Exploring neural and peripheral physiological correlates of simulator sickness

Abstract

This article investigates neural and physiological correlates of simulator sickness (SS) through a controlled experiment conducted within a fully immersive dome projection system. Our goal is to establish a reliable, objective, and in situ measurable predictive indicator of SS. SS is a problem common to all types of visual simulators consisting of motion sickness-like symptoms that may be experienced while and after being exposed to a dynamic, immersive visualization. It leads to ethical concerns and impaired validity of simulator-based research. Due to the popularity of virtual reality devices, the number of people exposed to this problem is increasing and, therefore, it is crucial to find reliable predictors of this condition before any symptoms appear. Despite its relevance and the several theories about its origins, SS cannot yet be quantitatively modeled and predicted. Our results indicate that, while neural correlates did not materialize, physiological measures may be a solid early indicator of oncoming SS.

Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models

“…All EEG features got negative values of adjusted R 2 score, indicating the failure of using EEG features to establish the regression model for VR sickness prediction. The superiority of autonomic physiological signals presented here is consistent with previous study [9].…”

“…Since autonomic responses (such as sweating, increased heart rate and nausea) are the most obvious symptoms, there are many autonomic physiological signal-based solutions for objective and automatic VR sickness assessment [4]- [7]. However, the current state-of-the-art approach in motion sickness and simulator sickness assessment (including but not limited to pure VR sickness) is AI-integrated multimodal biosensing including behavioural and autonomic physiological data and neural data [8], [9]. For example, Prof Gargiulo et al from the motion sickness lab at the Reykjavik University are using EMG (Electromyography, that is, behavioural physiological signals), EEG (electroencephalogram, that is, neural signals) and heart rate (autonomic physiological signals) together to evaluate VRbased simulator sickness.…”

“…They concluded that EMG features are the most correlated with self-reported simulator sickness levels, which are followed by EEG features; while the heart rate features showed the poorest performance. In another study [9], Prof Marcus et al from the Technical University of Braunschweig used EEG and a set of autonomic physiological signals (including finger-mounted heart rate, respiration as well as electrodermal activity (EDA) signals) to assess simulator sickness. They found that EDA may be a solid indicator of simulator sickness if compared to EEG features.…”

“…The inconsistent findings about simulator sickness in [8] (which shows that EEG data is superior to autonomic physiological data) and [9] (which showed that autonomic physiological data is superior to EEG data) leads to a research question of how autonomic physiological and neural data differs in the context of pure VR sickness assessment. This is especially pertinent given that both [8] and [9] did not explore brain network-based EEG features, which we believe are more correlated with VR sickness levels compared to conventional EEG power spectrum if according to the recently proposed human vestibular network (HVN) [10]. HVN is a widespread brain network including at least the autonomic, sensorimotor and cognitive domains [10].…”

Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection

“…Gang Li, Mark McGill, Stephen Brewster, Chao Ping Chen, Joaquin A. Anguera, Adam Gazzaley, and Frank Pollick T recent study [15], Tauscher and colleagues investigated both EEG and autonomic physiological signals during CS using a physical projection room-based dome VR. The authors claimed that the reason they did not use a head mounted display (HMD) was that the straps on the HMD would interfere with the EEG signals.…”