Locomotion techniques with EEG signals in a virtual reality environment

Abstract

The popularity of virtual reality (VR) experiences has been driven by a combination of technological advances, increased accessibility, and increased interest from both consumers and industries. VR applications are a great example of how immersive environments like Metaverse can take our perception to a new level. Locomotion is the most critical component in VR applications. Locomotion means movement from one place to another in a virtual reality environment. However, VR locomotion can have physical and psychological effects on users. These include potential adverse effects such as cybersickness (CS), eyestrain, disorientation, and psychological problems such as addiction or detachment from reality. Predicting potential problems is essential to understanding and mitigating them. A VREEG dataset was obtained to predict VR locomotion effects with thirty-two participants who completed ten locomotion techniques. This dataset used objective and subjective measures for VR locomotion tasks to assess physiological, usability, and cybersickness/predictability. A predictive model has used EEG features extracted from time, frequency, and time–frequency domains. This model achieved an accuracy of 99% from the dataset for nausea, oculomotor, and disorientation levels. It has also attracted attention due to its teleportation techniques (such as shift and blink locomotion), fast transitions, and low CS. Predictive goals provide insight into potential interests and areas where proactive action can be taken to reduce risks and maximize the benefits of virtual reality technology.