A multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults

Gait is a fundamental motor function, and its analysis is essential for
understanding locomotor control, rehabilitation, and the early detection of
neurological and musculoskeletal disorders. While many datasets capture either
biomechanical or neural aspects of gait, publicly available multimodal
datasets that integrate brain, muscle, kinematic, and ground reaction force
recordings remain scarce. This limitation restricts advances in modeling
neuromechanical interactions and the development of machine learning
approaches for gait classification and rehabilitation technologies. To address
this gap, we provide a comprehensive dataset of treadmill walking from 59
healthy adults with a mean age of 24 ± 5 years, representing both sexes and
different body mass index categories. Participants walked at three controlled
speeds (0.5, 0.75, and 1.0 m/s), with synchronized recordings from scalp
electroencephalography, surface electromyography of 12 lower-limb muscles,
inertial sensors capturing kinematics, and bilateral force plates measuring
three-dimensional forces, moments, and center of pressure. The dataset enables
investigations into brain-body interactions, speed-dependent adaptations, and
neuromechanical variability, while supporting the benchmarking of
computational models for gait analysis.