# EEG results you can check.

Open EEG evaluation

8 fixed protocols on 7 public datasets carry the core matrix’s 9 decoding methods and, beside them, 14 foundation encoders as frozen probes. Questions beyond the matrix cover transfer, calibration and model adaptation, reliability, clinical groups and data quality, signals other than scalp EEG, and small pilot studies. The open register lists every EEG dataset and model we know of, with how far each has been checked.

Core matrix

- Protocols: 8
- Datasets: 7
- Comparisons: 39
- Methods: 9

[Also on these protocols: 14 foundation encoders, frozen →](https://bci.report/protocols/)

Across the site: [17 questions](https://bci.report/topics/) · [32 datasets with results](https://bci.report/datasets/) · [20 methods with result pages](https://bci.report/methods/)

Core benchmark matrix

## 9 methods × 8 protocols

The original snapshot: balanced accuracy in percent, and command detection on idle.

Each method’s score on each protocol; each column heading gives the cohort size and chance level.

| Method | [Motor imagery & rest](https://bci.report/protocols/mi-rest/) — n=10 · chance 50% | [Idle & command](https://bci.report/protocols/idle/) — n=4 · detection | [SSVEP · 8 channels](https://bci.report/protocols/beta-8ch/) — n=70 · chance 2.5% | [SSVEP · 4 channels](https://bci.report/protocols/beta-4ch/) — n=70 · chance 2.5% | [Arithmetic & rest](https://bci.report/protocols/arithmetic-rest/) — n=36 · chance 50% | [P300 target ERP](https://bci.report/protocols/p300-target/) — n=21 · chance 50% | [Semantic target ERP](https://bci.report/protocols/semantic-target/) — n=30 · chance 50% | [Sleep staging](https://bci.report/protocols/sleep-scalp/) — n=20 · chance 20% |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [CBraMod](https://bci.report/methods/cbramod/) — Foundation model · 8 of 8 | 62.9 | 18.3 | 33.7 | 27.6 | 62.3 | 55.0 | 55.8 | 71.3 |
| [EEGNet](https://bci.report/methods/eegnet/) — Compact model · 8 of 8 | 71.0 | 1.7 | 55.8 | 44.1 | 67.6 | 53.3 | 61.4 | 56.5 |
| [LaBraM](https://bci.report/methods/labram/) — Foundation model · 8 of 8 | 53.5 | 18.3 | 10.8 | 12.9 | 64.6 | 49.4 ≤ chance | 53.2 | 71.1 |
| Spectral ridge — Classical method · 7 of 8 | 54.9 |  | 50.7 | 48.2 | 56.8 | 51.0 | 51.0 | 72.2 |
| [CSP+LDA](https://bci.report/methods/csp-lda/) — Classical method · 2 of 8 | 47.1 ≤ chance | 23.3 |  |  |  |  |  |  |
| [Standard CCA](https://bci.report/methods/cca/) — Classical method · 2 of 8 |  |  | 63.1 | 57.6 |  |  |  |  |
| Temporal ridge — Classical method · 2 of 8 |  |  |  |  |  | 53.8 | 56.3 |  |
| [Deep4Net](https://bci.report/methods/deep4net/) — Compact model · 1 of 8 |  | 3.3 |  |  |  |  |  |  |
| [ShallowFBCSPNet](https://bci.report/methods/shallowfbcspnet/) — Compact model · 1 of 8 |  | 43.3 |  |  |  |  |  |  |

Highest score in this snapshot Bar = position between that protocol's chance level and 100% Not evaluated [Core matrix · JSON ↓](https://bci.report/data/experiments.json)

Bars are comparable **down** a column and not **across** one: chance level is 50% for the two-class tasks, 20% for sleep staging and 2.5% for 40-class SSVEP, and every protocol uses a different cohort and electrode layout. There is no overall score. Read the idle column with the false-activation rate in its protocol.

