BCI Report40-target SSVEP
BETA (BEnchmark database Towards BCI Application) is a 40-target SSVEP dataset: 64-channel EEG from 70 participants performing a cued-spelling task on a keyboard-style virtual speller (8–15.8 Hz), four blocks each, recorded outside an electromagnetically shielded room. Released by Liu et al. (Tsinghua University) in Frontiers in Neuroscience (2020), with data on figshare.
Also known as BETA · BETA database · The BETA database · BETA: A Large Benchmark Database Toward SSVEP-BCI Application · Liu et al. (2020)
Description sources figshare.com · pmc.ncbi.nlm.nih.gov · doi.org · bci.med.tsinghua.edu.cn
Follow-up tests on BETA’s held-out frequencies: What lets a decoder answer an SSVEP frequency it never trained on?
Each figure comes from the reviewed download linked in its group. One row is above another only where their 95% intervals do not overlap.
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Standard CCA | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 63.1%57.2%–69.0% | 70 |
| Spectral ridge | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 50.7%45.6%–55.8% | 70 |
| LaBraM | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 10.8%9.5%–12.2% | 70 |
| CBraMod | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 33.7%29.8%–37.6% | 70 |
| EEGNet | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 55.8%50.1%–61.3% | 70 |
| Filter-bank CCA (FBCCA-M3-N7-H5) | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 79.1%75.1%–82.8%FBCCA-M3-N7-H5 · seven Chebyshev sub-bands, five harmonics · training-free · 250 Hz | 70 |
| ShallowFBCSPNet | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 23.0%20.3%–25.9%One fixed seed and training budget, as the EEGNet cell | 70 |
| Deep4Net | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 52.8%47.3%–58.2%One fixed seed and training budget, as the EEGNet cell | 70 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Standard CCA | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 57.6%51.8%–63.5% | 70 |
| Spectral ridge | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 48.2%43.1%–53.3% | 70 |
| LaBraM | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 12.9%11.2%–14.6% | 70 |
| CBraMod | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 27.6%24.1%–31.3% | 70 |
| EEGNet | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 44.1%38.6%–49.6% | 70 |
| Filter-bank CCA (FBCCA-M3-N7-H5) | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 72.3%67.5%–76.9%FBCCA-M3-N7-H5 · seven Chebyshev sub-bands, five harmonics · training-free · 250 Hz | 70 |
| ShallowFBCSPNet | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 16.2%14.1%–18.6%One fixed seed and training budget, as the EEGNet cell | 70 |
| Deep4Net | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 38.7%33.7%–43.8%One fixed seed and training budget, as the EEGNet cell | 70 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| REVE Base | Frozen encoder + ridge head | Balanced accuracy | 37.5%33.3%–41.9% | 70 |
| REVE Large | Frozen encoder + ridge head | Balanced accuracy | 52.7%47.7%–57.8% | 70 |
| LUNA Base | Frozen encoder + ridge head | Balanced accuracy | 8.6%7.7%–9.5% | 70 |
| LUNA Large | Frozen encoder + ridge head | Balanced accuracy | 10.0%8.9%–11.1% | 70 |
| BrainOmni Base | Frozen encoder + ridge head | Balanced accuracy | 21.7%18.6%–25.0% | 70 |
| CodeBrain | Frozen encoder + ridge head | Balanced accuracy | 11.9%10.5%–13.3% | 70 |
| EEGMamba | Frozen encoder + ridge head | Balanced accuracy | 40.2%35.7%–44.7% | 70 |
| ST-EEGFormer Base | Frozen encoder + ridge head | Balanced accuracy | 17.1%15.5%–18.8%Pretraining exposure: in the authors' published pretraining list. | 70 |
| ST-EEGFormer Large | Frozen encoder + ridge head | Balanced accuracy | 22.6%20.1%–25.3%Pretraining exposure: in the authors' published pretraining list. | 70 |
| eeg-fm-masking MAE (r=9 cm, L=2) | Frozen encoder + ridge head | Balanced accuracy | 54.8%49.4%–60.0% | 70 |
| ERP-FM Base | Frozen encoder + ridge head | Balanced accuracy | 18.4%16.3%–20.5% | 70 |
| SingLEM | Frozen encoder + ridge head | Balanced accuracy | 26.6%23.7%–29.5% | 70 |
| ZUNA 1.1 | Frozen encoder + ridge head | Balanced accuracy | 11.9%10.8%–13.0%Pretraining exposure: unknown: the authors do not list their pretraining data closely enough to decide. Model card: research use only, not for diagnosis or clinical use. | 70 |
| eeg-fm-masking JEPA (r=9 cm, L=2) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 49.6%44.5%–54.8% | 70 |
| eeg-fm-masking MAE (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 53.4%48.1%–58.6% | 70 |
| eeg-fm-masking JEPA (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 51.0%45.8%–56.1% | 70 |
Weights terms: REVE under the REVE Responsible Use License v1.0 (model versions: REVE Base @ dc2a075c · REVE Large @ 317531c7); LUNA under CC BY-ND 4.0, with no modified weights shared; BrainOmni, EEGMamba, ST-EEGFormer and SingLEM under MIT; CodeBrain and ZUNA under Apache-2.0; eeg-fm-masking under CC-BY-4.0; ERP-FM under CC BY-NC-SA 4.0, non-commercial. No model’s authors endorse these results. Every weights licence and its terms →
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| REVE Base | Frozen encoder + ridge head | Balanced accuracy | 25.5%22.4%–28.8% | 70 |
| REVE Large | Frozen encoder + ridge head | Balanced accuracy | 46.4%41.7%–51.2% | 70 |
| LUNA Base | Frozen encoder + ridge head | Balanced accuracy | 9.0%8.1%–10.1% | 70 |
| LUNA Large | Frozen encoder + ridge head | Balanced accuracy | 11.2%9.8%–12.6% | 70 |
| BrainOmni Base | Frozen encoder + ridge head | Balanced accuracy | 13.0%10.6%–15.6% | 70 |
| CodeBrain | Frozen encoder + ridge head | Balanced accuracy | 11.2%9.8%–12.7% | 70 |
| EEGMamba | Frozen encoder + ridge head | Balanced accuracy | 32.7%28.9%–36.7% | 70 |
| ST-EEGFormer Base | Frozen encoder + ridge head | Balanced accuracy | 17.5%15.8%–19.3%Pretraining exposure: in the authors' published pretraining list. | 70 |
| ST-EEGFormer Large | Frozen encoder + ridge head | Balanced accuracy | 22.4%19.9%–25.0%Pretraining exposure: in the authors' published pretraining list. | 70 |
| eeg-fm-masking MAE (r=9 cm, L=2) | Frozen encoder + ridge head | Balanced accuracy | 47.1%42.1%–52.1% | 70 |
| ERP-FM Base | Frozen encoder + ridge head | Balanced accuracy | 20.9%18.4%–23.5% | 70 |
| SingLEM | Frozen encoder + ridge head | Balanced accuracy | 20.5%17.8%–23.3% | 70 |
| ZUNA 1.1 | Frozen encoder + ridge head | Balanced accuracy | 8.4%7.5%–9.4%Pretraining exposure: unknown: the authors do not list their pretraining data closely enough to decide. Model card: research use only, not for diagnosis or clinical use. | 70 |
| eeg-fm-masking JEPA (r=9 cm, L=2) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 43.4%38.7%–48.3% | 70 |
| eeg-fm-masking MAE (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 46.7%41.9%–51.5% | 70 |
| eeg-fm-masking JEPA (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 45.4%40.6%–50.2% | 70 |
Weights terms: REVE under the REVE Responsible Use License v1.0 (model versions: REVE Base @ dc2a075c · REVE Large @ 317531c7); LUNA under CC BY-ND 4.0, with no modified weights shared; BrainOmni, EEGMamba, ST-EEGFormer and SingLEM under MIT; CodeBrain and ZUNA under Apache-2.0; eeg-fm-masking under CC-BY-4.0; ERP-FM under CC BY-NC-SA 4.0, non-commercial. No model’s authors endorse these results. Every weights licence and its terms →
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| EEGPT | Frozen encoder + ridge head | Balanced accuracy | 20.3%18.2%–22.6% | 70 |
PO5 and PO6 use channel embeddings that EEGPT’s pretraining never trained: they are two of the four names in its 62-name channel dictionary (AF7, AF8, PO5, PO6) that its 58-electrode pretraining montage leaves out. The other six channels here, and all four of the 4-channel subset, use trained embeddings.
For context: BETA is not in EEGPT’s published pretraining list, but the Tsinghua 40-target SSVEP benchmark (Wang et al., 2017) is, as TSU. BETA’s authors describe BETA as an extension of that benchmark, from the same group with the same 40-target layout and its own recordings; whether anyone took part in both is not stated.
Every core window is shorter than EEGPT’s 4-second pretraining window: 1 or 2 seconds, and sleep as fifteen 2-second segments. The encoder computes each 0.25-second patch the same way at any length; a shorter window gives fewer patches to average.
The readout is the site’s fixed probe on the frozen summary tokens, as for the published LaBraM and CBraMod rows: ridge (alpha 100) on 2,048 pooled values, and on idle a per-person logistic head. It is not the authors’ downstream probe, a learnable spatial filter with a linear head, and the windows are fed without the average reference EEGPT was pretrained with.
