BCI Report12-target SSVEP, dry and wet electrodes
An open wearable SSVEP dataset: 8-channel EEG from 102 healthy participants performing a cue-guided 12-target SSVEP task (9.25–14.75 Hz), recorded with wet and with dry electrodes, 10 consecutive blocks each. Participants were recruited through the BCI Brain-Controlled Robot Contest at the 2020 World Robot Contest. Released by Zhu et al. in Sensors (2021), with data on figshare.
Also known as Wearable SSVEP BCI Dataset · An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces · figshare 13560281 · Zhu et al. (2021)
Description sources figshare.com · pmc.ncbi.nlm.nih.gov · doi.org · bci.med.tsinghua.edu.cn
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 |
|---|---|---|---|---|
| Uniform randomcomparator | Wet recording · no source training | Balanced accuracy | 8.6%8.1%–9.1% | 102 |
| Uniform randomcomparator | Dry recording · no source training | Balanced accuracy | 8.1%7.6%–8.6% | 102 |
| Same-frequency powercomparator | Wet recording · no source training | Balanced accuracy | 39.8%35.4%–44.3% | 102 |
| Same-frequency powercomparator | Dry recording · no source training | Balanced accuracy | 36.0%31.8%–40.2% | 102 |
| CCA | Wet recording · no source training | Balanced accuracy | 81.6%77.7%–85.1% | 102 |
| CCA | Dry recording · no source training | Balanced accuracy | 65.5%60.6%–70.2% | 102 |
| FBCCA | Wet recording · no source training | Balanced accuracy | 82.3%78.7%–85.7%Five Butterworth order-4 sub-bands (6–80 to 38–80 Hz), four harmonics | 102 |
| FBCCA | Dry recording · no source training | Balanced accuracy | 64.6%59.5%–69.6%Five Butterworth order-4 sub-bands (6–80 to 38–80 Hz), four harmonics | 102 |
| Spectral ridge | Trained on wet, tested on wet | Balanced accuracy | 40.3%36.5%–44.3% | 102 |
| Spectral ridge | Trained on wet, tested on dry | Balanced accuracy | 35.2%31.8%–38.8% | 102 |
| Spectral ridge | Trained on dry, tested on dry | Balanced accuracy | 37.2%33.5%–41.0% | 102 |
| Spectral ridge | Trained on dry, tested on wet | Balanced accuracy | 39.7%35.9%–43.6% | 102 |
| EEGNet | Trained on wet, tested on wet | Balanced accuracy | 75.0%70.3%–79.5% | 102 |
| EEGNet | Trained on wet, tested on dry | Balanced accuracy | 45.6%40.5%–50.8% | 102 |
| EEGNet | Trained on dry, tested on wet | Balanced accuracy | 64.0%59.1%–68.8% | 102 |
| EEGNet | Trained on dry, tested on dry | Balanced accuracy | 56.8%51.9%–61.6% | 102 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Spectral ridge | Source-only, trained on wet, tested on wet | Balanced accuracy | 39.2%35.4%–43.0% | 102 |
| Spectral ridge | Source-only, trained on wet, tested on dry | Balanced accuracy | 35.4%32.0%–39.0% | 102 |
| Spectral ridge | Source-only, trained on dry, tested on wet | Balanced accuracy | 39.0%35.2%–42.9% | 102 |
| Spectral ridge | Source-only, trained on dry, tested on dry | Balanced accuracy | 37.2%33.6%–40.9% | 102 |
| Spectral ridge | 12 calibration trials · wet | Balanced accuracy | 15.3%13.7%–17.1% | 102 |
| Spectral ridge | 24 calibration trials · wet | Balanced accuracy | 18.2%15.9%–20.7% | 102 |
| Spectral ridge | 48 calibration trials · wet | Balanced accuracy | 21.3%18.3%–24.5% | 102 |
| Spectral ridge | 12 calibration trials · dry | Balanced accuracy | 15.0%13.6%–16.5% | 102 |
| Spectral ridge | 24 calibration trials · dry | Balanced accuracy | 18.3%16.3%–20.7% | 102 |
| Spectral ridge | 48 calibration trials · dry | Balanced accuracy | 21.8%19.3%–24.5% | 102 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| CCA | No calibration · dry | Balanced accuracy | 65.1%60.0%–70.0% | 102 |
| CCA | No calibration · wet | Balanced accuracy | 81.1%77.2%–84.8% | 102 |
| Single-band eTRCA | 24 calibration trials · dry | Balanced accuracy | 12.4%10.4%–15.0% | 102 |
| Single-band eTRCA | 24 calibration trials · wet | Balanced accuracy | 39.8%34.1%–45.8% | 102 |
| Single-band eTRCA | 48 calibration trials · dry | Balanced accuracy | 15.1%12.6%–18.2% | 102 |