13 further foundation encoders, run as frozen probes on the same eight protocols (BrainOmni Base on six of them). They are checkpoints of 11 models; 3 more checkpoints of one of them form a masking ablation, 16 in all. They sit outside this snapshot. [The foundation-encoder rows on the first protocol →](https://bci.report/protocols/mi-rest/#foundation-v9) Beside them sits EEGPT, from the same runs as the published LaBraM rows and, except on idle, the CBraMod rows, with only the encoder swapped. [EEGPT’s row on the first protocol →](https://bci.report/protocols/mi-rest/#eegpt)

[Jev-style questions on EEG: the evidence](https://bci.report/jev-style/): Decision research · “Jev-style” here means the interface of a decision model like TypeSafe’s Jev, applied to EEG: encode the recording once, then answer several explicit, typed questions about it, each with a probability. Three routes, each answered by this site’s own runs on public EEG data. · Read the evidence →

One protocol at a time

## The evidence behind each score

## Rest versus right-hand imagery

ds003810 · 10 subjects · 1,200 epochs · 5 participant-disjoint folds · 15 channels · 2-second windows

Measured here

### Results at a glance

[CSV ↓](https://bci.report/data/mi-rest-results.csv)

| Model / configuration | Balanced accuracy | Macro F1 | Compute time |
| --- | --- | --- | --- |
| Spectral ridge — Supervised fit · 15 ch | **54.9%** — Descriptive 95% interval: 53.2–56.7% | **0.544** — Mean across held-out participants | 0.2s |
| CSP+LDA — Supervised fit · 15 ch | **47.1%** — Descriptive 95% interval: 43.8–50.2% At or below the 50% chance level | **0.406** — Mean across held-out participants | 8.8s |
| LaBraM — Frozen encoder + ridge head · 15 ch | **53.5%** — Descriptive 95% interval: 51.4–55.4% | **0.509** — Mean across held-out participants | 2.2s |
| CBraMod — Frozen encoder + ridge head · 15 ch | **62.9%** — Descriptive 95% interval: 59.6–66.5% | **0.617** — Mean across held-out participants | 5.0s |
| EEGNet — Scratch · 20 epochs · 15 ch | **71.0%** — Descriptive 95% interval: 65.0–76.3% Single seed — the highest of 3 runs (68.17–71.00%, mean 69.47%) | **0.680** — Mean across held-out participants | 55.5s |
| 13 further foundation encoders, frozen [Every row with its footnote →](https://bci.report/protocols/mi-rest/#foundation-v9) |  |  |  |
| BrainOmni Base — Frozen encoder + ridge head · 15 ch | **58.8%** — Descriptive 95% interval: 57.0–60.8% | **0.582** — Mean across held-out participants | — not published |
| CodeBrain — Frozen encoder + ridge head · 15 ch | **58.8%** — Descriptive 95% interval: 54.5–62.6% | **0.573** — Mean across held-out participants | — not published |
| eeg-fm-masking MAE (r=9 cm, L=2) — Frozen encoder + ridge head · 15 ch | **68.1%** — Descriptive 95% interval: 64.3–72.3% | **0.677** — Mean across held-out participants | — not published |
| EEGMamba — Frozen encoder + ridge head · 15 ch | **67.3%** — Descriptive 95% interval: 63.6–70.6% | **0.669** — Mean across held-out participants | — not published |
| ERP-FM Base — Frozen encoder + ridge head · 15 ch | **54.8%** — Descriptive 95% interval: 51.8–57.8% | **0.524** — Mean across held-out participants | — not published |
| LUNA Base — Frozen encoder + ridge head · 15 ch | **52.6%** — Descriptive 95% interval: 50.2–54.9% | **0.503** — Mean across held-out participants | — not published |
| LUNA Large — Frozen encoder + ridge head · 15 ch | **49.9%** — Descriptive 95% interval: 46.6–53.5% At or below the 50% chance level | **0.486** — Mean across held-out participants | — not published |
| REVE Base — Frozen encoder + ridge head · 15 ch | **66.2%** — Descriptive 95% interval: 61.8–70.6% | **0.640** — Mean across held-out participants | — not published |
| REVE Large — Frozen encoder + ridge head · 15 ch | **70.2%** — Descriptive 95% interval: 64.0–76.1% | **0.673** — Mean across held-out participants | — not published |
| SingLEM — Frozen encoder + ridge head · 15 ch | **72.0%** — Descriptive 95% interval: 66.6–76.6% | **0.715** — Mean across held-out participants | — not published |
| ST-EEGFormer Base — Frozen encoder + ridge head · 15 ch | **58.1%** — Descriptive 95% interval: 53.7–63.3% | **0.541** — Mean across held-out participants | — not published |
| ST-EEGFormer Large — Frozen encoder + ridge head · 15 ch | **62.4%** — Descriptive 95% interval: 57.4–67.8% | **0.586** — Mean across held-out participants | — not published |
| ZUNA 1.1 — Frozen encoder + ridge head · 15 ch — Model card: research use only, not for diagnosis or clinical use. | **50.7%** — Descriptive 95% interval: 49.2–52.1% Interval reaches the 50% chance level Exposure unknown | **0.499** — Mean across held-out participants | — not published |
| EEGPT, frozen · the same runs as the published LaBraM and CBraMod rows [Its row with its footnotes →](https://bci.report/protocols/mi-rest/#eegpt) |  |  |  |
| EEGPT — Frozen encoder + ridge head · 15 ch | **58.3%** — Descriptive 95% interval: 54.9–61.8% | **0.555** — Mean across held-out participants | — not published |

