# Does EEG decoding still work while walking or running?

Motion robustness · scalp and ear EEG

Performance declines as participants move.

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

Measured on: [Mobile BCI dataset (SSVEP and ERP paradigms)](https://bci.report/datasets/mobile-bci/); [Phantom EEG with motion, muscle and eye artifacts](https://bci.report/datasets/phantom/); [Multimodal gait dataset · treadmill walking](https://bci.report/datasets/gait-eeg/) · Methods: [LaBraM](https://bci.report/methods/labram/); [CBraMod](https://bci.report/methods/cbramod/); [CCA](https://bci.report/methods/cca/)

## Short answer

Less well. With the same **23** people on a three-target, two-second SSVEP task (eight selected scalp channels; chance **33.3%**), the training-free author-style CCA reference scored **82.3%** balanced accuracy standing, **70.7%** at a slow walk and **68.6%** at a fast walk. On an ERP task, with the same **17** people in both conditions, a decoder fit on each person's first standing session and then used unchanged saw its scalp ROC AUC change by **−0.276 AUC** from standing to running (paired 95% interval **−0.322** to **−0.227 AUC**). Both tasks are derivatives of one original mobile study.

**Author-style CCA, the same 23 people standing and walking.** Participant-mean balanced accuracy of the training-free reference on three-target, two-second SSVEP with eight selected scalp channels. Dot: the estimate; line: participant bootstrap 95% interval; dashed line: chance, 33.3%.

- Standing: 82.3% (74.6%–89.4%)
- Slow walk · 0.8 m/s: 70.7% (62.3%–78.6%)
- Fast walk · 1.6 m/s: 68.6% (60.1%–76.7%)

Matched operating point

## Two seconds, eight selected scalp channels

All four methods use the window [0, 2 s) and the same eight posterior scalp channels, and are evaluated on 23 people with three SSVEP targets.

Participant-mean balanced accuracy; participant bootstrap 95% intervals are shown below each score.

| Method | Standing | Slow walk · 0.8 m/s | Fast walk · 1.6 m/s |
| --- | --- | --- | --- |
| Author-style CCA — analytic reference | 82.3% (74.6%–89.4%) | 70.7% (62.3%–78.6%) | 68.6% (60.1%–76.7%) |
| Spectral ridge — trained on the other 22 standing participants | 78.7% (70.7%–86.1%) | 69.9% (61.2%–78.1%) | 68.6% (60.4%–76.3%) |
| LaBraM — frozen encoder + ridge, trained on other participants | 59.5% (53.6%–65.5%) | 54.7% (49.5%–60.1%) | 47.8% (43.3%–52.3%) |
| CBraMod — frozen encoder + ridge, trained on other participants | 59.9% (54.3%–65.4%) | 46.2% (41.1%–51.4%) | 46.0% (42.2%–49.6%) |

Bars span 0–100%; chance is 33.3%.

LaBraM uses verified standard channel IDs; CBraMod uses an exploratory positional channel order. Record-level pretraining exposure is not certified.

A separate operating point

## Five-second scalp and ear comparisons

Here the two classical methods use a five-second window instead of two.

Participant-mean balanced accuracy · 23 people · 3 targets. Scalp uses 8 selected channels from 32 recorded; ear uses 14 contacts.

| Method / modality | Standing | Slow walk · 0.8 m/s | Fast walk · 1.6 m/s |
| --- | --- | --- | --- |
| Author-style CCA — 8 selected scalp channels · 5 s | 88.7% (80.0%–95.9%) | 82.9% (74.3%–90.4%) | 80.9% (72.0%–88.8%) |
| Spectral ridge — 8 selected scalp channels · 5 s | 85.0% (77.0%–92.0%) | 79.2% (70.1%–87.5%) | 77.2% (67.7%–85.7%) |
| Author-style CCA — 14 ear contacts · 5 s | 53.1% (47.4%–59.6%) | 43.4% (39.6%–47.8%) | 39.1% (36.7%–41.8%) |
| Spectral ridge — 14 ear contacts · 5 s | 52.0% (46.9%–57.8%) | 45.4% (40.7%–51.2%) | 40.0% (36.4%–43.7%) |

ERP session transfer

## AUC falls toward chance while moving

For each participant and modality, a temporal-feature L2 logistic regression is fit on the first standing session and then used unchanged. The endpoint is ROC AUC.

