BCI ReportWet-trained EEGNet
75.0% → 45.6%
29.4 percentage-point lossSensor transfer · 102 participants
Do dry EEG electrodes decode as well as wet ones?
Balanced accuracy falls most when an EEGNet trained on wet recordings is tested on dry recordings from held-out people.
Measured on: Wearable SSVEP BCI dataset (dry and wet electrodes) · Methods: EEGNet; CCA; FBCCA
Short answer
Not in this dataset. On a 12-target, two-second SSVEP task, with the same 102 people recorded on both sensor types, the training-free FBCCA reference scored 82.3% on wet recordings and 64.6% on dry ones. An EEGNet trained on wet recordings fell by 29.4 pp of balanced accuracy when tested on dry recordings (paired 95% interval −34.5 pp to −24.3 pp). Wearing order and time on task change together with sensor type, so the hardware’s own share of the gap cannot be separated.
FBCCA · wet recordingtraining-free
82.3% (78.7%–85.7%)
FBCCA · dry recordingtraining-free
64.6% (59.5%–69.6%)
EEGNet · wet → wettrained on the first sensor, tested on the second
75.0% (70.3%–79.5%)
EEGNet · wet → drytrained on the first sensor, tested on the second
45.6% (40.5%–50.8%)
EEGNet · dry → drytrained on the first sensor, tested on the second
56.8% (51.9%–61.6%)
EEGNet · dry → wettrained on the first sensor, tested on the second
64.0% (59.1%–68.8%)
−29.4 ppWet-trained EEGNet: dry minus wet balanced accuracy
The largest observed cross-sensor loss
Across the same 102 people, the paired participant bootstrap 95% interval is −34.5 to −24.3 pp.
Source-trained models
Train on one sensor, test on both
Six folds hold out 17 people at a time. Both sensor recordings from each held-out person stay out of source training. Every cell is participant-mean balanced accuracy on 12-target, two-second SSVEP.
| Model and training source | Test on wet | Test on dry |
|---|---|---|
| Spectral ridgetrained on wet · participant-disjoint | 40.3%36.5%–44.3% | 35.2%31.8%–38.8% |
| Spectral ridgetrained on dry · participant-disjoint | 39.7%35.9%–43.6% | 37.2%33.5%–41.0% |
| EEGNettrained on wet · participant-disjoint | 75.0%70.3%–79.5% | 45.6%40.5%–50.8% |
| EEGNettrained on dry · participant-disjoint | 64.0%59.1%–68.8% | 56.8%51.9%–61.6% |
Dry-trained EEGNet
56.8% → 64.0%
7.3 percentage-point gainTesting the dry-trained model on wet recordings increased balanced accuracy by 7.3 points; 95% interval +4.2 to +10.4 pp.
A different kind of reference
82.3% / 64.6%
64.6% dry FBCCA balanced accuracyFBCCA reads each target recording analytically, with no training.
Analytic references
CCA, FBCCA and a power reference
Training-free: each scores the target recording directly.
| Reference | Wet recording | Dry recording |
|---|---|---|
| Same-frequency poweranalytic or fixed reference | 39.8% · 95% interval 35.4%–44.3% | 36.0% · 95% interval 31.8%–40.2% |
| CCAanalytic or fixed reference | 81.6% · 95% interval 77.7%–85.1% | 65.5% · 95% interval 60.6%–70.2% |
| FBCCAanalytic or fixed reference | 82.3% · 95% interval 78.7%–85.7% | 64.6% · 95% interval 59.5%–69.6% |
Methods & limits
Limits
Order remains a confound
Wearing order and time on task change with sensor type. Both order strata show the wet-trained drop.
Amplitude units are unresolved
The release documents no physical amplitude unit, so the scale-dependent foundation-model runs remain held.
One EEGNet seed
The source-trained EEGNet cells use one training seed. The paired intervals describe participant variation conditional on that fitted protocol.
An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces
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
Zhu, F., Jiang, L., Dong, G., Gao, X., & Wang, Y. (2021). An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces. Sensors, 21(4), 1256. https://doi.org/10.3390/s21041256
Aggregate results computed from the official Tsinghua BCI Lab author mirror snapshot acquired 2026-09-20.
Dataset record ↗ · Sensors paper ↗ · CC BY 4.0
Data source: reviewed aggregate JSON · schema bci-report-public-deployment-topics-v1.
Cite this page
BCI Report (2026). Do dry EEG electrodes decode as well as wet ones? https://bci.report/topics/dry-vs-wet/
Figures from release deployment-topics-20260920-v1 (2026-09-20). Cite the upstream datasets as well: their credits are on this page.
Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