Scroll the table sideways for the remaining columns.

[Foundation-encoder rows · CSV ↓](https://bci.report/data/foundation-models-mi-rest.csv) · [EEGPT’s cells · JSON ↓](https://bci.report/data/eegpt-core-update.json)

Weights terms: REVE under the REVE Responsible Use License v1.0 (model versions: REVE Base @ dc2a075c · REVE Large @ 317531c7); LUNA under CC BY-ND 4.0, with no modified weights shared; BrainOmni, EEGMamba, ST-EEGFormer and SingLEM under MIT; CodeBrain and ZUNA under Apache-2.0; eeg-fm-masking under CC-BY-4.0; ERP-FM under CC BY-NC-SA 4.0, non-commercial. No model’s authors endorse these results. [Every weights licence and its terms →](https://bci.report/protocols/mi-rest/#v9-licences)

EEGPT: the checkpoint’s licence is unresolved, and no original or converted weights are distributed; Apache-2.0 covers the repository’s code only. [Its licence and terms →](https://bci.report/protocols/mi-rest/#eegpt-terms)

Masking ablation: three sibling checkpoints of eeg-fm-masking

Read against the paper-recommended checkpoint, the matrix row above: the same architecture, corpus and recipe, with another masking framework or geometry.

| eeg-fm-masking MAE (r=9 cm, L=2) — Frozen encoder + ridge head · 15 ch — matrix row above | **68.1%** — Descriptive 95% interval: 64.3–72.3% | **0.677** — Mean across held-out participants | — not published |
| --- | --- | --- | --- |
| eeg-fm-masking JEPA (r=9 cm, L=2) — Frozen encoder + ridge head · 15 ch | **63.2%** — Descriptive 95% interval: 58.2–67.9% | **0.620** — Mean across held-out participants | — not published |
| eeg-fm-masking JEPA (r=one channel, L=1) — Frozen encoder + ridge head · 15 ch | **63.3%** — Descriptive 95% interval: 59.0–67.4% | **0.628** — Mean across held-out participants | — not published |
| eeg-fm-masking MAE (r=one channel, L=1) — Frozen encoder + ridge head · 15 ch | **64.9%** — Descriptive 95% interval: 60.3–69.4% | **0.645** — Mean across held-out participants | — not published |

Compute time covers fitting and prediction, possibly with accelerator waiting, and excludes data preparation.