**−0.276 AUC** Scalp ROC AUC change: standing to running, same 17 people

### The running contrast is paired on the same people

Running has 17 participants, standing and walking 24. Among the same 17 people, the standing-to-running change is −0.276 AUC for scalp (paired 95% interval −0.322 to −0.227 AUC) and −0.225 AUC for ear (−0.277 to −0.175 AUC).

Participant-mean ROC AUC. Scalp uses 32 channels; ear uses 14 contacts. Values in parentheses are participant bootstrap 95% intervals.

| Modality | Standing | Slow walk · 0.8 m/s | Fast walk · 1.6 m/s | Running · 2.0 m/s |
| --- | --- | --- | --- | --- |
| Scalp · 32 channels — standing-session fit · unchanged weights | 0.859 (0.820–0.897) — n=24 | 0.740 (0.703–0.777) — n=24 | 0.635 (0.602–0.673) — n=24 | 0.586 (0.561–0.613) — n=17 |
| Ear · 14 contacts — standing-session fit · unchanged weights | 0.734 (0.694–0.774) — n=24 | 0.628 (0.595–0.661) — n=24 | 0.563 (0.540–0.587) — n=24 | 0.517 (0.491–0.541) — n=17 |

Engineering control · physical phantom

## Artifacts, measured without people

A conductive head phantom broadcast ten known brain sources while artifacts were added one kind at a time. A linear decoder trained on the clean recording was tested on held-out time blocks of every condition. One phantom is one experimental unit.

**Neither column is accuracy.** Correlation asks whether the recovered source moves with the true one. Predictive R² also scores amplitude and has no floor: below zero, the prediction is further from the true source than a flat line would be.

Median across five folds of the within-fold median across ten brain sources. The same 60-second blocks are held out in every condition, with a two-second purge on each side of a training block. Trained on the brain-only recording.

| Condition | Signed correlation r | Predictive R² |
| --- | --- | --- |
| Brain sources only — training condition, held-out time | 0.761 (fold range 0.755 to 0.772) | 0.567 (fold range 0.549 to 0.594) |
| Eye artifacts | 0.492 (fold range 0.432 to 0.569) | −33.612 (fold range −75.205 to −23.971) |
| Walking | 0.549 (fold range 0.542 to 0.577) | −795.772 (fold range −935.028 to −425.205) |
| Neck muscle | 0.310 (fold range 0.249 to 0.317) | −596.820 (fold range −1106.382 to −281.754) |
| Facial muscle | 0.143 (fold range 0.127 to 0.155) | −164.313 (fold range −270.004 to −110.197) |
| All artifacts together | 0.115 (fold range 0.104 to 0.131) | −142.832 (fold range −172.296 to −100.142) |

### Phantom EEG with motion, muscle and eye artifacts

Ryan J. Downey and Daniel P. Ferris · Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts, OpenNeuro ds004784 v1.0.4, doi:10.18112/openneuro.ds004784.v1.0.4. Method: doi:10.3390/s23198214.

One electrically conductive head phantom; no human participants.

[OpenNeuro dataset ↗](https://doi.org/10.18112/openneuro.ds004784.v1.0.4) · [Method paper ↗](https://doi.org/10.3390/s23198214) · [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/)

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

Treadmill speed · dry electrodes

## What a walking-speed score may be measuring

58 people walked on a treadmill at three speeds while a dry-electrode headset recorded 19 scalp channels. A fixed classifier guessed the speed from each whole 58-second recording. A second classifier saw only three movement-nuisance features: amplitude, low-frequency and line-frequency power. Chance is 33.3%.

