Bayesian Decoder Design for Investigation of Cognitive Arousal and Performance Using Physiological and Behavioral Data
| dc.contributor.advisor | Faghih, Rose T. | |
| dc.contributor.committeeMember | Contreras-Vidal, Jose L. | |
| dc.contributor.committeeMember | Grigoriadis, Karolos M. | |
| dc.creator | Khazaei, Saman | |
| dc.date.accessioned | 2022-06-16T22:45:07Z | |
| dc.date.created | December 2021 | |
| dc.date.issued | 2021-12 | |
| dc.date.submitted | December 2021 | |
| dc.date.updated | 2022-06-16T22:45:08Z | |
| dc.description.abstract | Human arousal is one of the indicators of internal stress that has effects on productivity and performance. Poor stress management may lead to reduced performance. Hence, decoding the unobserved arousal and performance, and identifying the arousal-performance relationship are challenging topics of study. On the other hand, cognitive performance can be affected by several external factors such as working environment and surroundings. Decoding human cognitive performance during a cognitive task, based on the environmental variations, is an important topic of interest in the cognitive neuroengineering area. Our study has a great potential to transform workplaces and educational systems. In this study, using the state-space approach within an expectation-maximization (EM) framework, we first obtain the arousal and performance states separately. We investigate the feasibility of using the Yerkes–Dodson law from psychology to link arousal to cognitive performance. Thereafter, we develop a novel arousal decoder based on the corresponding performance. Next, in order to capture the external effects on performance, we design the performance state-space model such that it becomes adaptive to the environmental changes. We develop a time-varying state-space model by applying the autoregressive conditional heteroskedasticity (ARCH) framework to our problem. Subsequently, we present a particle filtering approach and track the human performance through a cognitive task under the EM scheme. Our findings manifest the background music effects on the obtained arousal and performance states. The arousal and performance relationship reveals the existence of Yerkes–Dodson law. The novel arousal decoder offers us a better agreement between the arousal-performance relationship and the Yerkes–Dodson law. Furthermore, our investigations indicate that applying the ARCH framework within the state-space model results in an improved performance state estimation and it outperforms the previous model in terms of capturing the environmental impacts on human performance. Our study can be implemented directly in designing non-invasive closed-loop systems, future smart workplaces, and online educational systems to regulate stress for maximizing performance and productivity. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Saman Khazaei, Md Rafiul Amin, and Rose T Faghih. "Decoding a neurofeedback-modulated cognitive arousal state to investigateperformance reg- ulation by the yerkes-dodson law," in 2021 43rd Annual International Confer- ence of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2021 | |
| dc.identifier.uri | https://hdl.handle.net/10657/9253 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Bayesian Decoder Design | |
| dc.subject | Cognitive science | |
| dc.title | Bayesian Decoder Design for Investigation of Cognitive Arousal and Performance Using Physiological and Behavioral Data | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2023-12-01 | |
| local.embargo.terms | 2023-12-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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