EEGPT: the checkpoint’s licence is unresolved, and no original or converted weights are distributed; Apache-2.0 covers the repository’s code only. Its licence and terms →
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| EEGPT | Frozen encoder + ridge head | Balanced accuracy | 17.3%15.5%–19.2% | 70 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Standard CCA | Fixed threshold: calibrated confidence at least 0.8 | Trials accepted | 3,176 / 11,2000 of 70 people had nothing accepted. | 70 |
| Standard CCA | Learned reject option minus calibrated confidence, same classifier | Error among the 80% most certain, difference | +0.3 pp−0.3 pp–+1.0 ppNo difference resolved: the interval includes zero. | 70 |
| CBraMod | Fixed threshold: calibrated confidence at least 0.8 | Trials accepted | 165 / 11,20038 of 70 people had nothing accepted. | 70 |
| CBraMod | Learned reject option minus calibrated confidence, same classifier | Error among the 80% most certain, difference | −0.03 pp−0.8 pp–+0.6 ppNo difference resolved: the interval includes zero. | 70 |
| EEGNet | Fixed threshold: calibrated confidence at least 0.8 | Trials accepted | 1,863 / 11,20010 of 70 people had nothing accepted. | 70 |
| EEGNet | Learned reject option minus calibrated confidence, same classifier | Error among the 80% most certain, difference | +0.8 pp+0.02 pp–+1.6 ppDifference resolved: the interval excludes zero. | 70 |
Limits: Uniform target prior by design; cue-paced lab spelling with no idle state, so no false-activation figure of any kind. Calibration data are participant-disjoint inner out-of-fold scores (cross-fit). Inner models see about 3/4 of the training people, so they may be less confident than the full model whose scores are tested. This approximation is part of every calibrated figure. Rejecting likely errors on known classes is not detecting unfamiliar input. An uncertainty threshold may not flag an unseen class (https://arxiv.org/abs/2603.13324). Route 1 makes no out-of-distribution claim. A contrast is called a difference only when its paired 95% participant-bootstrap interval excludes 0 (no multiplicity correction); otherwise "no difference resolved". No ranking where intervals overlap. Descriptive entries are never called differences. Every limitation of route 1 →
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Plain spectrum (L0) | P1 · label template − question number (TPL − ID) · seen, 32-way · three seeds | Paired difference, language arm minus reference (pp) | −5.96 pp−6.84–−5.08 ppID higher; 2 pp margin not met; passes the triviality canary. | 70 |
| Plain spectrum (L0) | P1 · description − question number (DESC − ID) · seen, 32-way · three seeds | Paired difference, language arm minus reference (pp) | −5.46 pp−6.26–−4.66 ppID higher; 2 pp margin not met; passes the triviality canary. | 70 |
| Plain spectrum (L0) | P2 · new sentence frame − training wording (TPL(a1) − TPL(train)) · seen, reworded · three seeds | Paired difference, language arm minus reference (pp) | −4.01 pp−6.53–−2.12 ppTPL(train) higher; 2 pp margin not met; passes the triviality canary. | 70 |
| Plain spectrum (L0) | P2 · synonym − training wording (TPL(a2) − TPL(train)) · seen, reworded, for these three synonyms · three seeds | Paired difference, language arm minus reference (pp) | −1.84 pp−2.10–−1.57 ppTPL(train) higher; 2 pp margin not met; passes the triviality canary. | 70 |
| Plain spectrum (L0) | P2 · new description − training descriptions (DESC(a3) − DESC(train)) · seen, reworded · three seeds | Paired difference, language arm minus reference (pp) | −4.13 pp−5.58–−3.11 ppDESC(train) higher; 2 pp margin not met; passes the triviality canary. | 70 |
| Plain spectrum (L0) | P4 · label template − CCA, no training (TPL − CCA) · unseen frequency, 8-way · three seeds | Paired difference, language arm minus reference (pp) | −52.21 pp−55.13–−48.99 ppUnseen question. CCA higher; 2 pp margin not met; headroom gate passed. | 70 |
| Plain spectrum (L0) | P4 · label template − average of the two neighbouring seen frequencies (TPL − NN(TPL)) · unseen frequency, 8-way · three seeds | Paired difference, language arm minus reference (pp) | −10.98 pp−12.24–−9.67 ppUnseen question. NN(TPL) higher; difference only: no margin; headroom gate passed. | 70 |
| Plain spectrum (L0) | P4 · label template − numeric code (TPL − NUM) · unseen frequency, 8-way · three seeds | Paired difference, language arm minus reference (pp) | −12.77 pp−14.52–−10.99 ppUnseen question. NUM higher; difference only: no margin; headroom gate passed. | 70 |
| Plain spectrum (L0) | P4 · label template − CCA, no training (TPL − CCA) · unseen frequency, neighbour 3-way · three seeds | Paired difference, language arm minus reference (pp) | −62.36 pp−65.87–−58.69 ppUnseen question. CCA higher; 2 pp margin not met; headroom gate passed. | 70 |
