Electrocortical activity distinguishes between uphill and level walking in humans
J Cortney Bradford1, Jamie R Lukos2, Daniel P Ferris3
1Translational Neuroscience Branch, US Army Research Laboratory, Aberdeen Proving Ground, Maryland; School of Kinesiology, University of Michigan, Ann Arbor, Michigan jcbrad@umich.edu.
Journal of Neurophysiology
|December 20, 2015
Summary
Walking on an incline engages different brain activity than level walking, particularly in sensorimotor and attention areas. These findings suggest brain control adjustments for varied terrain.
Area of Science:
- Neuroscience
- Biomechanics
- Human Locomotion
Background:
- Understanding the neural control of locomotion is crucial for rehabilitation and assistive technologies.
- Electrocortical activity patterns during walking can reveal brain adaptations to different surface inclines.
Purpose of the Study:
- To investigate differences in electroencephalography (EEG) activity between walking on an incline versus a level surface.
- To identify specific brain regions and frequency bands involved in adapting gait to inclines.
Main Methods:
- Subjects underwent EEG recording while walking on a treadmill at 0% and 15% grades.
- Independent Component (IC) analysis was used to isolate and analyze cortical sources from EEG data.
- Event-related spectral perturbations were analyzed in relation to gait cycle events.
Main Results:
- Theta power was significantly higher during incline walking in anterior cingulate, sensorimotor, and posterior parietal regions.
- Gamma power was greater during level walking in left sensorimotor and anterior cingulate areas.
- Distinct alpha and beta power fluctuations indicated cortical lateralization in sensorimotor areas for both conditions.
Conclusions:
- Locomotor adjustments for incline walking require supraspinal input, especially from the left sensorimotor cortex, anterior cingulate, and posterior parietal areas.
- EEG can differentiate neural activity between incline and level walking, supporting its use in brain-computer interfaces.
- Findings pave the way for EEG-based control signals in ambulatory brain-computer interface technologies.


