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

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.
• 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.