# Motor imagery & rest on ds003810

Core-matrix protocol · Transfer to a new person

Everything that produced the scores below — cohort, split, electrodes, window, what each method was allowed to learn, and what guessing would score — as the released protocol file states it. Compare scores within this protocol only.

## The protocol at a glance

- Task: Rest versus right-hand imagery
- Evaluation: Transfer to a new person
- Dataset: [ds003810](https://bci.report/datasets/ds003810/)
- People: 10
- Data: 1,200 epochs · 5 participant-disjoint folds
- Input: 15 channels · 2-second windows
- Chance level: 50.0%
- Metrics: Balanced accuracy (primary) · Macro F1 (secondary)

## Results

Every method run under this protocol, with the score the released results file holds. Compare down this table only: other protocols differ in cohort, electrodes, window or chance level.

**Balanced accuracy, every method under this protocol.** Dot: the estimate; line: descriptive 95% interval; dashed line: chance level.

- Spectral ridge: 54.9% (53.2%–56.7%)
- CSP+LDA: 47.1% (43.8%–50.2%)
- LaBraM: 53.5% (51.4%–55.4%)
- CBraMod: 62.9% (59.6%–66.5%)
- EEGNet: 71.0% (65.0%–76.3%)

| Method | Training mode | Balanced accuracy | Macro F1 | Channels | People | Scoring time |
| --- | --- | --- | --- | --- | --- | --- |
| Spectral ridge — Classical method | Supervised fit | 54.9% (53.2%–56.7%) | 0.544 — Mean across held-out participants | 15 | 10 | 0.2 s |
| [CSP+LDA](https://bci.report/methods/csp-lda/) — Classical method | Supervised fit | 47.1% (43.8%–50.2%) At or below chance level | 0.406 — Mean across held-out participants | 15 | 10 | 8.8 s |
| [LaBraM](https://bci.report/methods/labram/) — Foundation model | Frozen encoder + ridge head | 53.5% (51.4%–55.4%) | 0.509 — Mean across held-out participants | 15 | 10 | 2.2 s |
| [CBraMod](https://bci.report/methods/cbramod/) — Foundation model | Frozen encoder + ridge head | 62.9% (59.6%–66.5%) | 0.617 — Mean across held-out participants | 15 | 10 | 5.0 s |
| [EEGNet](https://bci.report/methods/eegnet/) — Compact model | Scratch · 20 epochs | 71.0% (65.0%–76.3%) Single seed — the highest of the seeds run (see Stability) | 0.680 — Mean across held-out participants | 15 | 10 | 55.5 s |

Scoring time includes fitting and prediction, may include accelerator waiting, and excludes data preparation. It is configuration-specific, not a hardware benchmark.

**Not run under this protocol:** [ShallowFBCSPNet](https://bci.report/methods/shallowfbcspnet/), [Deep4Net](https://bci.report/methods/deep4net/), [Standard CCA](https://bci.report/methods/cca/), Temporal ridge. A method missing here was not run on this protocol — that is not a failure.

## Read with care

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

### The protocol, step by step

- 5 participant-disjoint folds. All recordings from a person stay together. Each person contributes to the held-out predictions once.
- epoching: two seconds from the class annotation onset; amplitude: converted to microvolts according to source calibration, then per-channel epoch mean removed; resampling: none; native sampling rate retained; selection: natural file/event order; when over the cap, retain 60 evenly spaced event indices per participant/class; source_units: Microv declared by BIDS channels.tsv; EDF physical dimension is absent, so MNE returns the source numeric microvolt values without SI scaling
- One fixed seed (20260919); no early stopping or test-based tuning. EEGNet trains for 20 epochs per fold. Frozen encoders use training-only standardized ridge heads (alpha 100).
- Labels: rest, right_hand_imagery.
- Uniform-guessing reference: 50.00%. Scores weight participants equally. Intervals describe participant variation; cross-validation training sets overlap.
- run 0 is real dominant-hand movement and is excluded; only imagery runs 1-4 contribute
- the source reports online 0.5-45 Hz filtering
- controlled cue-locked laboratory windows; this does not measure continuous false activations
- foundation-model pretraining overlap is unknown
- EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.

### Stability

EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.

