VR (Virtual Reality) Motion Sickness (VRMS) refers to purely visually-induced motion sickness. Not everyone is susceptible to VRMS, but if experienced, nausea will often lead users to withdraw from the ongoing VR applications. VRMS represents a serious challenge in the field of VR ergonomics and human factors. Like other neuro-ergonomics researchers did before, this paper considers VRMS as a brain state problem as various etiologies of VRMS support the claim that VRMS is caused by disagreement between the vestibular and visual sensory inputs. However, what sets this work apart from the existing literature is that it explores anti-VRMS brain patterns via electroencephalogram (EEG) in VRMS-resistant individuals. Based on existing datasets of a previous study, we found enhanced theta activity in the left parietal cortex in VRMS-resistant individuals (N= 10) compared to VRMS-susceptible individuals (N=10). Even though the sample size per se is not large, this finding achieved medium effect size. This finding offers new hypotheses regarding how to reduce VRMS by the enhancement of brain functions per se (e.g., via non-invasive transcranial electrostimulation techniques) without the need to redesign the existing VR content.

Virtual reality (VR) has the potential to induce cybersickness (CS), which impedes CS-susceptible VR users from the benefit of emerging VR applications. To better detect CS, the current study investigated whether/how the newly proposed human vestibular network (HVN) is involved in flagship consumer VR-induced CS by simultaneously recording autonomic physiological signals as well as neural signals generated in sensorimotor and cognitive domains. The VR stimuli were made up of one or two moderate CS-inducing entertaining task(s) as well as a mild CS-inducing cognitive task implemented before and after the moderate CS task(s). Results not only showed that CS impaired cognitive control ability, represented by the degree of attentional engagement, but also revealed that combined indicators from all three HVN domains could together establish the best regression relationship with CS ratings. More importantly, we found that every HVN domain had its unique advantage with the dynamic changes in CS severity and time. These results provide evidence for involvement of the HVN in CS and indicate the necessity of HVN-based CS detection.