Treadmill-walking dataset from 59 healthy young adults at Khalifa University, each walking one minute at 0.5, 0.75 and 1.0 m/s, with synchronized 19-channel dry-electrode EEG (DSI-24), surface EMG from 12 lower-limb muscles, 17-sensor IMU kinematics and split-belt force plates. Released on PhysioNet as v1.0.0 (CC BY 4.0, April 2026) by Katmah et al.
Also known asA multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults · PhysioNet multimodal-gait-dataset · doi:10.13026/r0ea-7161
Movement-nuisance features · Movement-nuisance features, not brain signal
50.6% (44.3%–56.9%)
0%25%50%75%100%
Balanced accuracy, 2 configurations. Dot: the estimate; line: 95% interval; dashed line: chance level where the payload records one.
Method
Condition
Metric
Value
People
Relative spectral bands
Relative spectral bands, 19 scalp channels
Balanced accuracy
45.4%39.1%–51.7%
58
Movement-nuisance featuresreference or comparator, not a decoding model
Movement-nuisance features, not brain signal
Balanced accuracy
50.6%44.3%–56.9%
58
Source and licence
Multimodal gait dataset · treadmill walking
CreditR. 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.
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data belong to the people credited.