Semester of Gradation
Fall 2025
Degree Type
Dissertation/Thesis
Degree Name
Doctor of Philosophy in Interdisciplinary Engineering
Department
Department of Electrical and Computer Engineering
Committee Chair/First Advisor
Sylvia Bhattacharya
Second Advisor
Hoseon Lee
Third Advisor
Sumit Chakravarty
Fourth Advisor
Awatef Ergai
Abstract
Brain–Computer Interfaces (BCIs), particularly those based on Motor Imagery (MI), still require extensive subject-specific training, mainly because EEG signal characteristics vary widely across individuals. This dissertation investigates whether resting-state EEG contains subject-dependent information that can reduce, or even replace, the need for per-user training for building MI decoding models. The central premise throughout this work is that the brain’s resting activity can serve as a neural signature that influences how MI activity will later manifest.
The dissertation first establishes that resting-state spectral power, specifically in the Alpha band, relates systematically to how strongly the hemisphere experiencing this power activates during motor imagery. Based on this relationship, subjects were grouped into three rest-based neurophysiological profiles rather than treated as a single heterogeneous population. Across multiple datasets and model configurations, the clustering strategy consistently improved cross-subject decoding, as evidenced by better performance within clusters than in mixed clusters. This demonstrated clear potential for plug-and-play MI-BCIs.
Overall, this dissertation reframes resting-state EEG as a functional and predictive signal source, showing that “rest” contains actionable structure for decoding, model transfer, and subject grouping. The work moves toward the long-standing goal of reducing training overhead in MI-BCIs and introduces a biologically grounded alternative to purely mathematical transfer-learning approaches.
Comments
The table in Appendix C is part of my previously published work: In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Kennesaw State University’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation.