A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality

Abstract

Cybersickness is an unpleasant side effect of exposure to a virtual reality (VR) experience and refers to such physiological repercussions as nausea and dizziness triggered in response to VR exposure. Given the debilitating effect of cybersickness on the user experience in VR, academic interest in the automatic detection of cybersickness from physiological measurements has crested in recent years. Electroencephalography (EEG) has been extensively used to capture changes in electrical activity in the brain and to automatically classify cybersickness from brainwaves using a variety of machine learning algorithms. Recent advances in deep learning (DL) algorithms and increasing availability of computational resources for DL have paved the way for a new area of research into the application of DL frameworks to EEGbased detection of cybersickness. Accordingly, this review involved a systematic review of the peer-reviewed papers concerned with the application of DL frameworks to the classification of cybersickness from EEG signals. The relevant literature was identified through exhaustive database searches, and the papers were scrutinized with respect to experimental protocols for data collection, data preprocessing, and DL architectures. The review revealed a limited number of studies in this nascent area of research and showed that the DL frameworks reported in these studies (i.e., DNN, CNN, and RNN) could classify cybersickness with an average accuracy rate of 93%. This review provides a summary of the trends and issues in the application of DL frameworks to the EEG-based detection of cybersickness, with some guidelines for future research.

Graduating nursing students' user experiences of the immersive virtual reality simulation in learning – A qualitative descriptive study

“…Simulation sickness is the term used to describe the temporary side effects caused by immersive VR, which may include symptoms such as nausea, dizziness or eyestrain. (Yildirim, 2020 ). Experienced simulation sickness may affect users' interest in using VR products (Davis et al, 2014 ).…”

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

“…It incorporates convolutional layers that perform convolutions to identify patterns, pooling layers for faster parameterization, and fullyconnected layers with full connections to the previous layer for high-level reasoning [71]. Given the high performance of CNN architectures for image data, using images extracted by sequential raw EEG data as an input to a CNN constitutes a trend in EEG preprocessing [34], [75]. These images are called topo-maps and preserve the spatial and spectral traits of the signal, namely, the electrode location and adequate frequency information [76].…”

A review of cybersickness in head-mounted displays: raising attention to individual susceptibility

“…Our review shows that including physiological data for training results does not ensure high prediction power. However, Yildirim's review (Yildirim 2020) reported that EEG signals showed a great power for successful prediction. The difference may lie in the sample size or the content used.…”