Visual comparison

### Accuracy by configuration

CC0-1.0 · Aggregate research results [Original dataset ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) [Use & privacy →](https://bci.report/data-use/) [Report an issue with this result →](https://bci.report/data-use/#corrections)

**Limits**

Ten-person laboratory task with prompted rest. This is not continuous-idle monitoring or a physical low-channel headset test.

Questions

## What the evidence can answer

Figures under one question often share participants: they are repeated conditions within its protocols.

### Transfer

Does a decoder still work after a change of sensor, display, electrode layout, body movement or session?

- [Dry vs. wet electrodes](https://bci.report/topics/dry-vs-wet/): Sensor transfer · What changes when a decoder crosses between two native eight-channel recordings from the same 102 people? · 2 s SSVEP · 12 targets · balanced accuracy
- [Screen to VR](https://bci.report/topics/screen-to-vr/): Context transfer · The same 21 people calibrated on a PC screen and tested in a VR headset, and the reverse: the level reached after the switch, with no same-display score to compare. Plus: a P300 decoder trained at one image rate and tested at another. · P300 · 21 people in VR · 9 people across image rates
- [Fewer electrodes](https://bci.report/topics/fewer-electrodes/): Montage · In-ear against scalp for sleep, four posterior electrodes against sixteen for eyes open or closed, and foundation encoders on BETA with eight and four electrodes: channel sets from specific research recordings. · Two paired comparisons · 10 and 19 people · BETA, 8 and 4 electrodes, 70 people
- [On the move](https://bci.report/topics/on-the-move/): Motion robustness · Standing, walking and running results, with scalp and ear recordings and incompatible time windows kept apart. · SSVEP balanced accuracy · ERP ROC AUC
- [Later sessions](https://bci.report/topics/later-sessions/): Session transfer · same person · Trained on a person’s earlier session and tested on a later one, with no labels from it: motor imagery across recording sessions, with simple baselines and frozen CBraMod; target detection at the publisher’s nominal later visits; and continuous cursor tracking, where a constant baseline does better. · Motor imagery, WBCIC-SHU, 51 and 11 people · RSVP, 15 people · cursor tracking, a negative result

### Adapting models

How much calibration a decoder needs, which part of a pretrained model to update, whether pretraining helps, whether one model can answer several questions, including questions asked in words, and what lets a decoder answer an SSVEP frequency it never trained on.

- [How much calibration?](https://bci.report/topics/calibration-budget/): Calibration budget · Twelve, 24 or 48 labeled trials on a wearable SSVEP task, and up to 40 from a person’s next motor-imagery session: the gain differs by method, and some people’s scores fall. · SSVEP · 102 people · motor imagery, next session · 51 people
- [Which part to update?](https://bci.report/topics/model-adaptation/): Model adaptation · LaBraM on new people, same task, zero labels from the test person: head only, last block or LoRA, printed beside the core matrix’s frozen readout. Plus: the next-day experiment, run and held. · EEGMAT, 36 people · three update rules · next day: held
- [Does pretraining help?](https://bci.report/topics/does-pretraining-help/): Representation controls · Matched pretrained and constructor-random encoders under fixed and train-selected readout settings. Plus further foundation models in the same frozen recipe: three checkpoints and, narrowly, EEGPT above every published row on sleep staging, none above training-free CCA on BETA. · Two tasks · three encoders · three random initializations · 16 further checkpoints
- [One model, several questions](https://bci.report/topics/shared-encoder/): Shared encoder · route 2 · One shared encoder with a fixed output per question, against a head told which question it is answering and against a separate model per question, at matched data and compute — with what each costs. · Motor imagery, OpenBMI, 51 people · sleep, BOAS · EESM19, crude
- [Questions in language](https://bci.report/topics/questions-in-language/): Questions in language · route 3 · One small head over frozen EEG features, asked each question by a number, a label template or a description: it works only for questions it was trained on, and not on every dataset. · SSVEP, BETA, 70 people · sleep, BOAS · secondary: EESM19, OpenBMI, Wearable-102
- [New SSVEP frequencies](https://bci.report/topics/new-ssvep-frequencies/): Route 3 follow-ups · Fourteen follow-ups: thirteen on route 3’s BETA cohort, five on Wearable-102, one of them only there. Heads that read reference spectra or stimulus phase stayed far below CCA, which needs no training; a small learned correction on top of CCA added a few points on BETA and changed little on Wearable-102; filter-bank CCA, untrained too, scored above CCA in every setting tested, and an untrained harmonic sum came out nearly level with a learned eigenvalue head. · SSVEP, BETA, 70 people · 8 held-out frequencies · Wearable-102, 102 people

