Prediction and Detection of Virtual Reality induced Cybersickness: A Spiking Neural Network Approach Using Spatiotemporal EEG Brain Data and Heart Rate Variability

Virtual Reality (VR) is an evolving wearable technology across many domain applications, including health delivery. Yet, human physiological adoption of VR technology is limited by cybersickness (CS) - a debilitating sensation accompanied by a cluster of symptoms, including nausea, oculomotor issues and dizziness. A leading problem is the lack of automated objective tools to predict or detect CS in individuals, which can then be used for resistance training, timely warning systems or clinical intervention. This paper explores the spatiotemporal brain dynamics and heart rate variability involved in cybersickness, and uses this information to both predict and detect CS episodes. The present study applies deep learning of EEG in a spiking neural network (SNN) architecture with a fusion of sympathetic heart rate variability parameters to predict CS prior to using VR (77.5%) and detect it (75.0%), which is more accurate than using just EEG (75%, 70.3%) or ECG alone (74.2%, 72.6%). The study found that Cz (premotor and supplementary motor cortex) and O2 (primary visual cortex) are key hubs in functionally connected networks associated with both CS events and susceptibility to CS. Consequently, Cz and O2 are presented here as promising targets for therapeutic interventions to alleviate and/or prevent the cybersickness.