Brain Signatures of Time Perception in Virtual Reality

TL;DR

This study demonstrates that EEG can objectively index time-perception states in virtual reality. By presenting diverse time-modulation paradigms and labeling events as overestimation, correct estimation, or underestimation, the authors identify robust spectral signatures across delta/theta, alpha/beta, and high-beta/gamma bands that persist across participants, modulators, and durations. The findings reveal a consistent mapping: delta/theta characterize overestimation, alpha/beta characterize correct estimation, and high-beta/gamma characterize underestimation, supporting attention-allocation theories of temporal processing. These brain signatures enable potential real-time monitoring and adaptive VR systems that tailor the experience to individual cognitive states, enhancing immersion and user experience.

Abstract

Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measurement and representation of user state. While extrinsic physical characteristics such as locomotion and pose can be accurately tracked in real-time, reliably capturing mental states is more challenging. Quantitative psychology allows considering more intrinsic features like emotion, attention, or cognitive load. Time perception, in particular, is strongly tied to users' mental states, including stress, focus, and boredom. However, research on objectively measuring the pace at which we perceive the passage of time is scarce. In this work, we investigate the potential of electroencephalography (EEG) as an objective measure of time perception in VR, exploring neural correlates with oscillatory responses and time-frequency analysis. To this end, we implemented a variety of time perception modulators in VR, collected EEG recordings, and labeled them with overestimation, correct estimation, and underestimation time perception states. We found clear EEG spectral signatures for these three states, that are persistent across individuals, modulators, and modulation duration. These signatures can be integrated and applied to monitor and actively influence time perception in VR, allowing the virtual environment to be purposefully adapted to the individual to increase immersion further and improve user experience. A free copy of this paper and all supplemental materials are available at https://vrarlab.uni.lu/pub/brain-signatures.

Figures (10)

• Figure 1: Experiment setup showing (1) EEG cap, (2) headset-mounted hand-tracking device, and (3) VR headset.
• Figure 2: Gesture-based user interface used in our study.
• Figure 3: Example collage of four pictures for each mode of emotion modulator; the pictures are blurred due to the authors of the image datasets not allowing publication.
• Figure 4: Three modes of visual cognitive modulator.
• Figure 5: Three modes of visual oddball modulator.