen probes on 8 protocols and EEGMAT adaptation."
        },
        {
          "name": "REVE Large",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "New directory entry."
        },
        {
          "name": "LUNA (Base, Large)",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "CC BY-ND 4.0 licence note required."
        },
        {
          "name": "BrainOmni Base",
          "suggested_status": "Evaluated (6 of 8 protocols)",
          "replaces": null,
          "note": "1 s ERP protocols not run by contract."
        },
        {
          "name": "CodeBrain",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "13.53M encoder parameters."
        },
        {
          "name": "EEGMamba",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "Wang et al. 2025 (Neural Networks), CBraMod group."
        },
        {
          "name": "ST-EEGFormer (Base, Large)",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "BETA cells flagged as pretraining-exposed."
        },
        {
          "name": "eeg-fm-masking (4 checkpoints)",
          "suggested_status": "Evaluated (frozen probes, masking ablation)",
          "replaces": null,
          "note": "CC-BY-4.0 attribution."
        },
        {
          "name": "ERP-FM Base",
          "suggested_status": "Evaluated (frozen probes)",
          "replaces": null,
          "note": "CC BY-NC-SA 4.0; ERP protocols in design, others negative controls."
        },
        {
          "name": "SingLEM",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "semantic-target flagged as pretraining-exposed."
        },
        {
          "name": "ZUNA 1.1",
          "suggested_status": "Evaluated",
          "replaces": null,
          "note": "Exposure unknown on all core datasets; research-use model card."
        },
        {
          "name": "MIRepNet, EEG-DINO",
          "suggested_status": "Catalogue only (not evaluated)",
          "replaces": null,
          "note": "User decision 2026-10-04."
        },
        {
          "name": "EEGPT",
          "suggested_status": "unchanged (Rights review pending)",
          "replaces": null,
          "note": "Never loaded in v9."
        }
      ],
      "not_run": [
        {
          "model": "brainomni-base",
          "protocol": "p300-target",
          "reason": "BrainTokenizer needs a 512-sample window (2.0 s at 256 Hz); the published segment is 1 s = 256 samples, so upstream unfold() would zero-pad 256 of 512 samples of every window (invented samples). All upstream downstream tasks use >= 2 s windows."
        },
        {
          "model": "brainomni-base",
          "protocol": "semantic-target",
          "reason": "BrainTokenizer needs a 512-sample window (2.0 s at 256 Hz); the published segment is 1 s = 256 samples, so upstream unfold() would zero-pad 256 of 512 samples of every window (invented samples). All upstream downstream tasks use >= 2 s windows."
        },
        {
          "model": "luna-large",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: LUNA Large is frozen probes only in the v9 stage specification"
        },
        {
          "model": "steegformer-large",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: ST-EEGFormer Large is frozen probes only"
        },
        {
          "model": "eeg-fm-masking/mae-r9cm-L2",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: eeg-fm-masking is a frozen-probe masking ablation"
        },
        {
          "model": "eeg-fm-masking/jepa-r9cm-L2",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: eeg-fm-masking is a frozen-probe masking ablation"
        },
        {
          "model": "eeg-fm-masking/mae-rone-L1",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: eeg-fm-masking is a frozen-probe masking ablation"
        },
        {
          "model": "eeg-fm-masking/jepa-rone-L1",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: eeg-fm-masking is a frozen-probe masking ablation"
        },
        {
          "model": "erp-fm-base",
          "protocol": "EEGMAT adaptation",
          "reason": "not run by design: ERP-FM is frozen probes only and EEGMAT arithmetic is outside the ERP design"
        },
        {
          "model": "MIRepNet",
          "protocol": "all",
          "reason": "catalogue only by user decision (2026-10-04); not evaluated"
        },
        {
          "model": "EEG-DINO",
          "protocol": "all",
          "reason": "catalogue only by user decision (2026-10-04); upstream weights carry no licence"
        },
        {
          "model": "EEGPT",
          "protocol": "all",
          "reason": "never loaded in v9 (checkpoint licence unresolved on the site)"
        },
        {
          "model": "LUNA Huge, BrainOmni tiny, ST-EEGFormer small/largeV2, ZUNA 1.0, other ERP-FM checkpoints, eeg-fm-masking other 54 geometries",
          "protocol": "all",
          "reason": "not fetched: outside the approved v9 list"
        }
      ],
      "required_limitations": [
        "Fixed recipes: the frozen probe is the published linear head with only the encoder swapped; adaptation is one untuned five-epoch recipe on one task. Scores are not any model's ceiling.",
        "Small cohorts (motor imagery 10 people, P300 21, sleep 20, idle 4); intervals are descriptive participant bootstraps that ignore cross-validation dependence and carry no multiplicity correction.",
        "Rows are ordered by model family, never ranked; a row is called above or below another only when the marginal intervals do not overlap.",
        "Balanced or class-matched designs (P300, semantic target, motor imagery vs rest, sampled sleep epochs) are method comparisons, not detection, false-alarm or latency estimates for real use.",
        "New people, same task and recording setup only: not cross-day, cross-device or cross-dataset evidence; no deployment or clinical claim.",
        "All encoders run on the published 1-2 s windows (sleep as 15 x 2 s), shorter than most pretraining contexts; model-specific input handling and declared deviations are listed per row.",
        "Pretraining exposure, as the authors' published pretraining lists show it (checked 2026-10-04): BETA is in ST-EEGFormer's list and TMNRED (semantic-target) in SingLEM's; ZUNA 1.1 publishes no list, so it is unknown on all core datasets; EEGMamba's list is read from its official code, with medium confidence. A dataset absent from a list is not proof that its recordings were never seen.",
        "Timing and memory were measured on a shared GPU and are indicative only.",
        "No author endorsement is implied. No adapted weights or LoRA deltas were kept or shared."
