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

TL;DR

This paper systematically reviews recent deep learning methods applied to EEG signals for detecting cybersickness in VR, highlighting promising accuracy rates and identifying research gaps.

Contribution

It provides a comprehensive overview of DL frameworks used for EEG-based cybersickness detection and offers guidelines for future research directions.

Findings

DL frameworks (DNN, CNN, RNN) achieve around 93% accuracy

Limited number of studies in this emerging field

Identifies key challenges and future research guidelines

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 EEG-based 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.