Exploring the brain physiological activity and quantified assessment of VR cybersickness using EEG signals

Cybersickness in virtual reality (VR) significantly impedes user experience enhancement. Sensory conflict theory explains cybersickness as arising from brain conflicts, making a brain physiology-based examination essential for cybersickness research. In this study, we analyze the impact of cybersickness on brain neural activity and achieve the quantified assessment of cybersickness using cybersickness-related electroencephalography (EEG) data. We conduct a cybersickness induction experiment by view rotation and simultaneously collect EEG signals from 36 subjects. We investigate both brain functional connectivity and neural oscillation power aiming to demonstrate the specific variation trends of brain physiological characteristics across varying degrees of cybersickness. Filtering raw EEG highlights cybersickness-related features, facilitating the quantified assessment of cybersickness through a Convolutional Temporal-Transformer Network, named CTTNet. The results demonstrate that cybersickness leads to a significant reduction in the power of the beta and gamma frequency bands in the frontal lobe, accompanied by weakened internal connectivity within these bands. Conversely, as the severity of cybersickness increases, connectivity between the posterior brain regions and the frontal lobe in the mid-to-high frequency bands is enhanced. CTTNet achieves accurate evaluation of cybersickness by effectively capturing temporal-spatial EEG features and the long-term temporal dependencies of cybersickness. A significant and robust relationship between cybersickness and cerebral physiological characteristics is demonstrated. These findings hold the potential to offer valuable insights for the future real-time assessment and mitigation of cybersickness, particularly focusing on brain dynamics. •Investigating the impact of various degrees of cybersickness on brain activity.•A network is proposed to learn temporal dependencies within EEG signals.•Accurate quantitative assessment of cybersickness based on EEG.