      ],
      "audits": {
        "all_groups_passed": true,
        "groups": [
          {
            "group": "reve",
            "models": [
              "reve-base",
              "reve-large"
            ],
            "checks_passed": 17,
            "passed": true,
            "defects": 0,
            "sha256": "6368897ffa1528981ddbee728e58ccf6516df39fbacd8bbc1fdb79171ac1d9e0"
          },
          {
            "group": "erp-fm",
            "models": [
              "erp-fm-base"
            ],
            "checks_passed": 12,
            "passed": true,
            "defects": 0,
            "sha256": "0ae0a3cae5b4dea51573eb54557a2e6267fbabc43482269e2f9e01a966e83b01"
          },
          {
            "group": "masking-steeg",
            "models": [
              "eeg-fm-masking/mae-r9cm-L2",
              "eeg-fm-masking/jepa-r9cm-L2",
              "eeg-fm-masking/mae-rone-L1",
              "eeg-fm-masking/jepa-rone-L1",
              "steegformer-base",
              "steegformer-large"
            ],
            "checks_passed": 15,
            "passed": true,
            "defects": 0,
            "sha256": "863fe6c10289690066fb9abef7876e3db2f17647a6727554283a6003b765e600"
          },
          {
            "group": "cbramod-layout",
            "models": [
              "codebrain",
              "eegmamba"
            ],
            "checks_passed": 10,
            "passed": true,
            "defects": 0,
            "sha256": "e107711c628ed050ef36f7bcf163cb8fe1c782a9c7f911b451ef09dd23b766fd"
          },
          {
            "group": "positions",
            "models": [
              "luna-base",
              "luna-large",
              "brainomni-base"
            ],
            "checks_passed": 11,
            "passed": true,
            "defects": 0,
            "sha256": "3ddbd6e0d804e85cd19e193e4fd8b9944aaa8f9c5e103c3ff76429c101d1f565"
          },
          {
            "group": "single-channel",
            "models": [
              "singlem",
              "zuna"
            ],
            "checks_passed": 8,
            "passed": true,
            "defects": 0,
            "sha256": "6d540185453a1db3a785007b835116023dc79e45d88d6e3f2a2dda1b60dfb57b"
          }
        ],
        "rule": "Only groups whose independent audit passed are released; a group that failed would appear as not passed, with no number.",
        "note": "Each auditor wrote its own code and imported nothing from the harness or the group's adapters. The audits stay in the private run root and are bound by hash."
      }
    }
  },
  "status_only": [],
  "holds": [],
  "not_published": [
    "Per-trial predictions, per-person and per-fold metrics, features and their hashes, the channel lists and contracts of each run.",
    "Timing, memory, hardware and software environments: measured on a shared GPU and indicative only, so the per-protocol CSVs leave scoring_seconds empty rather than put them beside the core rows' timings.",
    "The declared sensitivity runs (REVE without per-segment mean removal, ERP-FM with an average reference and a 45 Hz low-pass, ST-EEGFormer sleep as 5 x 6 s segments): never substituted for a primary row, and published only as the row footnotes state them.",
    "The release candidate's generated headline sentences and the handoff's prose: the pages write their own copy from the values.",
    "The pretraining-exposure table's process history (its open items from the previous expansion round) and its overlaps outside the seven core datasets: no page uses them.",
    "Revisions and checkpoint hashes of the three sibling masking checkpoints: the release candidate names only the paper-recommended checkpoint's revision.",
    "Model weights, adapted weights and LoRA deltas: never kept or shared.",
    "The aggregate, the group summaries, the audit status and the harness validation: pinned by SHA-256 and checked, never copied, because they carry private storage paths. The six independent audits, with their check names and notes, and the run's decision log stay in the private run root; the audits are bound by hash."
  ],
  "provenance": {
    "manifest_sha256": "234fc9bf732de4e5cd7b1c94456233d11f7621a5dbc42a615f84fe9b382bcf57",
    "release_candidate_sha256": "770021b49a6619bc73fd862163e0b2309cd5292a91aed233fc20164fdf08fbb8",
    "aggregate_sha256": "cc3fd8570ec96cfb725bde69c6616ff3565dd7d16739dc7dcf0a7b37b66c32be",
    "pretraining_exposure_sha256": "c7b1370ffb6d996797dacbb202016de5071e6e3ecc6a53bb50ac297908dd1d95",
    "audit_status_sha256": "0b0560565058f8ca05da53e8fb6c4b567d20284cefc6859675a66534a8410165",
    "references_reresolved": 1499,
    "handoff_rows_checked": 45,
    "included": [
      "foundation-models-v9"
    ],
    "inputs": [
      "mi-rest",
      "idle",
      "beta-8ch",
      "beta-4ch",
      "arithmetic-rest",
      "p300-target",
      "semantic-target",
      "sleep-scalp"
    ],
    "holds": []
  }
}