Core-matrix protocol · Transfer to a new person

P300 target ERP on ds006593

Name: BCI Report: P300 target ERP on ds006593 — protocol and results
Creator: BCI Report
License: https://creativecommons.org/licenses/by/4.0/

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: Visual target versus nontarget events
Evaluation: Transfer to a new person
Dataset: ds006593
People: 21
Data: 2,520 epochs · 5 participant-disjoint folds
Input: 19 channels · 1-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.

Spectral ridge

51.0% (49.6%–52.4%)

Temporal ridge

53.8% (51.9%–55.6%)

LaBraM

49.4% (47.7%–51.2%)

CBraMod

55.0% (53.3%–56.7%)

EEGNet

53.3% (51.7%–54.9%)

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

Method	Training mode	Balanced accuracy	Macro F1	Channels	People	Scoring time
Spectral ridgeClassical method	Supervised fit	51.0%49.6%–52.4%Interval reaches chance level	0.505Mean across held-out participants	19	21	0.4 s
Temporal ridgeClassical method	Supervised fit	53.8%51.9%–55.6%	0.520Mean across held-out participants	19	21	0.5 s
LaBraMFoundation model	Frozen encoder + ridge head	49.4%47.7%–51.2%At or below chance level	0.486Mean across held-out participants	19	21	10.6 s
CBraModFoundation model	Frozen encoder + ridge head	55.0%53.3%–56.7%	0.529Mean across held-out participants	19	21	4.8 s
EEGNetCompact model	Scratch · 20 epochs	53.3%51.7%–54.9%	0.500Mean across held-out participants	19	21	45.6 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: CSP+LDA, ShallowFBCSPNet, Deep4Net, Standard CCA. A method missing here was not run on this protocol — that is not a failure.

Read with care

Balanced target/nontarget sample changes the source 1:9 prevalence. One-second windows include later flashes at 0.3-second intervals; this is not an online speller estimate.

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.
units: microvolts; epoch: [stimulus onset, onset + 1.0 s); filtering: none added
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: nontarget, target.
Uniform-guessing reference: 50.00%. Scores weight participants equally. Intervals describe participant variation; cross-validation training sets overlap.
Research subset with fixed deterministic sampling capped at 60 epochs per participant/class.
The cap balances target and nontarget and therefore changes the original approximately 1:9 class prevalence.
Stimuli were presented about every 0.3 s, so each 1 s epoch contains responses to later stimuli; this is intrinsic next-stimulus contamination.

Stability

One fixed seed and training budget; multi-seed sensitivity pending.

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.

Model terms

Spectral ridge: Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
Temporal ridge: 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

ds006593

Credit OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source.

Licence CC0-1.0 ↗

Dataset record ↗ · Every result on this dataset →

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 ↓

Public-data register note Pinned CC0 release with dataset-specific IRB and consent evidence and comparatively low metadata exposure.

Rights reviewed 2026-09-20 · against doi.org ↗ · nemar.org ↗

Reproducibility record

Protocol id: parallel-fixed-subject-folds-v1/ds006593
Dataset release: OpenNeuro snapshot 1.0.0 · b3fa345b310abfcf442dd4e2867f6652336c3fe4
Hardware: Local Ubuntu / CUDA
Scoring stage sum: 61.9 s · may include accelerator waiting
Audit record SHA-256: 30762120c57bede9f47d2e96637656e8948fca458016f7f2e3a39f951a357716
Summary SHA-256: a63a7ba5bbd6dd7865ae260b707c2db896136646a910d96516fa5c862e1146c0
Protocol SHA-256: bf6796dfe04dffa57bdd08933d2bb743722da71594135f04fa4fc33f3fb7dd46

Downloads

Results · CSV ↓Protocol · JSON ↓Core matrix · JSON ↓

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