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Related Experiment Video

Updated: Aug 4, 2025

Virtual Reality Experiments with Physiological Measures

Virtual Reality Experiments with Physiological Measures

Published on: August 29, 2018

GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality.

Dillon Lohr¹, Samantha Aziz², Lee Friedman²

¹Texas State University, Department of Computer Science, San Marcos, TX, 78666, USA. djl70@txstate.edu.

Scientific Data

|March 30, 2023

Summary

GazeBaseVR offers a large, longitudinal eye-tracking dataset from VR headsets, ideal for studying eye movement biometrics and diverse research applications.

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Area of Science:

Ophthalmology
Computer Science
Human-Computer Interaction

Background:

Eye-tracking (ET) data is crucial for understanding human visual behavior.
Existing datasets often lack the scale, longitudinal aspect, or VR integration needed for advanced research.
Virtual reality (VR) offers immersive environments for collecting ecologically valid eye-tracking data.

Purpose of the Study:

To introduce GazeBaseVR, a comprehensive, longitudinal binocular eye-tracking dataset.
To provide a rich resource for research in eye movement biometrics, VR interaction, and human behavior.
To enable studies on factors influencing eye movements, including fairness and health impacts.

Main Methods:

Collected 5,020 binocular eye-tracking recordings at 250 Hz using an ET-enabled VR headset.
Recruited 407 diverse college-aged participants, with recordings up to six times over 26 months.
Included five distinct eye-tracking tasks: vergence, smooth pursuit, video viewing, reading, and saccades.

Main Results:

GazeBaseVR is a large-scale, longitudinal dataset with extensive participant diversity.
The dataset includes participants from previous studies and those recorded pre/post COVID-19 infection.
Rich metadata supports research on eye movement biometrics, VR usability, and fairness.

Conclusions:

GazeBaseVR is a valuable resource for advancing eye-tracking research in VR.
Its longitudinal nature and diverse tasks support robust analysis of eye movement patterns.
The dataset facilitates research on individual differences and health-related changes in visual behavior.