Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection

Abstract

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 VRinduced 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.

Exploring Neural Biomarkers in Young Adults Resistant to VR Motion Sickness: A Pilot Study of EEG

“…Taken together, AI research in the field of VRMS is still in its infancy, and it is combining conventional statistics methods with clear brain research regions which is a rigorous scientific approach worth following. The present work advances on previous rigorous studies [6], [7], by identifying the differences in EEG-based brain activity patterns between VRMS-resistant and -susceptible young adults. This work aims to determine whether a hypothesis can be generated for future neurostimulation studies to develop a novel brain regulation techniques-based VRMS mitigation solution.…”

“…The current state-of-the-art brain imaging techniques-based VRMS detection involves a participant wearing an HMD VR headset and watching illusory self-motion-inducing VR contents (e.g., traveling in a tunnel or riding a rollercoaster) in a firstperson perspective while having their EEG signals monitored. Then, conventional statistics [6], [7] or advanced artificial intelligence (AI) approaches [8]- [10] are employed to find the potential correlates between objective EEG features and subjective VRMS ratings. Both [6] and [7] found that some EEG features in the vestibular regions are significantly associated with VRMS ratings, but [7] made this conclusion based on more comprehensive EEG features and taking peripheral physiological signals as references.…”

“…Then, conventional statistics [6], [7] or advanced artificial intelligence (AI) approaches [8]- [10] are employed to find the potential correlates between objective EEG features and subjective VRMS ratings. Both [6] and [7] found that some EEG features in the vestibular regions are significantly associated with VRMS ratings, but [7] made this conclusion based on more comprehensive EEG features and taking peripheral physiological signals as references. Regarding AI-based studies, except for [10], authors claimed that their AI models could achieve a very good detection accuracy of 82.83% with sample size N=130 [8] or even as high as 98% with sample size N=25 [9].…”

“…This work aims to determine whether a hypothesis can be generated for future neurostimulation studies to develop a novel brain regulation techniques-based VRMS mitigation solution. This is a new data analysis study based on partial data from a previous study [7]. The open data set showed after watching the moderate VRMS-inducing contents (that is, tunnel travel), exactly half of the participants (N=10) dropped out due to high VRMS ratings while the other half (N=10) completed to trial.…”

Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality

“…The user needs to choose a number between 0 (no sickness at all) and 20 (frank sickness). Prior studies have proven that the responses from the SSQ and from the FMS are highly correlated; thus many studies have used either question method depending on the design and goal of their experiment [ 27 , 41 , 45 – 51 ]. In this work, we employed the FMS which has the advantage of taking quick responses about each video.…”