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Preprocessing pupil size data: Guidelines and code.

Mariska E Kret1,2, Elio E Sjak-Shie3

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Pupillometry, the study of pupil size, offers insights into cognitive and emotional states. This guide details how to preprocess pupillometry data, focusing on noise detection and removal for accurate analysis.

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Area of Science:

  • Psychophysiology
  • Cognitive Neuroscience
  • Ophthalmology

Background:

  • Pupillometry is a widely used psychophysiological measure.
  • Pupil size changes reflect cognitive states, emotions, and brain functioning.
  • Applications range from clinical assessment to driving safety.

Purpose of the Study:

  • To review literature on pupil size measurements.
  • To identify and detect sources of noise in pupillometry data.
  • To provide guidelines and tools for correct data preprocessing.

Main Methods:

  • Literature review on pupillometry.
  • Identification of common noise sources in pupil size data.
  • Development of step-by-step preprocessing guidelines.
  • Provision of an open-source MATLAB script for data processing.

Main Results:

  • Noise is a significant factor in pupillometry data.
  • Effective noise detection and removal are crucial for accurate analysis.
  • Standardized preprocessing enhances the reliability of pupillometry findings.

Conclusions:

  • Accurate preprocessing of pupillometry data is essential.
  • The provided guidelines and script facilitate reliable pupil size analysis.
  • Further interdisciplinary research in pupillometry is encouraged.