# Do dry EEG electrodes decode as well as wet ones?

Sensor transfer · 102 participants

Balanced accuracy falls most when an EEGNet trained on wet recordings is tested on dry recordings from held-out people.

- [Reviewed aggregate JSON ↓](https://bci.report/data/deployment-topics.json)
- 

Measured on: [Wearable SSVEP BCI dataset (dry and wet electrodes)](https://bci.report/datasets/wearable-ssvep-102/) · Methods: [EEGNet](https://bci.report/methods/eegnet/); [CCA](https://bci.report/methods/cca/); [FBCCA](https://bci.report/methods/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.

**Wet and dry recordings of the same 102 people.** Participant-mean balanced accuracy on 12-target, two-second SSVEP. Dot: the estimate; line: participant bootstrap 95% interval; dashed line: chance, 1 in 12. Dark rows are wet recordings, rust rows dry.

- FBCCA · wet recording — training-free: 82.3% (78.7%–85.7%)
- FBCCA · dry recording — training-free: 64.6% (59.5%–69.6%)
- EEGNet · wet → wet — trained on the first sensor, tested on the second: 75.0% (70.3%–79.5%)
- EEGNet · wet → dry — trained on the first sensor, tested on the second: 45.6% (40.5%–50.8%)
- EEGNet · dry → dry — trained on the first sensor, tested on the second: 56.8% (51.9%–61.6%)
- EEGNet · dry → wet — trained on the first sensor, tested on the second: 64.0% (59.1%–68.8%)

**−29.4 pp** Wet-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.

Native 8-channel headband · 102 participants · 12,240 trials per target sensor · participant bootstrap 95% intervals.

| Model and training source | Test on wet | Test on dry |
| --- | --- | --- |
| Spectral ridge — trained on wet · participant-disjoint | 40.3% (36.5%–44.3%) | 35.2% (31.8%–38.8%) |
| Spectral ridge — trained on dry · participant-disjoint | 39.7% (35.9%–43.6%) | 37.2% (33.5%–41.0%) |
| EEGNet — trained on wet · participant-disjoint | 75.0% (70.3%–79.5%) | 45.6% (40.5%–50.8%) |
| EEGNet — trained on dry · participant-disjoint | 64.0% (59.1%–68.8%) | 56.8% (51.9%–61.6%) |

Wet-trained EEGNet

75.0% → 45.6%

Dry-trained EEGNet

56.8% → 64.0%

Testing 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%

FBCCA reads each target recording analytically, with no training.

Analytic references

## CCA, FBCCA and a power reference

Training-free: each scores the target recording directly.

Target-recording references on the same two-second, 12-target protocol.

| Reference | Wet recording | Dry recording |
| --- | --- | --- |
| Same-frequency power — analytic or fixed reference | 39.8% · 95% interval 35.4%–44.3% | 36.0% · 95% interval 31.8%–40.2% |
| CCA — analytic or fixed reference | 81.6% · 95% interval 77.7%–85.1% | 65.5% · 95% interval 60.6%–70.2% |
| FBCCA — analytic 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 ↗](https://figshare.com/articles/dataset/An_Open_Dataset_for_Wearable_SSVEP-Based_Brain-Computer_Interfaces/13560281) · [Sensors paper ↗](https://doi.org/10.3390/s21041256) · CC BY 4.0

Data source: [reviewed aggregate JSON](https://bci.report/data/deployment-topics.json) · schema bci-report-public-deployment-topics-v1.

Keep exploring

Transfer

- [— Sensor transfer **Dry vs. wet electrodes**](https://bci.report/topics/dry-vs-wet/)
- [— Context transfer **Screen to VR**](https://bci.report/topics/screen-to-vr/)
- [— Montage **Fewer electrodes**](https://bci.report/topics/fewer-electrodes/)
- [— Motion robustness **On the move**](https://bci.report/topics/on-the-move/)
- [— Session transfer · same person **Later sessions**](https://bci.report/topics/later-sessions/)

Adapting models

- [— Calibration budget **How much calibration?**](https://bci.report/topics/calibration-budget/)
- [— Model adaptation **Which part to update?**](https://bci.report/topics/model-adaptation/)
- [— Representation controls **Does pretraining help?**](https://bci.report/topics/does-pretraining-help/)
- [— Shared encoder · route 2 **One model, several questions**](https://bci.report/topics/shared-encoder/)
- [— Questions in language · route 3 **Questions in language**](https://bci.report/topics/questions-in-language/)
- [— Route 3 follow-ups **New SSVEP frequencies**](https://bci.report/topics/new-ssvep-frequencies/)

Reliability & clinical

- [— Abstention · decision research **When not to act**](https://bci.report/topics/when-not-to-act/)
- [— Sleep staging · simple baselines **Sleep-stage balance**](https://bci.report/topics/sleep-staging/)
- [— Clinical research **Clinical groups**](https://bci.report/topics/clinical-groups/)
- [— Data quality and reproducibility **Checked end to end**](https://bci.report/topics/data-quality/)

Other signals & pilots

- [— Other signals · human and macaque **Beyond scalp EEG**](https://bci.report/topics/beyond-scalp-eeg/)
- [— Pilot studies · small cohorts **Pilot studies**](https://bci.report/topics/pilot-studies/)

[All questions and the transfer-coverage map →](https://bci.report/topics/)

## 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`](https://bci.report/releases/#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](https://doi.org/10.5281/zenodo.23123296). [BibTeX for the site and its releases →](https://bci.report/api/#cite-heading) · [CITATION.cff ↗](https://raw.githubusercontent.com/twu3202/bci-report/main/CITATION.cff)

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