Abstract

Mobile brain-body imaging (MoBI) seeks to understand human brain and body dynamics during movement and locomotor tasks such as walking with perturbations that challenge balance and lead to adaptation of walking behavior. In this dissertation, I evaluated the long-term electromyography (EMG) recording performance of dry epidermal electrodes for measuring electrical muscle activity. I also evaluated the relationships between the signals recorded from the two sides of dual-sided electroencephalography (EEG) electrodes, a recent advancement in EEG electrode design for measuring electrical brain activity. Last, I investigated adaptation of brain and body responses to small and frequent perturbations during treadmill walking while I recorded brain activity using a custom-built dual-layer EEG system and body kinematics using motion capture. Dry epidermal electrodes provided better Signal Quality Indices, a metric I developed that accounts for signal-to-noise and signal-to-motion contributions, during limited dynamic movements, indicating that high-quality EMG for long-term recording was possible but also limited. For the dual-sided EEG electrode evaluation, I quantified correlations between dual-sided EEG signals in a benchtop experiment. Signals recorded from two sides of a dual-sided EEG electrode were highly correlated during constrained movements but degraded in more realistic random movements. This information is critical for developing EEG cleaning algorithms based on dual-layer EEG systems. For the locomotor adaptation studies, I quantified gait stability using margin of stability and its components and performed source localization and time-frequency analyses to determine electrocortical processes during perturbed walking. Small and frequent treadmill perturbations disrupted gait stability and quickly induced direction-dependent gait stability adaptation. Anterior cingulate theta-band adaptation occurred and was more evident during belt deceleration perturbations compared to belt acceleration perturbations. These results add new knowledge about the characteristics of novel EMG and EEG electrodes and revealed the potential of modulating perturbation direction to tune gait stability strategy and activation of electrocortical dynamics.

Notes

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Graduation Date

2022

Semester

Fall

Advisor

Huang, Helen

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Biomedical Engineering

Format

application/pdf

Identifier

CFE0009375; DP0027098

URL

https://purls.library.ucf.edu/go/DP0027098

Language

English

Release Date

December 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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