Neuroergonomics, the application of neuroscience to human factors and ergonomics, is an emerging science focusing on the human brain concerning performance at work and in everyday settings. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different physical exertion activity levels that involve the musculoskeletal system in everyday settings remain poorly understood. Furthermore, the assessment of functional connectivity among different brain regions during different force exertion levels remains unclear. One approach to investigating the brain connectome is to model the underlying mechanism of the brain as a complex network. This study applied employed a graph-theoretical approach to characterize the topological properties of the functional brain network induced by predefined force exertion levels, namely extremely light (EL), light (L), somewhat hard (SWH), hard (H), and extremely hard (EH) in two frequency bands, i.e., alpha and beta. Twelve female participants performed an isometric force exertion task and rated their perception of physical comfort at different physical exertion levels. A CGX-Mobile-64 EEG was used for recording spontaneous brain electrical activity. After preprocessing the EEG data, a source localization method was applied to study the functional brain connectivity at the source level. Subsequently, the alpha and beta networks were constructed by calculating the coherence between all pairs of 84 brain regions of interests that were selected using Brodmann Areas. Graph -theoretical measures were then employed to quantify the topological properties of the functional brain networks at different levels of force exertions at each frequency band. During an 'extremely hard' exertion level, a small-world network was observed for the alpha coherence network, whereas an ordered network was observed for the beta coherence network. The results suggest that high-level force exertions are associated with brain networks characterized by a more significant clustering coefficient, more global and local efficiency, and shorter characteristic path length under alpha coherence. The above suggests that brain regions are communicating and cooperating to a more considerable degree when the muscle force exertions increase to meet physically challenging tasks. The exploration of the present study extends the current understanding of the neurophysiological basis of physical efforts with different force levels of human physical exertion to reduce work-related musculoskeletal disorders.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Ismail, Lina, "Topological Changes in the Functional Brain Networks Induced by Isometric Force Exertions Using a Graph Theoretical Approach: An EEG-based Neuroergonomics Study" (2021). Electronic Theses and Dissertations, 2020-. 956.
Restricted to the UCF community until November 2021; it will then be open access.