Keywords

"EEG", "metabolic cost", "deep learning", "Convolution neural network", "cost of transport"

Abstract

Electroencephalography (EEG) is a technique used to non-invasively record electrical signals from the brain that can be used to understand how the brain functions and controls movements. EEG, however, is susceptible to recording electrical artifact signals that do not originate from brain activity. Common artifacts that come up in EEG data are generated by eye movements, heart contractions, muscle contractions, motion artifacts, electromagnetic interference signals, and powerline noise. These artifacts are often filtered out of EEG data or reduced to clean the signals and preserve the information from the brain. However, some of these signals may provide useful information about a person’s metabolic cost or the amount of energy required to perform a task. To measure metabolic cost, individuals breathe into a mask or mouthpiece attached to a metabolic analyzer in a process called indirect calorimetry. Metabolic cost is a common measure for evaluating the effectiveness of rehabilitation assistive devices. As such, several studies have used wearable sensors such as inertial measurement units (IMUs), electromyography (EMG), and heart rate monitors to estimate metabolic cost. The purpose of this study was to explore using EEG and its artifacts to predict the metabolic cost of individuals while they walked on a treadmill at a range of fixed speeds and a self-paced speed. We used a dual-layer EEG system that simultaneously records normal EEG on the scalp and electrical signals that are not generated from the body but rather by motion or from the environment. We recorded dual-layer EEG and metabolic energy as three young adults walked on a treadmill at a range of speeds. We then trained a deep learning convolution neural network (CNN) model with two subsets of data (just the motion artifact signals, and the minimally processed traditional EEG signals recorded on the scalp) data to predict metabolic cost to determine which subset of data had more information. Our preliminary results suggest that there is potential for using EEG to predict metabolic cost.

Completion Date

2024

Semester

Fall

Committee Chair

Helen Huang

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Biomedical Engineering

Format

PDF

Identifier

DP0028991

Language

English

Release Date

12-15-2024

Access Status

Thesis

Campus Location

Orlando (Main) Campus

Accessibility Status

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