Human electrocortical, electromyographical, ocular, and kinematic data during perturbed walking and standing
Steven M Peterson1, Daniel P Ferris2
1Department of Biology, University of Washington, Seattle 98195, USA.
Data in Brief
|January 6, 2022
Summary
This study introduces a new multi-modal dataset for balance control research, combining electroencephalography (EEG) and motion data. The dataset aids in understanding neural processing during dynamic balance tasks.
Area of Science:
- Neuroscience
- Biomechanics
- Human Motor Control
Background:
- Active balance control is essential for daily activities, relying on integrated sensory inputs for coordinated muscle actions.
- Recording cortical processing during mobile balance tasks is challenging due to neuroimaging limitations and motion artifacts.
Purpose of the Study:
- To present a synchronized, multi-modal dataset for studying neural activity during balance control.
- To facilitate research on sensorimotor integration and balance perturbations.
Main Methods:
- Collected high-density electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), and 3D body motion data.
- Recorded data from 30 healthy participants during standing and walking with sensorimotor perturbations.
- Included over 18,000 balance perturbation events.
Main Results:
- A comprehensive dataset comprising 20 hours of synchronized multi-modal recordings.
- Data is standardized using the Brain Imaging Data Structure (BIDS) format.
- Publicly released code for replicating event-related findings.
Conclusions:
- The dataset provides a valuable resource for investigating the neural basis of balance control.
- Standardized data and open-source code promote reproducibility and further research in sensorimotor neuroscience.
- Enables advanced analysis of cortical processing during dynamic balance challenges.
