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Related Experiment Video

Updated: Apr 12, 2026

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
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Systematic classification differences across eye movement detection algorithms.

Jonathan Nir1, Leon Y Deouell2,3

  • 1Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, 9190401, Jerusalem, Israel. jonathan.nir@mail.huji.ac.il.

Behavior Research Methods
|April 10, 2026
PubMed
Summary
This summary is machine-generated.

A new toolkit, pEYES, streamlines eye movement (EM) detection in eye-tracking (ET) research, enabling reproducible comparisons between algorithms. Engbert's algorithm showed superior performance, highlighting the need for careful detector selection in EM analysis.

Keywords:
Algorithm evaluationEye movement detectionEye trackingOpen source

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Area of Science:

  • Cognitive Science
  • Computer Science
  • Neuroscience

Background:

  • Eye movement (EM) detection is crucial for eye-tracking (ET) research, segmenting raw data into oculomotor events.
  • Current methods lack standardized evaluation, hindering reproducibility and transparency across studies.

Purpose of the Study:

  • Introduce pEYES, an open-source toolkit for streamlined EM detection and robust algorithm comparison.
  • Provide standardized evaluation procedures for assessing detector performance.

Main Methods:

  • Implemented several threshold-based EM detectors within the pEYES toolkit.
  • Evaluated seven detection algorithms on two human-annotated datasets using metrics like Cohen's kappa and d-prime.
  • Assessed performance for fixation and saccade onsets and offsets.

Main Results:

  • Engbert's adaptive velocity-threshold algorithm consistently performed best, sometimes reaching human-level precision.
  • Detector performance varied significantly across datasets.
  • Fixation offsets and saccade onsets were detected more reliably than other boundaries.

Conclusions:

  • Detector selection is critical and should be tailored to specific tasks and datasets.
  • The pEYES toolkit promotes rigorous and reproducible EM analysis through open collaboration.
  • Standardized evaluation frameworks are essential for advancing eye-tracking research.