Theses and Dissertations

ORCID

https://orcid.org/0000-0002-1965-224X

Issuing Body

Mississippi State University

Advisor

Vandenheever, David

Committee Member

Chander, Harish

Committee Member

To, Filip S. D.

Committee Member

Burch, Reuben F.

Committee Member

Sharma, Sameer

Date of Degree

12-8-2023

Original embargo terms

Campus Access Only 2 Years

Document Type

Dissertation - Campus Access Only

Major

Biomedical Engineering

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Agricultural and Biological Engineering

Abstract

According to various studies, compared with novice athletes, experts exhibit superior integration of perceptual, cognitive, and motor skills. This superior ability has been associated with the focused and efficient organization of task-related neural networks. Specifically, skilled individuals demonstrate a spatially localized or relatively lower response in brain activity, characterized as ‘neural efficiency’, when performing within their domain of expertise. Previous works also suggested that elite basketball players can predict successful free throws more rapidly and accurately based on cues from body kinematics. These traits are the result of a prolonged training of specific motor skills and focused excitability of the motor cortex during the reaction, movement planning, and execution phases. However, due to motion artifacts occurring during movement initiation and execution, the knowledge about the underlying mechanism and the connection between brain neural networks and body musculoskeletal systems is still limited. Thus, the objective of this study is to utilize electroencephalography (EEG) and motion capture systems (MoCap) to advance and expand the current understanding of the relationships between neurophysiological activities and human biomechanics as well as their effects on the success rate of the motor skills.

The project focuses on fulfilling three specific aims. The first aim focused on the integration of the EEG and the MoCap systems to analyze and compare the successful and unsuccessful outcomes of basketball throws. Then, the second aim utilized Convolution Neural Networks (CNNs) as an alternative approach to predict the shot’s outcome based on the recorded EEG signals and biomechanical parameters. Lastly, the third aim identified the impact of each EEG channel and MoCap parameter on the CNN models using ablation methods. The results obtained from this study can be a practical approach in analyzing the sources that lead to better elite athletes’ performance in various sport-related tasks. Moreover, the acquired data can contribute to a deeper understanding of the vital biomechanical and neurological factors that directly affect the performance of elite athletes during successful outcomes, thus, providing vital information for the overall improvement of athletic performance and guidance for sport-specific training needs.

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