**+5.2 pp** Nuisance features minus spectral bands, same 58 people

### Nuisance features alone score in the same range as the spectral bands

50.6% against 45.4%, with overlapping intervals (44.3%–56.9% and 39.1%–51.7%). A walking-speed score can be reached without any spectral pattern, so a score like this alone is not evidence of decoding the brain.

Participant-held-out balanced accuracy over five folds, with its participant-bootstrap 95% interval, and macro F1. Every person contributes all three speeds.

| Input | Balanced accuracy | Macro F1 |
| --- | --- | --- |
| Relative spectral bands — five relative bands on each of 19 scalp channels | 45.4% (39.1%–51.7%) | 45.5% |
| Movement-nuisance features — comparator · three features, no spectral pattern | 50.6% (44.3%–56.9%) | 48.6% |

### No motion sensors in the model

The source also recorded inertial and muscle signals; they were not used as controls. Speed order was not verified to be counterbalanced, so fatigue, drift and electrode contact may change with it.

### 1 person excluded before scoring

One source file has malformed channel labels, so that person's 3 recordings were excluded; no channel was relabelled.

### Multimodal gait · EEG during treadmill walking at three speeds

R. Katmah, A. AlShehhi, D. Kosaji, N. Al-Rahmani, M. Abdullah, A. A. V. Hulleck and K. Khalaf · A multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults, PhysioNet v1.0.0 (2026), doi:10.13026/r0ea-7161.

The study followed the Declaration of Helsinki and was approved by the Institutional Review Board of Khalifa University (H19-038); all participants gave written informed consent. Only cohort aggregates appear here.

[PhysioNet dataset ↗](https://doi.org/10.13026/r0ea-7161) · [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

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

Methods & limits

## Limits

### Scalp 8 is a selection

The mobile SSVEP scalp view selects eight posterior channels from 32 recorded; the dry-versus-wet page uses a native eight-channel headband.

### Balanced accuracy differs from AUC

The SSVEP endpoint is participant-mean balanced accuracy for a three-class task. The ERP endpoint is participant-mean ROC AUC. The two values are on different scales.

### The derivatives share a study

NEMAR nm000125 and nm000201 are SSVEP and ERP derivatives of the same original mobile BCI study, with different assessed sample counts.

### Lee2021 – SSVEP paradigm of the Mobile BCI dataset

Lee, Y., Shin, G., Lee, M., & Lee, S. (2026). Lee2021 – SSVEP paradigm of the Mobile BCI dataset (Version v1.0.2) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000125

Lee, Y.-E., Shin, G.-H., Lee, M., & Lee, S.-W. (2021). Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running. Scientific Data, 8, 315. https://doi.org/10.1038/s41597-021-01094-4

NEMAR/MOABB-generated BIDS derivative · CC BY 4.0.

[SSVEP dataset ↗](https://nemar.org/dataset/nm000125) · [Source study ↗](https://doi.org/10.1038/s41597-021-01094-4)

### ERP paradigm of the Mobile BCI dataset

Lee, Y., Shin, G., Lee, M., & Lee, S. (2026). ERP paradigm of the Mobile BCI dataset (Version v1.0.2) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000201

Lee, Y.-E., Shin, G.-H., Lee, M., & Lee, S.-W. (2021). Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running. Scientific Data, 8, 315. https://doi.org/10.1038/s41597-021-01094-4

NEMAR/MOABB-generated BIDS derivative · CC BY 4.0.

[ERP dataset ↗](https://nemar.org/dataset/nm000201) · [Source study ↗](https://doi.org/10.1038/s41597-021-01094-4)

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). *Does EEG decoding still work while walking or running?* https://bci.report/topics/on-the-move/

Figures from releases [`context-update-20260927`](https://bci.report/releases/#context-update-20260927) (2026-09-27), [`evidence-update-20260922`](https://bci.report/releases/#evidence-update-20260922) (2026-09-22), [`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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