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GazeParser: an open-source and multiplatform library for low-cost eye tracking and analysis.

Hiroyuki Sogo1

  • 1Ehime University, 3 Bunkyo-cho, Matsuyama, Ehime, 790-8577, Japan. hsogo@ehime-u.ac.jp

Behavior Research Methods
|December 15, 2012
PubMed
Summary

GazeParser offers an affordable, open-source solution for eye movement analysis in cognitive research. This video-based system provides accurate data comparable to commercial trackers, overcoming cost and programming barriers.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computer Vision

Background:

  • Eye movement analysis is crucial for understanding visual perception and cognition.
  • High costs and complex programming of traditional eye trackers pose significant barriers to research.

Purpose of the Study:

  • To introduce GazeParser, an open-source, low-cost eye tracking and data analysis library.
  • To evaluate the performance and accuracy of GazeParser in psychological experiments.

Main Methods:

  • GazeParser utilizes a video-based eye tracker and Python libraries for data recording and analysis.
  • Integration with experimental control libraries like PsychoPy and VisionEgg is supported.
  • Performance was assessed through three eye movement experiments.

Main Results:

  • GazeParser demonstrated minimal errors in sampling intervals (less than 1 ms).
  • Spatial accuracy ranged from 0.7° to 1.2° across participants.
  • Saccade detection (latency and amplitude) in gap/overlap and antisaccade tasks showed agreement with commercial eye trackers.

Conclusions:

  • GazeParser provides a viable, cost-effective alternative for eye tracking in research.
  • The system exhibits adequate performance for psychological experiments, facilitating broader accessibility to eye movement analysis.