### Reliability & clinical

When a decoder should not act, what accuracy hides in sleep staging, what resting-state EEG says about a clinical group, and what a public dataset’s files show when checked end to end.

- [When not to act](https://bci.report/topics/when-not-to-act/): Abstention · decision research · Command detection read with its false-activation rate; when a decoder should decline to decide, on two protocols; and the decision-research plan, with its three routes. · Idle, 4-person pilot · non-control, 20 people · reliable decisions, 36 and 70 people
- [Sleep-stage balance](https://bci.report/topics/sleep-staging/): Sleep staging · simple baselines · Two simple baselines in 25 healthy sleepers and 55 people with sleep apnoea, as two separate experiments: accuracy beside balanced accuracy, and the stage the stager never predicts. · Dreem DOD-H and DOD-O · 25 and 55 nights · balanced accuracy
- [Clinical groups](https://bci.report/topics/clinical-groups/): Clinical research · A 149-person Parkinson's and control comparison beside an age-and-sex-only comparator; neither is a diagnosis. · 149 people · one site · balanced accuracy
- [Checked end to end](https://bci.report/topics/data-quality/): Data quality and reproducibility · Public EEG datasets read from their own files: Shin2022’s runs, duplicates and valid windows counted before a participant-held-out check, where the EEG arm sits close to a control that reads only trial duration; training-free CCA on Kim beta-range SSVEP; MIND’s stored markers, read on record IDs; three one-person cases, eldBETA, Damm and NeBULA, with counts and no score; and a timing hold on one converted ZuCo run. · Shin2022, 10 people · Kim beta-range · MIND · one-person cases, counts only · ZuCo, status only

### Other signals & pilots

What simple baselines show on signals other than scalp EEG, in people and in one macaque, and what small pilot studies show.

- [Beyond scalp EEG](https://bci.report/topics/beyond-scalp-eeg/): Other signals · human and macaque · fNIRS telling right-hand, left-hand and foot tapping apart in people the classifier never saw; finger velocity from one macaque’s motor-cortex spiking, where a ridge decoder lowers the squared error but not the absolute error; and a numerical coverage check of fNIRS recordings. Intracortical handwriting and functional ultrasound: status only. · fNIRS, 30 people · intracortical spiking, 1 macaque · fNIRS coverage, 12 people
- [Pilot studies](https://bci.report/topics/pilot-studies/): Pilot studies · small cohorts · Small pilots: injected orthosis errors in one cohort, including a frozen EEG foundation model against matched classical features; inner speech in Greek and English, near its chance level; flat channels in a handwriting dataset, where masked EEG sits just above chance; and a stored-clip check on an emotion dataset, where simple controls have smaller errors than EEG. · Orthosis, 8 people · inner speech, BINGO, 17 subject fields · handwriting, 5 people · REFED, 2 nominal folders, 30 clips

### Which kinds of transfer have been measured

What changes between training and test, and whether this site has evidence on it.

