Human electrocortical, electromyographical, ocular, and kinematic data during perturbed walking and standing

Abstract

Active balance control is critical for performing many of our everyday activities. Our nervous systems rely on multiple sensory inputs to inform cortical processing, leading to coordinated muscle actions that maintain balance. However, such cortical processing can be challenging to record during mobile balance tasks due to limitations in noninvasive neuroimaging and motion artifact contamination. Here, we present a synchronized, multi-modal dataset from 30 healthy, young human participants during standing and walking while undergoing brief sensorimotor perturbations. Our dataset includes 20 total hours of high-density electroencephalography (EEG) recorded from 128 scalp electrodes, along with surface electromyography (EMG) from 10 neck and leg electrodes, electrooculography (EOG) recorded from 3 electrodes, and 3D body position from 2 sensors. In addition, we include ∼18000 total balance perturbation events across participants. To facilitate data reuse, we share this dataset in the Brain Imaging Data Structure (BIDS) data standard and publicly release code that replicates our previous event-related findings.

Keywords

• Mobile brain/body imaging
• Electroencephalography
• Electromyography
• Electrooculography
• Motion capture
• Independent component analysis