| Single-band eTRCA | 48 calibration trials · wet | Balanced accuracy | 64.0%57.1%–70.7% | 102 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| CCA with learned correction | Dry electrodes · trained on BETA, run unchanged | Accuracy | 65.45%60.82%–70.08% | 102 |
| Matched CCAcomparator | Dry electrodes · no training | Accuracy | 65.38%60.73%–70.06% | 102 |
| CCA with learned correction | Dry electrodes · CCA with learned correction minus matched CCA, the same people and windows (primary contrast) | Paired difference, accuracy | +0.07 pp−0.71–+0.91 ppThe interval includes zero. | 102 |
| Cropped-spectrum check (Wearable-102)comparator | Dry electrodes · no training, a diagnostic only | Accuracy | 57.78%52.79%–62.89% | 102 |
| CCA with learned correction | Wet electrodes · trained on BETA, run unchanged | Accuracy | 81.04%77.14%–84.49% | 102 |
| Matched CCAcomparator | Wet electrodes · no training | Accuracy | 81.65%77.60%–85.14% | 102 |
| CCA with learned correction | Wet electrodes · CCA with learned correction minus matched CCA, the same people and windows (primary contrast) | Paired difference, accuracy | −0.61 pp−1.48–+0.23 ppThe interval includes zero. | 102 |
| Cropped-spectrum check (Wearable-102)comparator | Wet electrodes · no training, a diagnostic only | Accuracy | 73.01%68.46%–77.18% | 102 |
| CCA with learned correction | Both sensors · trained on BETA, run unchanged | Accuracy | 73.24%69.40%–76.88% | 102 |
| Matched CCAcomparator | Both sensors · no training | Accuracy | 73.51%69.71%–77.22% | 102 |
| CCA with learned correction | Both sensors · CCA with learned correction minus matched CCA, the same people and windows (secondary contrast) | Paired difference, accuracy | −0.27 pp−0.95–+0.40 ppThe interval includes zero. | 102 |
| Cropped-spectrum check (Wearable-102)comparator | Both sensors · no training, a diagnostic only | Accuracy | 65.40%60.94%–69.69% | 102 |
The 105 models are CCA with a learned correction, trained on BETA (7 participant folds × 5 frequency rotations × 3 training seeds), run with no retraining on 102 people’s recordings as stored: 12 targets, 12,240 two-second windows per sensor. Each person’s accuracy is averaged over the 105 models, then over people, with no ensemble or model choice. Both methods are given the 12 candidate frequencies. The model’s score is a scaled CCA correlation plus a bounded learned term, so its difference from matched CCA is what the learned term adds. Intervals: paired person bootstrap, 2,000 draws, conditional on the fixed 105 models and unadjusted for the two sensors.
Matched CCA uses sine and cosine references up to the third harmonic, as the models’ own CCA term does, and reached 65.38% dry and 81.65% wet; the dry-versus-wet page's CCA uses references up to the fourth harmonic on the same windows and reached 65.47% and 81.56%.
Wearable-102’s stored amplitude unit is undocumented and the correction reads amplitude spectra, so a unit effect is not separated from the change of dataset. Window onset, hardware, reference, people and frequency grid all change at once; no equivalence margin was tested, and no one-factor ablation or target adaptation was run, so no single cause is isolated. Dry and wet are the primary contrasts; both sensors together and the cropped-spectrum check are secondary, the check a diagnostic only. This is a different task on other recordings, so these scores do not compare with BETA’s held-out or seen tasks. Wearable-102 also appears on other pages of this site, and its documentation cannot rule out that some people also appear in BETA.
Credit Zhu, F., Jiang, L., Dong, G., Gao, X., & Wang, Y. (2021). An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces (Version 4) [Data set]. Figshare. https://doi.org/10.6084/m9.figshare.13560281.v4
CC BY 4.0 · Dataset record ↗ · doi.org ↗
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
BCI Report (2026). Wearable SSVEP BCI dataset (dry and wet electrodes): EEG decoding results. https://bci.report/datasets/wearable-ssvep-102/
Figures from releases new-ssvep-frequencies-update-20261020 (2026-10-20), deployment-topics-20260920-v1 (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 ↗