Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions

Abstract

The constantly evolving technological landscape of the Metaverse has introduced a significant concern: cybersickness (CS). There is growing academic interest in detecting and mitigating these adverse effects within virtual environments (VEs). However, the development of effective methodologies in this field has been hindered by the lack of sufficient benchmark datasets. In pursuit of this objective, we meticulously compiled a comprehensive dataset by analyzing the impact of virtual reality (VR) environments on CS, immersion levels, and EEG-based emotion estimation. Our dataset encompasses both implicit and explicit measurements. Implicit measurements focus on brain signals, while explicit measurements are based on participant questionnaires. These measurements were used to collect data on the extent of cybersickness experienced by participants in VEs. Using statistical methods, we conducted a comparative analysis of CS levels in VEs tailored for specific tasks and their immersion factors. Our findings revealed statistically significant differences between VEs, highlighting crucial factors influencing participant engagement, engrossment, and immersion. Additionally, our study achieved a remarkable classification performance of 96.25% in distinguishing brain oscillations associated with VR scenes using the multi-instance learning method and 95.63% in predicting emotions within the valence-arousal space with four labels. The dataset presented in this study holds great promise for objectively evaluating CS in VR contexts, differentiating between VEs, and providing valuable insights for future research endeavors.

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“…The researchers concluded that while various models for detecting cybersickness have been developed, there is no model to predict early adverse events in VEs. Future, more accurate and effective ML approaches will undoubtedly be inspired by current knowledge of how the brain works, as well as the new brain/organism models/approaches being developed and linking them to these bio-digital frontiers ( Benelli et al, 2023 ; Daşdemir, 2023 ; Yang et al, 2023 ). These and similar studies ( Chang et al, 2023 ; Souchet et al, 2023 ) indicate, as does our research, that we are still at the beginning of understanding the potentially beneficial and detrimental effects of digital worlds on their users, especially older adults ( Drazich et al, 2023 ; Séba et al, 2023 ).…”