Overview
Our research explores motion sickness and cybersickness in virtual reality, including in-car VR and sensory conflicts between visual and vestibular motion. We use EEG, physiological sensing and behavioural measures to detect and understand cybersickness, examine how individual factors such as gaming experience influence susceptibility, and develop mitigation approaches using targeted brain stimulation.
Research projects and themes
Our work spans four connected areas, from understanding the causes of cybersickness to developing methods for its prediction and mitigation.
Methods and capabilities
Our research combines immersive technologies, neuroscience, physiological sensing and computational analysis. This allows us to study cybersickness from multiple perspectives, from subjective experience and behaviour to vestibular activity, brain signals and autonomic responses.
Collaborators
Our XR and cybersickness research is interdisciplinary, bringing together expertise in perception, neuroscience, human–computer interaction and immersive technology.
Academic collaborators
Multimodal Interaction Group
We collaborate closely with the Multimodal Interaction Group, led by Professor Stephen Brewster and based within the Glasgow Interactive Systems Section at the University of Glasgow. The collaboration combines PACO’s expertise in perception and neuroscience with MIG’s research in virtual and augmented reality, multimodal interaction and in-car interfaces.
Industry collaboration
We work with industry partners to investigate the practical challenges of using immersive technologies in real-world settings. These collaborations help translate research on cybersickness, passenger XR and user comfort into safer and more accessible immersive experiences.
Selected publications
These publications represent our work across cybersickness detection, individual susceptibility, in-car VR, neural measurement and brain-stimulation-based mitigation.
Investigating How Brain Stimulation Mitigates Motion Sickness Using Spatiotemporal Nystagmus Parameters Captured in VR
Li, G., McGill, M., Grant, A., Pöhlmann, K., Holden, R., Brewster, S., & Pollick, F. (2026). IEEE Transactions on Visualization and Computer Graphics.
This study uses eye-tracking measures to investigate how vestibular brain stimulation reduces motion sickness in VR.
Predicting Motion Sickness Caused by Mismatched VR Visual Motion and Physical Rotation Using Resting-State EEG and EEGPT
Byeon, H., Li, G., Pollick, F., & Cho, S.-B. (2026). IEEE AIxVR.
This research examines whether resting-state EEG and an EEG foundation model can predict susceptibility to motion sickness caused by visual–vestibular conflict.
Reduced Motion Sickness Using Vestibular EEG-Guided tACS Under Mismatched Physical Rotation and VR Visual Motion
Li, G., McGill, M., Grant, A., Pöhlmann, K., Holden, R., Brewster, S., & Pollick, F. (2025). IEEE Transactions on Intelligent Transportation Systems, 26(8), 11603–11618.
This study evaluates EEG-guided brain stimulation as a method for reducing motion sickness during mismatched physical and virtual motion.
Detecting In-Car VR Motion Sickness from Lower Face Action Units
Li, G., Guha, T., Onuoha, O., Qiu, Z., Grant, A., Feng, Z., Zhang, Z., Pöhlmann, K., McGill, M., Brewster, S., & Pollick, F. (2024). IEEE International Symposium on Mixed and Augmented Reality, 1019–1028.
This work investigates whether facial movements captured through a VR headset can provide an unobtrusive measure of motion sickness in simulated and real-world driving conditions.
Is Video Gaming a Cure for Cybersickness? Gamers Experience Less Cybersickness Than Non-Gamers in a VR Self-Motion Task
Pöhlmann, K. M. T., Li, G., Wilson, G., McGill, M., Pollick, F., & Brewster, S. (2024). IEEE Transactions on Visualization and Computer Graphics, 30(11), 7225–7233.
This study explores individual differences in cybersickness and finds that previous gaming experience may be associated with greater resistance to visually induced motion sickness.
Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection
Li, G., McGill, M., Brewster, S., Chen, C. P., Anguera, J. A., Gazzaley, A., & Pollick, F. (2022). IEEE Journal of Biomedical and Health Informatics, 26(6), 2469–2480.
This research combines neural and autonomic physiological signals to investigate objective, multimodal detection of cybersickness.