# P300 target ERP on ds006593

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: Visual target versus nontarget events
- Evaluation: Transfer to a new person
- Dataset: [ds006593](https://bci.report/datasets/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.

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

| Method | Training mode | Balanced accuracy | Macro F1 | Channels | People | Scoring time |
| --- | --- | --- | --- | --- | --- | --- |
| Spectral ridge — Classical method | Supervised fit | 51.0% (49.6%–52.4%) Interval reaches chance level | 0.505 — Mean across held-out participants | 19 | 21 | 0.4 s |
| Temporal ridge — Classical method | Supervised fit | 53.8% (51.9%–55.6%) | 0.520 — Mean across held-out participants | 19 | 21 | 0.5 s |
| [LaBraM](https://bci.report/methods/labram/) — Foundation model | Frozen encoder + ridge head | 49.4% (47.7%–51.2%) At or below chance level | 0.486 — Mean across held-out participants | 19 | 21 | 10.6 s |
| [CBraMod](https://bci.report/methods/cbramod/) — Foundation model | Frozen encoder + ridge head | 55.0% (53.3%–56.7%) | 0.529 — Mean across held-out participants | 19 | 21 | 4.8 s |
| [EEGNet](https://bci.report/methods/eegnet/) — Compact model | Scratch · 20 epochs | 53.3% (51.7%–54.9%) | 0.500 — Mean 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](https://bci.report/methods/csp-lda/), [ShallowFBCSPNet](https://bci.report/methods/shallowfbcspnet/), [Deep4Net](https://bci.report/methods/deep4net/), [Standard CCA](https://bci.report/methods/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 ↗](https://creativecommons.org/publicdomain/zero/1.0/)

[Dataset record ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) · [Every result on this dataset →](https://bci.report/datasets/ds006593/)

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/p300-target-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 ↗](https://doi.org/10.18112/openneuro.ds006593.v1.0.0) · [nemar.org ↗](https://nemar.org/dataset/on006593)

## 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 ↓](https://bci.report/data/p300-target-results.csv)
- [Protocol · JSON ↓](https://bci.report/data/p300-target-protocol.json)
- [Core matrix · JSON ↓](https://bci.report/data/experiments.json)

### Other protocols

- [Motor imagery & rest on ds003810](https://bci.report/protocols/mi-rest/)
- [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/)
- [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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