To Blur or Not to Blur? A Multimodal Cybersickness Dataset Under Different Blur Conditions

Cybersickness, a form of motion sickness experienced in virtual reality, remains a significant challenge that limits prolonged user engagement. Researchers have explored various mitigation techniques to reduce user discomfort in VR; however, although several public datasets on cybersickness exist, datasets that include data collected under mitigation techniques are rarely publicly available. To address this gap, we present a user study with 38 participants in a VR roller coaster simulation where they experienced three visual conditions: no blur (NB), static maximum blur (MB), and dynamic blur (DB), where the blur intensity adapts in real time based on users' self-reported sickness levels. We present a novel multimodal dataset that includes eye tracking, head tracking, and physiological signals such as electrocardiogram and galvanic skin response collected under conditions with and without cybersickness reduction techniques. In addition, the dataset contains subjective measures including fast motion sickness ratings reported every 30 seconds, simulator sickness questionnaire scores, and igroup presence questionnaire responses for evaluating users' sense of presence in the virtual environment in different visual conditions. In addition to data recorded during the experience, the dataset contains baseline physiological recordings collected before the VR simulation. As an initial use case, we conducted a case study of training several deep regression models to predict cybersickness in which we achieved 1.59 RMSE with all modality. This open dataset allows future researchers to explore the relationship between physiological responses, user behavior, and cybersickness in VR. It can also support the development of predictive models and adaptive systems that select the most appropriate cybersickness reduction technique for each user, leading to more comfortable and personalized VR experiences.