Brain activity during cybersickness: a scoping review.

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Virtual Reality

Background:

• Virtual reality (VR) use frequently induces cybersickness, characterized by nausea and disorientation.
• Current cybersickness detection relies on subjective questionnaires, prompting research into objective measures like electroencephalogram (EEG).
• Understanding EEG correlates of cybersickness is crucial for developing reliable, non-invasive detection methods.

Purpose of the Study:

• To conduct a scoping review of experimental studies investigating EEG for cybersickness detection.
• To analyze EEG analysis pipelines, including preprocessing, feature extraction, selection, and classification methods.
• To identify trends in VR content, hardware, and participant demographics within EEG-based cybersickness research.

Main Methods:

• A systematic database search identified 33 experimental studies on cybersickness and EEG.
• EEG analysis pipelines were categorized into four key steps: preprocessing, feature extraction, feature selection, and classification.
• Characteristics of methods used in each step were surveyed and analyzed.

Main Results:

• Frequency or time-frequency analysis dominated EEG feature extraction methods.
• Classification models achieved high accuracy (79-100%) in predicting cybersickness.
• Studies predominantly utilized head-mounted display (HMD)-based VR and portable EEG devices, often featuring scenic navigation content with young adult participants.

Conclusions:

• EEG shows promise as an objective measure for detecting cybersickness.
• Standardization of EEG analysis pipelines and broader demographic studies are needed.
• This review provides a comprehensive overview and outlines future research avenues in EEG-based cybersickness detection.