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eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R.

Shawn T Schwartz1,2,3,4,5, Haopei Yang1,2, Alice M Xue1,2,3,5

  • 1Stanford University, Stanford, United States.

Biorxiv : the Preprint Server for Biology
|June 12, 2025
PubMed
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This summary is machine-generated.

A new pupillometry preprocessing suite, eyeris, enhances research reproducibility. It offers a standardized, FAIR-compliant workflow for analyzing pupil data, improving cognitive and emotional state insights.

Area of Science:

  • Cognitive Neuroscience
  • Psychophysiology
  • Biomedical Engineering

Background:

  • Pupillometry offers non-invasive insights into cognitive and emotional processes.
  • Existing pupillometry preprocessing lacks standardization and FAIR principles, hindering reproducibility.
  • Established tools exist for fMRI and EEG, but not for pupillometry.

Purpose of the Study:

  • To develop a standardized, FAIR-compliant pupillometry preprocessing suite.
  • To enhance reproducibility in pupillometry research.
  • To provide an intuitive, modular, and extensible solution.

Main Methods:

  • Development of the eyeris pupillometry preprocessing suite.
  • Implementation of a recommended preprocessing workflow.

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  • Integration of signal processing best practices for tonic and phasic pupillometry.
  • Generation of interactive reports for quality control and data sharing.

    • Main Results:

    • eyeris provides a comprehensive solution for pupillometry preprocessing.
    • The suite promotes FAIR and open science practices.
    • It ensures high-fidelity preprocessing for improved reproducibility.

    Conclusions:

    • eyeris offers a robust, transparent, and adaptive solution for pupillometry.
    • The tool aims to significantly improve reproducibility in the field.
    • Standardized preprocessing is crucial for advancing pupillometry research.