School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, 4072, Australia.
Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology Delhi (IIIT-D), New Delhi, India.
Sci Rep. 2024 Sep 17;14(1):21667. doi: 10.1038/s41598-024-72376-1.
In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, including electroencephalography and electrodermal activity. An experiment has been undertaken whereby participants (N = 22) were each exposed to three different levels of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodiment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector Machine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Regression, and Multiple Layer Perceptron. Results demonstrated that the Multiple Layer Perceptron model obtained the highest macro average accuracy of . Posthoc analysis revealed that relative band power, which is expressed as the ratio of power in a specific frequency band to the total baseline power, in both the frontal and parietal regions, including beta over theta and alpha ratio, and differential entropy were most significant in detecting different levels of presence.
在虚拟现实（VR）中，更高的存在感会积极影响用户的体验和参与度。有几个参数负责在 VR 中产生不同程度的存在感，包括但不限于图形保真度、多感官刺激和体现感。然而，存在感的标准测量方法，包括自我报告问卷，存在偏差。本研究专注于通过机器学习开发一种稳健的模型，使用多模态神经和生理信号，包括脑电图和皮肤电活动，来检测 VR 中的不同存在感水平。已经进行了一项实验，其中参与者（N=22）在 VR 中以随机顺序分别暴露于三种不同程度的存在感（高、中、低）。在每个级别中系统地操纵了包括图形保真度、音频提示、延迟和带有触觉反馈的体现感在内的四个参数，以区分不同级别。在使用 One-vs-Rest 方法的三分类问题中评估了多种多类分类器，包括支持向量机、k-最近邻、额外梯度提升、随机森林、逻辑回归和多层感知机。结果表明，多层感知机模型获得了最高的宏观平均准确率为. 事后分析表明，相对频带功率（表示特定频带中的功率与总基线功率的比值），在前额和顶叶区域，包括β波相对于θ波和α波的比值以及差分熵，在检测不同程度的存在感方面最为显著。
