  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.