| What changes | Measured | Status only | Held | Not measured |
| --- | --- | --- | --- | --- |
| Person | [Core matrix · new-person protocols](https://bci.report/#overview); [Dry vs. wet electrodes](https://bci.report/topics/dry-vs-wet/) n=102 also changes: sensor; [LaBraM adaptation · EEGMAT](https://bci.report/topics/model-adaptation/#adaptation) n=36; [Standing to walking and running, SSVEP](https://bci.report/topics/on-the-move/) n=23 also changes: movement; [Pretraining controls](https://bci.report/topics/does-pretraining-help/) n=36 / 10; [Nine encoders adapted · EEGMAT](https://bci.report/topics/does-pretraining-help/#v9-adaptation) n=36; [Parkinson’s disease vs. controls](https://bci.report/topics/clinical-groups/) n=149; [Sleep staging · Dreem, two cohorts](https://bci.report/topics/sleep-staging/) n=55 / 25; [fNIRS, executed tapping · Bak et al.](https://bci.report/topics/beyond-scalp-eeg/#bak-fnirs) n=30; [Orthosis device errors · EEG pilot](https://bci.report/topics/pilot-studies/#orthosis) n=8; [Inner speech, Greek and English · BINGO](https://bci.report/topics/pilot-studies/#bingo); [Imagined handwriting, four coded labels · EEG pilot](https://bci.report/topics/pilot-studies/#handwriting) n=5 |  | [Neural and foundation models on the Dreem cohorts](https://bci.report/releases/#hold-dreem-amplitude-sensitive-models); [Foundation models on the clinical cohort](https://bci.report/releases/#hold-clinical-foundation-models) |  |
| Session / day | [Mobile ERP · first session to the others](https://bci.report/topics/on-the-move/#erp-heading) n=24 / 17 also changes: movement; [OpenBMI motor imagery · first session to the second](https://bci.report/topics/calibration-budget/#next-session) n=51; [WBCIC-SHU motor imagery · an earlier session to a later one](https://bci.report/topics/later-sessions/#wbcic-shu) n=51 / 11; [Longitudinal RSVP · first visit to later visits](https://bci.report/topics/later-sessions/#longitudinal-rsvp) n=15; [Continuous cursor tracking · earliest to latest session](https://bci.report/topics/later-sessions/#forenzo) | [Cross-session pilot](https://bci.report/topics/model-adaptation/#cross-session) n=1 | [Next-day adaptation](https://bci.report/releases/#hold-bnci2015-001-crossday) |  |
| Sensor / montage | [Dry ⇄ wet electrodes](https://bci.report/topics/dry-vs-wet/) n=102 also changes: person; [In-ear vs. scalp, sleep](https://bci.report/topics/fewer-electrodes/#montage-finding) n=10 comparison; [Posterior subset vs. all electrodes](https://bci.report/topics/fewer-electrodes/#posterior-subset) n=19 comparison; [BETA, eight vs. four electrodes](https://bci.report/topics/fewer-electrodes/#v9-montage) n=70 comparison |  |  | From one headset or amplifier to another |
| Context | [Screen ⇄ VR, P300](https://bci.report/topics/screen-to-vr/) n=21; [Standing to walking and running, SSVEP](https://bci.report/topics/on-the-move/) n=23 also changes: person; [Standing to walking and running, ERP](https://bci.report/topics/on-the-move/#erp-heading) n=24 / 17 also changes: session; [Image rate, P300](https://bci.report/topics/screen-to-vr/#image-rate) n=9 also changes: recording; [Greek ⇄ English inner speech](https://bci.report/topics/pilot-studies/#bingo) also changes: person |  |  |  |
| Dataset |  |  |  | Training on one dataset, testing on another |

n is the number of people behind an entry; two sizes (n=24 / 17) are two analyses or cohorts in one entry, largest first. “comparison”: two electrode sets scored separately on the same data. “also changes”: what else changed in the same contrast.

