Virtual reality (VR) presents immersive opportunities across many applications, yet the inherent risk of developing cybersickness during interaction can severely reduce enjoyment and platform adoption. Cybersickness is marked by symptoms such as dizziness and nausea, which previous work primarily assessed via subjective post-immersion questionnaires and motion-restricted controlled setups. In this paper, we investigate the \emph{dynamic nature} of cybersickness while users experience and freely interact in VR. We propose a novel method to \emph{continuously} identify and quantitatively gauge cybersickness levels from users' \emph{passively monitored} electroencephalography (EEG) and head motion signals. Our method estimates multitaper spectrums from EEG, integrating specialized EEG processing techniques to counter motion artifacts, and, thus, tracks cybersickness levels in real-time. Unlike previous approaches, our method requires no user-specific calibration or personalization for detecting cybersickness. Our work addresses the considerable challenge of reproducibility and subjectivity in cybersickness research.

翻译：虚拟现实（VR）为众多应用领域提供了沉浸式体验机会，然而交互过程中诱发虚拟现实晕动症的固有风险会严重降低用户体验与平台采用率。虚拟现实晕动症以眩晕、恶心等症状为特征，既往研究主要通过沉浸后主观问卷及运动受限的受控实验设置进行评估。本文探究用户在VR环境中自由交互体验时虚拟现实晕动症的动态特性。我们提出一种创新方法，通过被动监测用户的脑电图（EEG）与头部运动信号，连续识别并量化评估虚拟现实晕动症水平。该方法通过多锥谱估计处理EEG信号，结合专用EEG处理技术以抑制运动伪影，从而实现晕动症水平的实时追踪。与现有方法不同，本方法无需针对个体用户进行晕动症检测的校准或个性化设置。本研究解决了虚拟现实晕动症研究中可重复性与主观性方面的重大挑战。