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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 Department of Psychology, Stanford University, Stanford, California, USA.
Psychophysiology
|August 13, 2026
Summary
Researchers developed eyeris, a pupillometry preprocessing suite, to standardize data analysis and enhance reproducibility. This tool promotes FAIR data practices and offers a transparent, adaptive solution for high-fidelity pupillometry research.
Area of Science:
- Cognitive Neuroscience
- Psychophysiology
- Computational Neuroscience
Background:
- Pupillometry offers insights into cognitive and autonomic processes.
- Current pupillometry preprocessing lacks standardization and reproducibility, unlike fMRI and EEG.
- Existing methods often fail to adhere to Findability, Accessibility, Interoperability, and Reusability (FAIR) principles.
Purpose of the Study:
- To introduce eyeris, a comprehensive pupillometry preprocessing suite.
- To promote FAIR and open science practices in pupillometry research.
- To enhance the reproducibility and transparency of pupillometry data analysis.
Main Methods:
- Development of eyeris, an intuitive, modular, and extensible software suite.
- Implementation of a recommended preprocessing workflow incorporating signal processing best practices.
- Integration of a file management schema and interactive reporting for quality control.
Main Results:
- eyeris provides a standardized and robust framework for pupillometry preprocessing.
- The suite ensures high-fidelity data processing for both tonic and phasic pupillometry.
- Interactive reports facilitate quality assurance and data sharing.
Conclusions:
- eyeris offers a transparent, adaptive, all-in-one solution for pupillometry preprocessing.
- The tool significantly improves the reproducibility of pupillometry research.
- Adoption of eyeris can advance open science practices in the field.
