Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Rahman, M.A., Brown, D.J. ORCID: 0000-0002-1677-7485, Mahmud, M. ORCID: 0000-0002-2037-8348, Harris, M., Shopland, N. ORCID: 0000-0003-2082-9070, Heym, N. ORCID: 0000-0003-2414-8854, Sumich, A. ORCID: 0000-0003-4333-8442, Turabee, Z.B., Standen, B., Downes, D. ORCID: 0000-0002-4886-5260, Xing, Y., Thomas, C., Haddick, S., Premkumar, P., Nastase, S., Burton, A. ORCID: 0000-0002-9073-8310 and Lewis, J., 2023. Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning. Brain Informatics, 10: 14. ISSN 2198-4018

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Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety

Item Type: Journal article
Publication Title: Brain Informatics
Creators: Rahman, M.A., Brown, D.J., Mahmud, M., Harris, M., Shopland, N., Heym, N., Sumich, A., Turabee, Z.B., Standen, B., Downes, D., Xing, Y., Thomas, C., Haddick, S., Premkumar, P., Nastase, S., Burton, A. and Lewis, J.
Publisher: Springer
Date: December 2023
Volume: 10
ISSN: 2198-4018
Rights: © the author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > Nottingham School of Art & Design
Schools > School of Architecture, Design and the Built Environment
Schools > School of Science and Technology
Schools > School of Social Sciences
Record created by: Jonathan Gallacher
Date Added: 03 Jul 2023 17:06
Last Modified: 03 Jul 2023 17:06
URI: https://irep.ntu.ac.uk/id/eprint/49314

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