BCI ReportMotion robustness · scalp and ear EEG
Performance declines as participants move.
Measured on: Mobile BCI dataset (SSVEP and ERP paradigms); Phantom EEG with motion, muscle and eye artifacts; Multimodal gait dataset · treadmill walking · Methods: LaBraM; CBraMod; CCA
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.
Matched operating point
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.
| Method | Standing | Slow walk · 0.8 m/s | Fast walk · 1.6 m/s |
|---|---|---|---|
| Author-style CCAanalytic reference | 82.3%74.6%–89.4%82.3% balanced accuracy | 70.7%62.3%–78.6%70.7% balanced accuracy | 68.6%60.1%–76.7%68.6% balanced accuracy |
| Spectral ridgetrained on the other 22 standing participants | 78.7%70.7%–86.1%78.7% balanced accuracy | 69.9%61.2%–78.1%69.9% balanced accuracy | 68.6%60.4%–76.3%68.6% balanced accuracy |
| LaBraMfrozen encoder + ridge, trained on other participants | 59.5%53.6%–65.5%59.5% balanced accuracy | 54.7%49.5%–60.1%54.7% balanced accuracy | 47.8%43.3%–52.3%47.8% balanced accuracy |
| CBraModfrozen encoder + ridge, trained on other participants | 59.9%54.3%–65.4%59.9% balanced accuracy | 46.2%41.1%–51.4%46.2% balanced accuracy | 46.0%42.2%–49.6%46.0% balanced accuracy |
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
Here the two classical methods use a five-second window instead of two.
| Method / modality | Standing | Slow walk · 0.8 m/s | Fast walk · 1.6 m/s |
|---|---|---|---|
| Author-style CCA8 selected scalp channels · 5 s | 88.7%80.0%–95.9% | 82.9%74.3%–90.4% | 80.9%72.0%–88.8% |
| Spectral ridge8 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 CCA14 ear contacts · 5 s | 53.1%47.4%–59.6% | 43.4%39.6%–47.8% | 39.1%36.7%–41.8% |
| Spectral ridge14 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
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.
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).
| Modality | Standing | Slow walk · 0.8 m/s | Fast walk · 1.6 m/s | Running · 2.0 m/s |
|---|---|---|---|---|
| Scalp · 32 channelsstanding-session fit · unchanged weights | 0.8590.820–0.897n=24 | 0.7400.703–0.777n=24 | 0.6350.602–0.673n=24 | 0.5860.561–0.613n=17 |
| Ear · 14 contactsstanding-session fit · unchanged weights | 0.7340.694–0.774n=24 | 0.6280.595–0.661n=24 | 0.5630.540–0.587n=24 | 0.5170.491–0.541n=17 |
Engineering control · physical phantom
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.
| Condition | Signed correlation r | Predictive R² |
|---|---|---|
| Brain sources onlytraining condition, held-out time | 0.761fold range 0.755 to 0.772 | 0.567fold range 0.549 to 0.594 |
| Eye artifacts | 0.492fold range 0.432 to 0.569 | −33.612fold range −75.205 to −23.971 |
| Walking | 0.549fold range 0.542 to 0.577 | −795.772fold range −935.028 to −425.205 |
| Neck muscle | 0.310fold range 0.249 to 0.317 | −596.820fold range −1106.382 to −281.754 |
| Facial muscle | 0.143fold range 0.127 to 0.155 | −164.313fold range −270.004 to −110.197 |
| All artifacts together | 0.115fold range 0.104 to 0.131 | −142.832fold range −172.296 to −100.142 |
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.
Data source: reviewed aggregate JSON · schema bci-report-evidence-update-v1.
Treadmill speed · dry electrodes
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%.
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.
| Input | Balanced accuracy | Macro F1 |
|---|---|---|
| Relative spectral bandsfive relative bands on each of 19 scalp channels | 45.4%39.1%–51.7%45.4% balanced accuracy | 45.5% |
| Movement-nuisance featurescomparator · three features, no spectral pattern | 50.6%44.3%–56.9%50.6% balanced accuracy | 48.6% |
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.
One source file has malformed channel labels, so that person's 3 recordings were excluded; no channel was relabelled.
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.
Data source: reviewed aggregate JSON · schema bci-report-context-update-v1.
Methods & limits
The mobile SSVEP scalp view selects eight posterior channels from 32 recorded; the dry-versus-wet page uses a native eight-channel headband.
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.
NEMAR nm000125 and nm000201 are SSVEP and ERP derivatives of the same original mobile BCI study, with different assessed sample counts.
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.
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.
Data source: reviewed aggregate JSON · schema bci-report-public-deployment-topics-v1.
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 (2026-09-27), evidence-update-20260922 (2026-09-22), 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 ↗