A Pilot Study on EEG-Based Evaluation of Visually Induced Motion Sickness.

Area of Science:

• Neuroscience
• Virtual Reality Technology
• Human-Computer Interaction

Background:

• Visually induced motion sickness (VIMS), also known as virtual reality motion sickness (VRMS), is a significant challenge in virtual reality (VR) immersion.
• Symptoms of VIMS can detract from user experience and limit the widespread adoption of VR technologies.

Purpose of the Study:

• To investigate the relationship between electroencephalogram (EEG) signals and subjectively reported VIMS levels.
• To identify reliable EEG markers for evaluating VIMS in users.
• To assess the feasibility of using a low-electrode-count wearable EEG device for VIMS assessment.

Main Methods:

• Utilized a VR-based vehicle-driving simulator (VDS) to induce VIMS symptoms in participants.
• Collected electroencephalogram (EEG) data using a wearable Muse device with four electrodes.
• Analyzed EEG data for significant differences between no-VIMS and VIMS states, focusing on metrics like gravity frequency (GF), power spectral entropy (PSE), and Kolmogorov complexity (KC).

Main Results:

• Significant differences (P < 0.05) in EEG markers were observed between no-VIMS and VIMS states.
• Identified specific markers including means and standard deviations of gravity frequency (GF), standard deviation of power spectral entropy (PSE), and means of Kolmogorov complexity (KC) across various electrode sites (e.g., FP1, TP9, FP2, TP10).
• Demonstrated substantial individual variability in VIMS tolerance, susceptibility, and recoverability.

Conclusions:

• EEG markers can effectively differentiate between VIMS and non-VIMS states.
• Wearable EEG devices with a limited number of electrodes are feasible for VIMS evaluation.
• The findings pave the way for objective VIMS assessment and potential mitigation strategies.