# SSVEP · 4 channels on BETA

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: 40 visual targets · 4 posterior electrodes
- Evaluation: Transfer to a new person
- Dataset: [BETA](https://bci.report/datasets/beta/)
- People: 70
- Data: 11,200 trials · 7 participant-disjoint folds
- Input: 4 channels selected from a 64-channel recording · 2-second windows
- Chance level: 2.5%
- 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.

- Standard CCA: 57.6% (51.8%–63.5%)
- Spectral ridge: 48.2% (43.1%–53.3%)
- LaBraM: 12.9% (11.2%–14.6%)
- CBraMod: 27.6% (24.1%–31.3%)
- EEGNet: 44.1% (38.6%–49.6%)

| Method | Training mode | Balanced accuracy | Macro F1 | Channels | People | Scoring time |
| --- | --- | --- | --- | --- | --- | --- |
| [Standard CCA](https://bci.report/methods/cca/) — Classical method | Known-frequency reference · no fitting | 57.6% (51.8%–63.5%) | 0.567 — Mean across held-out participants | 4 | 70 | 1.1 s |
| Spectral ridge — Classical method | Supervised fit | 48.2% (43.1%–53.3%) | 0.467 — Mean across held-out participants | 4 | 70 | 0.7 s |
| [LaBraM](https://bci.report/methods/labram/) — Foundation model | Frozen encoder + ridge head | 12.9% (11.2%–14.6%) | 0.110 — Mean across held-out participants | 4 | 70 | 2.5 s |
| [CBraMod](https://bci.report/methods/cbramod/) — Foundation model | Frozen encoder + ridge head | 27.6% (24.1%–31.3%) | 0.257 — Mean across held-out participants | 4 | 70 | 3.0 s |
| [EEGNet](https://bci.report/methods/eegnet/) — Compact model | Scratch · 20 epochs | 44.1% (38.6%–49.6%) | 0.421 — Mean across held-out participants | 4 | 70 | 146.7 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/), Temporal ridge. A method missing here was not run on this protocol — that is not a failure.

## Read with care

Near-floor scores are not ordered reliably between the 8- and 4-electrode subsets. Electrode subsets from laboratory recordings do not validate a physical low-channel cap. Prompted SSVEP does not measure idle false activations. Single-seed results; pretraining overlap unknown.

### The protocol, step by step

- Seven participant-disjoint folds: train on 60 people, test on ten. All four blocks stay with their participant.
- Two seconds from stimulus onset; no visual-latency shift. Source data were already zero-phase filtered. Additional 6–80 Hz filtering applies to each selected window separately.
- Microvolt units are inferred from an independently documented loader, not explicitly stated in the author MAT description. Inconsistent phase metadata are unused by all methods.
- Standard CCA uses known frequencies and three harmonics without training labels. Frozen encoders use training-only standardized ridge heads (alpha 100). EEGNet trains from scratch for 20 epochs with one seed (20260912).
- Electrodes: POZ, O1, OZ, O2.
- Uniform-guessing reference: 2.5%. Descriptive 95% intervals resample participants; training sets overlap across folds.
- No cross-task overall ranking, model fine-tuning optimum or hardware benchmark is claimed.

### 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

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

### BETA

**Credit** Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA.

**Licence** [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/)

[Dataset record ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) · [Every result on this dataset →](https://bci.report/datasets/beta/)

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/beta-4ch-protocol.json)

**Public-data register note** Eligible only under the stated personal, research-led, noncommercial operation. Any ads, sponsorship, fees, or use directed toward commercial advantage requires fresh review or permission.

**Rights reviewed** 2026-09-20 · against [figshare.com ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) · [doi.org ↗](https://doi.org/10.3389/fnins.2020.00627)

## Reproducibility record

- Protocol id: `beta-ssvep-2s-posterior4and8-subject7fold-v1/posterior4`
- Dataset release: Hugging Face mirror of BETA database, Figshare record 12264401 v3 · mirror d4290c0200db8a104e0f557349dc49f90ba79506 · upstream Figshare version 3, 2022-06-15
- Hardware: Apple M5 / MPS and CPU
- Scoring stage sum: 153.9 s · may include accelerator waiting
- Audit record SHA-256: `ccddf911864b8471f488e46cae45a3651369dae1134afcdeaede68bdfcc31950`
- Summary SHA-256: `547a35763f7bca10a65c7b44c2f8c46af290159c692d237e92cd682fc21127a6`
- Protocol SHA-256: `dcd1d2bf4dbb46b7db8c4562623448633cde4641fa372c90f25ba9d84012c2f0`

## Downloads

- [Results · CSV ↓](https://bci.report/data/beta-4ch-results.csv)
- [Protocol · JSON ↓](https://bci.report/data/beta-4ch-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/)
- [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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