[The full map, with what each entry measures →](https://bci.report/topics/#transfer)

Holds

## What is held back, and why

- [Kim beta-range SSVEP · amplitude-dependent models](https://bci.report/topics/data-quality/#kim2025-beta-range): Held · Amplitude units, channel gains and the saved reference history are not established, so models whose output depends on amplitude are held on these files. Training-free CCA, which does not depend on a common amplitude scale, is published.
- [Damm finger motor imagery · one-person score](https://bci.report/topics/data-quality/#damm): Status only · One participant’s two random-order runs, checked end to end. The scores are held under the one-person rule; design, counts and file facts are published.
- [NeBULA · one-person scores](https://bci.report/topics/data-quality/#nebula): Status only · One participant reaching to three targets under three levels of assistance. The EEG, EMG and combined scores are held under the one-person rule; design, counts and file facts are published.
- [eldBETA · one-person score](https://bci.report/topics/data-quality/#eldbeta): Status only · The raw-recording route runs end to end on one participant’s recordings. With one person every score is that person’s, so the accuracy is held; the counts are published.
- [ZuCo on NEMAR · converted-run timing](https://bci.report/topics/data-quality/#zuco2): No score · In one converted run on NEMAR, some fixation times fall outside the recording. No EEG–text score is computed until the converter’s timing rules and a source-checksummed fixture are available.
- [ds007420 · fNIRS classifier](https://bci.report/topics/beyond-scalp-eeg/#ds007420): No score · Its numeric task codes have no verified lookup to hands or movements, and its event durations disagree between the events table, the optical files and the cited paper. Its optical coverage counts are published.
- [Dreem sleep cohorts · neural-network and foundation models](https://bci.report/topics/sleep-staging/#held): Held · The stored signal metadata says millivolts; the publisher’s own converter treats the same arrays as microvolts. Models that need absolute amplitude are not run; the published baselines do not depend on the unit.
- [Stieger longitudinal BCI · one-person pilot](https://bci.report/topics/model-adaptation/#cross-session): Status only · The pipeline works on one person’s eleven sessions. With one person every score is that person’s, so none is published.
- [OpenNeuro ds004902 · paired sleep comparison](https://bci.report/releases/#hold-ds004902): No score · Five source payloads are shorter than their own headers declare.
- [Foundation models on the clinical set](https://bci.report/topics/clinical-groups/): Held · Not run: the source states no physical amplitude unit.
- [L-FAME](https://bci.report/releases/#hold-lfame): Described, not scored · Its per-condition numbers are withheld.
- [BNCI2015-001 · next-day adaptation](https://bci.report/topics/model-adaptation/#next-day): Held · Run and independently replayed. The catalogue licence is CC BY-NC-ND and the description names no ethics approval, so no figure is published until that review is done.

[Full holds register →](https://bci.report/releases/#holds) [Corrections (2) →](https://bci.report/releases/#corrections)

Directory

## Everything else, by what it is

- [20 methods with result pages](https://bci.report/methods/): Methods · Every published figure for each method; the model directory, with why a catalogued model has no score; external field notes.
- [32 datasets with results](https://bci.report/datasets/): Datasets · Every figure measured on each dataset; the public-data register, with source terms, credit and what no release includes.
- [26 reviewed releases](https://bci.report/releases/): Releases · What the newest releases changed, a line for every earlier one, corrections, the holds register, and an archive with every file’s SHA-256.
- [53 downloadable files](https://bci.report/api/): Data & API · Every JSON and CSV file with what it holds, code examples, the release feed and how to cite.

Evaluation

## How these results were produced

Only explicitly reviewed cohort-level research results are exported. Raw EEG, individual results and model weights stay outside the website. Each comparison states its source, license, protocol and limitations. No cross-task overall score.

[Download all results · JSON ↓](https://bci.report/data/experiments.json)

01

### Status labels

Downloads, loading checks and scored evaluations have distinct status labels. Models awaiting an adapter have no result.

02

### Failure modes

Missed commands, false activations and abstention are reported.

03

### Same task, same protocol

Splits, channels, preprocessing and training modes travel with each protocol. Frozen encoders and scratch baselines are labeled separately.

04

### Fixed, audited local runs

Experiments run offline on a Mac or local GPU workstation. Timings are configuration-specific. This website performs no EEG inference or diagnosis.

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