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.

中文翻译：

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通过使用机器学习从多模态数据中进行唤醒检测，增强生物反馈驱动的自我引导虚拟现实暴露疗法

虚拟现实暴露疗法（VRET）是一种新颖的干预技术，允许个人在安全的环境中体验引起焦虑的刺激，识别特定的触发因素并逐渐增加他们对感知威胁的暴露。公开演讲焦虑（PSA）是社交焦虑的一种普遍形式，其特点是在向观众演讲时产生压力和焦虑。在自我引导的 VRET 中，参与者可以逐渐提高对暴露的耐受性，并随着时间的推移减少焦虑引起的唤醒和 PSA。然而，创建这样一个 VR 环境并确定焦虑引起的唤醒或痛苦的生理指标是一个公开的挑战。环境建模、角色创建和动画，心理状态确定和使用机器学习（ML）模型进行焦虑或压力检测同样重要，并且需要多学科专业知识。在这项工作中，我们利用公开数据集（使用脑电图和心率变异性）探索了一系列机器学习模型来预测唤醒状态。如果我们能够检测到焦虑引起的唤醒，我们就可以触发镇静活动，让个人应对和克服痛苦。在这里，我们讨论在唤醒检测中有效选择机器学习模型和参数的方法。我们提出了一个管道来克服虚拟现实暴露治疗背景下不同参数设置的模型选择问题。该管道可以扩展到唤醒检测至关重要的其他感兴趣的领域。最后，