# Semantic target ERP on TMNRED / ds005383

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: Reading · target versus nontarget events
- Evaluation: Transfer to a new person
- Dataset: [TMNRED / ds005383](https://bci.report/datasets/tmnred/)
- People: 30
- Data: 3,600 epochs · 5 participant-disjoint folds
- Input: 30 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%–52.5%)
- Temporal ridge: 56.3% (54.4%–58.4%)
- LaBraM: 53.2% (51.3%–54.9%)
- CBraMod: 55.8% (54.2%–57.4%)
- EEGNet: 61.4% (58.9%–64.0%)

| Method | Training mode | Balanced accuracy | Macro F1 | Channels | People | Scoring time |
| --- | --- | --- | --- | --- | --- | --- |
| Spectral ridge — Classical method | Supervised fit | 51.0% (49.6%–52.5%) Interval reaches chance level | 0.506 — Mean across held-out participants | 30 | 30 | 0.9 s |
| Temporal ridge — Classical method | Supervised fit | 56.3% (54.4%–58.4%) | 0.559 — Mean across held-out participants | 30 | 30 | 1.2 s |
| [LaBraM](https://bci.report/methods/labram/) — Foundation model | Frozen encoder + ridge head | 53.2% (51.3%–54.9%) | 0.519 — Mean across held-out participants | 30 | 30 | 3.4 s |
| [CBraMod](https://bci.report/methods/cbramod/) — Foundation model | Frozen encoder + ridge head | 55.8% (54.2%–57.4%) | 0.554 — Mean across held-out participants | 30 | 30 | 23.5 s |
| [EEGNet](https://bci.report/methods/eegnet/) — Compact model | Scratch · 20 epochs | 61.4% (58.9%–64.0%) | 0.606 — Mean across held-out participants | 30 | 30 | 83.4 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 event subset with collapsed semantic categories. Natural class prevalence is not preserved. Third-party reading passages and participant metadata are excluded from this website.

### 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: [event 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.
- Balancing to 60 per class changes the source target/nontarget prevalence.
- Events use numbered target/nontarget variants; the benchmark collapses the suffix only because the trial_type prefix explicitly names the semantic class.

### 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

### TMNRED / ds005383

**Credit** 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.

**Licence** [OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/)

[Dataset record ↗](https://doi.org/10.18112/openneuro.ds005383.v1.0.0) · [Every result on this dataset →](https://bci.report/datasets/tmnred/)

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/semantic-target-protocol.json)

**Public-data register note** Strong study-specific open-sharing evidence. Aggregate metrics avoid redistribution of stimulus text and participant metadata; credit under CC BY.

**Rights reviewed** 2026-09-20 · against [doi.org ↗](https://doi.org/10.18112/openneuro.ds005383.v1.0.0) · [www.nature.com ↗](https://www.nature.com/articles/s41597-025-05036-2)

## Reproducibility record

- Protocol id: `parallel-fixed-subject-folds-v1/ds005383`
- Dataset release: OpenNeuro snapshot 1.0.0 · ad78f3db430e636595b1b1c08417492f3067a25e
- Hardware: Local Ubuntu / CUDA
- Scoring stage sum: 112.4 s · may include accelerator waiting
- Audit record SHA-256: `5920974ae9ca3876ce1be82497c139f0783d2ff47173641e329af976441de4c4`
- Summary SHA-256: `c5dca7fc30d07a001b8980a4ea65e55ef292f0891a4d32a12262b8ec88389243`
- Protocol SHA-256: `839dfba89cf200aac7a28a7259f45024d006fcd4500170e247fcc1c4e9962c8d`

## Downloads

- [Results · CSV ↓](https://bci.report/data/semantic-target-results.csv)
- [Protocol · JSON ↓](https://bci.report/data/semantic-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/)
- [P300 target ERP on ds006593](https://bci.report/protocols/p300-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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