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Exploring Gaze Dynamics in Virtual Reality through Multiscale Entropy Analysis.

Sahar Zandi1, Gregory Luhan1

  • 1Department of Architecture, Texas A&M University, College Station, TX 77843, USA.

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|March 28, 2024
PubMed
Summary

Multiscale Entropy (MSE) analysis of eye movements in virtual reality (VR) reveals user interaction complexity. This complexity can guide the development of more intuitive and personalized VR experiences.

Keywords:
eye movementshuman sensingmultiscale entropytime series analysisuser experiencevirtual reality

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Area of Science:

  • Human-Computer Interaction
  • Neuroscience
  • Virtual Reality Technology

Background:

  • Understanding user behavior in virtual reality (VR) is crucial for enhancing immersion and usability.
  • Eye movement analysis offers a sensitive measure of cognitive and interactive processes.
  • Existing methods may not fully capture the complex, dynamic nature of gaze patterns in VR.

Purpose of the Study:

  • To analyze the complexity of binocular eye movements during various virtual reality tasks using Multiscale Entropy (MSE).
  • To investigate the longitudinal changes in eye movement patterns over time.
  • To explore the potential of MSE as a tool for optimizing VR interfaces and user experiences.

Main Methods:

  • Analysis of 5020 binocular eye movement recordings from 407 college-aged participants in the GazeBaseVR dataset.
  • Application of Multiscale Entropy (MSE) to vertical and horizontal eye movement components.
  • Data collected at 250 Hz using an eye-tracking-enabled VR headset across tasks like vergence, smooth pursuit, video viewing, reading, and random saccades.
  • Longitudinal data collection over a 26-month period.

Main Results:

  • Multiscale Entropy (MSE) successfully quantified the complexity of gaze patterns during diverse VR interactions.
  • Analysis revealed insights into the predictability and complexity of user interactions within virtual environments.
  • Longitudinal data provided a temporal perspective on eye movement behavior changes.

Conclusions:

  • MSE is a valuable analytical tool for understanding user behavior and interaction complexity in VR.
  • Findings suggest MSE can inform the design of more intuitive, immersive, and personalized VR experiences.
  • The study highlights the potential for MSE to contribute to enhanced user comfort and engagement in future VR technologies.