Cybersickness is a major challenge in virtual reality (VR), adversely affecting user comfort and usability. While prior research has examined visual and system factors, the impact of different VR tasks under varied rendering conditions remains underexplored. To address this, we investigated three interaction tasks—navigation, selection, and manipulation—together with two rendering parameters: center area radius (CAR) and peripheral resolution (PR). Cybersickness severity and frequency were assessed using Simulator Sickness Questionnaire (SSQ) scores and electroencephalography (EEG) data. Results show that task type significantly affects cybersickness severity, with CAR playing a critical role. Moreover, α-band power spectral density (PSD) strongly correlates with SSQ scores, suggesting its potential as a biomarker for cybersickness. Neural responses also exhibited temporal delays compared to subjective reports, offering insights into the mechanisms of cybersickness. These findings advance understanding of task- and rendering-related influences, informing more effective prediction and mitigation strategies in VR system design.

Cybersickness significantly impairs user comfort and immersion in virtual reality (VR). Effective identification of cybersickness leveraging physiological, visual, and motion data is a critical prerequisite for its mitigation. However, current methods primarily employ direct feature fusion across modalities, which often leads to limited accuracy due to inadequate modeling of inter-modal relationships. In this paper, we propose a multimodal contrastive learning method for cybersickness recognition. First, we introduce Brain Connectivity Graph Representation (BCGR), an innovative graph-based representation that captures cybersickness-related connectivity patterns across modalities. We further develop three BCGR instances: E-BCGR, constructed based on EEG signals; MV-BCGR, constructed based on video and motion data; and S-BCGR, obtained through our proposed standardized decomposition algorithm. Then, we propose a connectivity-constrained contrastive fusion module, which aligns E-BCGR and MV-BCGR into a shared latent space via graph contrastive learning while utilizing S-BCGR as a connectivity constraint to enhance representation quality. Moreover, we construct a multimodal cybersickness dataset comprising synchronized EEG, video, and motion data collected in VR environments to promote further research in this domain. Experimental results demonstrate that our method outperforms existing state-of-the-art methods across four critical evaluation metrics: accuracy, sensitivity, specificity, and the area under the curve. Source code: https://github.com/PEKEW/cybersickness-bcgr.