Evaluation and algorithmic adaptation of brain state control through audio entertainment
| dc.contributor.author | Cassim, Muhammed Rashaad | |
| dc.contributor.co-supervisor | Rubin, David | |
| dc.contributor.supervisor | Pantanowitz, Adam | |
| dc.date.accessioned | 2024-07-12T14:56:18Z | |
| dc.date.available | 2024-07-12T14:56:18Z | |
| dc.date.issued | 2023-12 | |
| dc.description | A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, in the School of Electrical and Information Engineering, in 2023. | |
| dc.description.abstract | This dissertation presents the design and evaluation of a system that can alter the dominant brain state of participants through audio entrainment. The ‘rch broadly aimed to identify the possible improvements of a dynamic entrainment stimulus when compared to a set entrainment stimulus. The dynamic entrainment stimulus was controlled by a Q-Learning (QL) model. The experiment sought to build on previous research by implementing existing entrainment methods in Virtual Reality and dynamically optimising the entrainment stimulus. The neurological effects of the stimuli were evaluated by analysing electroencephalogram measurements. It was found that a set 24 Hz entrainment stimulus increased the power of Beta band brain waves relative to a control condition. Further, contrary to existing research, it was found that the entrainment stimulus did not have a notable effect on brainwave connectivity at the entrainment frequency. The study subsequently evaluated if the QL agent could learn to optimise the entrainment stimulus. The agent was allowed to switch between an 18 and 24 Hz entrainment stimulus and succeeded in learning an optimised policy. The QL driven stimulus yielded results that generally exhibited the same characteristics as the set entrainment stimulus when using power and connectivity analysis methods. Furthermore, the power analysis indicated that the QL driven stimulus was able to affect a broader range of frequencies within the targeted band. The QL driven stimulus, additionally, resulted in higher meta-analysis metric values in some aspects. These factors indicate that it was able to have a more consistent impact on targeted brain waves. Lastly, results from participants whose stimulus was controlled by a QL driven stimulus using optimal actions indicated that the optimised actions created a more sustained increase in Beta band activity when compared to any other results, indicating the impact of the optimised policy learned. | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Engineering and the Built Environment | |
| dc.identifier.citation | Cassim, Muhammed Rashaad. (2023). Evaluation and algorithmic adaptation of brain state control through audio entertainment. [Master's dissertation, University of the Witwatersrand, Johannesburg]. WIReDSpace. https://hdl.handle.net/10539/38909 | |
| dc.identifier.uri | https://hdl.handle.net/10539/38909 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Electrical and Information Engineering | |
| dc.subject | Electroencephalogram (EEG) | |
| dc.subject | Virtual reality (VR)_ | |
| dc.subject | Metaverse | |
| dc.subject | Q-Learning (QL) model | |
| dc.subject | Beta band brain waves | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-9: Industry, innovation and infrastructure | |
| dc.title | Evaluation and algorithmic adaptation of brain state control through audio entertainment | |
| dc.type | Dissertation |