Seeds run for EEGNet: 71.00% · 69.25% · 68.17%; mean 69.47%

Same participants, folds, preprocessing and 20-epoch budget. Three seeds measure initialization variability, not population uncertainty. Main table retains its preselected seed; no best-seed selection.

### Pretraining exposure

Unknown unless explicitly documented; no unseen-pretraining claim.

### Notes on the methods

- One fixed configuration. Foundation encoders remain frozen; small networks train from scratch. These scores do not establish optimal fine-tuned performance. No individual predictions or participant-level results are distributed.
- One fixed configuration. Foundation encoders remain frozen; small networks train from scratch. These scores do not establish optimal fine-tuned performance. No individual predictions or participant-level results are distributed. EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.

### Model terms

- **Spectral ridge**: Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
- **CSP+LDA**: Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
- **LaBraM**: Code/repository: MIT · Checkpoint: committed in that repository; no separate weight terms
- **CBraMod**: Code: MIT · Weights: Apache-2.0 (official model card)
- **EEGNet**: Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.

## Source and licence

### ds003810

**Credit** Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/

**The dataset authors ask you to cite:** Peterson V, Galván C, Hernández H, Spies R. A feasibility study of a complete low-cost consumer-grade brain-computer interface system. Heliyon 6(3):e03425 (2020). [doi ↗](https://doi.org/10.1016/j.heliyon.2020.e03425) · [as their record asks ↗](https://openneuro.org/datasets/ds003810)

**Licence** [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/)

[Dataset record ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) · [Every result on this dataset →](https://bci.report/datasets/ds003810/)

BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data belong to the people credited.

**Permitted scope** Personal noncommercial research; aggregate results only

**Privacy** Only cohort aggregates are published here: no recording, no participant identifier, no per-person score. [The full review note is in the protocol JSON ↓](https://bci.report/data/mi-rest-protocol.json)

**Public-data register note** Clear CC0 snapshot plus dataset-specific ethics and signed-consent evidence; aggregate-only publication materially limits privacy exposure.

**Rights reviewed** 2026-09-20 · against [doi.org ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) · [pmc.ncbi.nlm.nih.gov ↗](https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/)

**Correction on record · 2026-10-01**: The ds003810 credit links only the 2022 Data in Brief description. The OpenNeuro record asks users to cite Peterson, Galván, Hernández and Spies, Heliyon 6(3):e03425 (2020); the dataset page now gives both. [Corrections register →](https://bci.report/releases/#corrections)

## Reproducibility record

- Protocol id: `parallel-fixed-subject-folds-v1/ds003810`
- Dataset release: OpenNeuro snapshot 2.0.2 · c674303319303b9ffc4ff7e2340b3a4807e8ffb5
- Hardware: Apple M5 / MPS
- Scoring stage sum: 71.7 s · may include accelerator waiting
- Audit record SHA-256: `e762cf7f048bf2df47b9399b9df6171733a93c162ce7800560952df30491a7f2`
- Summary SHA-256: `0dd66f4bd4d083fca1d1ab44a2d2951d57f6d19378079b8cb5ff3ee9241f0cda`
- Protocol SHA-256: `57ddb7692e3bb248b95c4e81b6aa02e7c8b19d774ed4a8b799c811b6ef3b65a2`

## Downloads

- [Results · CSV ↓](https://bci.report/data/mi-rest-results.csv)
- [Protocol · JSON ↓](https://bci.report/data/mi-rest-protocol.json)
- [Core matrix · JSON ↓](https://bci.report/data/experiments.json)

### Other protocols

- [Idle & command on ds005342](https://bci.report/protocols/idle/)
- [SSVEP · 8 channels on BETA](https://bci.report/protocols/beta-8ch/)
- [SSVEP · 4 channels on BETA](https://bci.report/protocols/beta-4ch/)
- [Arithmetic & rest on EEGMAT](https://bci.report/protocols/arithmetic-rest/)
- [P300 target ERP on ds006593](https://bci.report/protocols/p300-target/)
- [Semantic target ERP on TMNRED / ds005383](https://bci.report/protocols/semantic-target/)
- [Sleep staging on EESM19 scalp subset](https://bci.report/protocols/sleep-scalp/)

[All protocols →](https://bci.report/protocols/) · [The core matrix on the home page →](https://bci.report/#overview) · [All datasets →](https://bci.report/datasets/) · [All methods →](https://bci.report/methods/)

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