| Plain spectrum (L0) | P4 · label template − numeric code (TPL − NUM) · unseen frequency, neighbour 3-way · three seeds | Paired difference, language arm minus reference (pp) | −1.24 pp−3.17–+0.85 ppUnseen question. No difference shown; difference only: no margin; headroom gate passed. | 70 |
| CBraMod | P1 · label template − question number (TPL − ID) · seen, 32-way · three seeds | Paired difference, language arm minus reference (pp) | −3.49 pp−4.10–−2.93 ppID higher; 2 pp margin not met; passes the triviality canary. | 70 |
| CBraMod | P1 · description − question number (DESC − ID) · seen, 32-way · three seeds | Paired difference, language arm minus reference (pp) | −3.37 pp−3.98–−2.80 ppID higher; 2 pp margin not met; passes the triviality canary. | 70 |
| CBraMod | P2 · new sentence frame − training wording (TPL(a1) − TPL(train)) · seen, reworded · three seeds | Paired difference, language arm minus reference (pp) | −2.72 pp−4.43–−1.43 ppTPL(train) higher; 2 pp margin not met; passes the triviality canary. | 70 |
| CBraMod | P2 · synonym − training wording (TPL(a2) − TPL(train)) · seen, reworded, for these three synonyms · three seeds | Paired difference, language arm minus reference (pp) | −1.33 pp−1.53–−1.14 ppTPL(train) higher; equivalent within ±2 pp; passes the triviality canary. | 70 |
| CBraMod | P2 · new description − training descriptions (DESC(a3) − DESC(train)) · seen, reworded · three seeds | Paired difference, language arm minus reference (pp) | −2.95 pp−3.81–−2.26 ppDESC(train) higher; 2 pp margin not met; passes the triviality canary. | 70 |
| CBraMod | P4 · label template − CCA, no training (TPL − CCA) · unseen frequency, 8-way · three seeds | Paired difference, language arm minus reference (pp) | −61.94 pp−65.65–−58.06 ppUnseen question. CCA higher; 2 pp margin not met; headroom gate passed. | 70 |
| CBraMod | P4 · label template − average of the two neighbouring seen frequencies (TPL − NN(TPL)) · unseen frequency, 8-way · three seeds | Paired difference, language arm minus reference (pp) | −1.16 pp−1.97–−0.37 ppUnseen question. NN(TPL) higher; difference only: no margin; headroom gate passed. | 70 |
| CBraMod | P4 · label template − numeric code (TPL − NUM) · unseen frequency, 8-way · three seeds | Paired difference, language arm minus reference (pp) | −2.46 pp−3.23–−1.69 ppUnseen question. NUM higher; difference only: no margin; headroom gate passed. | 70 |
| CBraMod | P4 · label template − CCA, no training (TPL − CCA) · unseen frequency, neighbour 3-way (frequency and stimulus phase) · three seeds | Paired difference, language arm minus reference (pp) | −54.67 pp−57.77–−51.47 ppUnseen question. CCA higher; 2 pp margin not met; headroom gate passed. | 70 |
| CBraMod | P4 · label template − numeric code (TPL − NUM) · unseen frequency, neighbour 3-way (frequency and stimulus phase) · three seeds | Paired difference, language arm minus reference (pp) | +15.61 pp+14.25–+16.93 ppUnseen question. TPL higher; difference only: no margin; headroom gate passed. | 70 |
Limits: 'Unseen' means unseen by the EEG head; the text encoder has read these words, and every token of a held-out frequency also occurs in the training texts. Held-out SSVEP questions name a frequency, which CCA answers without training; interpolation tested, extrapolation secondary. In the neighbour 3-way the unseen frequency competes against trained detectors for its neighbours; at L1 that test also involves stimulus phase. Rewording and unseen results are separate rows and never pooled. 35 pre-declared comparisons, not corrected for multiplicity. The pre-run permutation canary failed twice (revisions 3 and 4) and was revised by the owner, who overrode revision 4's "no further revision"; revision 5 passed. Both failed records are kept and disclosed. Every limitation of route 3 →
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.
CC BY 4.0 ↗ · Dataset record ↗
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
A longer write-up of the core-matrix results on BETA is on Hugging Face: CCA and frozen EEG foundation models on BETA ↗
BCI Report (2026). BETA: EEG decoding results. https://bci.report/datasets/beta/
Figures from releases core-matrix-update-20261106 (2026-11-06), eegpt-core-update-20261013 (2026-10-13), questions-in-language-update-20261008 (2026-10-08), foundation-models-update-20261004 (2026-10-04), reliable-decisions-update-20261004 (2026-10-04), research-preview-20260920 (2026-09-20). Cite the upstream dataset as well: its credit is on this page.
Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