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

Brain Informatics

Research

Andrew Burton¹  Sean Haddick¹  Bradley Standen¹  Muhammad Arifur Rahman¹  Matthew Harris¹  Nicholas Shopland¹  James Lewis¹  David J. Brown¹  Mufti Mahmud²  Preethi Premkumar³  Simona Nastase⁴  David Downes⁵  Carolyn Thomas⁵  Yangang Xing⁶  Nadja Heym⁷  Zakia Batool Turabee⁷  Alexander Sumich⁷ 

[1] Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Division of Psychology, London South Bank University, SE1 0AA, London, UK;Independent Clinical Psychologist, London, UK;Nottingham School of Art & Design, Nottingham Trent University, Shakespeare St, NG1 4FQ, Nottingham, UK;School of ADBE, Nottingham Trent University, Shakespeare St, NG1 4FQ, Nottingham, UK;School of Social Sciences, Nottingham Trent University, Shakespeare St, NG1 4FQ, Nottingham, UK;

关键词: Biofeedback;    Arousal;    EEG;    HRV;    Glossophobia;    Stress;    VRET;   

DOI  :  10.1186/s40708-023-00193-9

 received in 2022-10-11, accepted in 2023-05-15,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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