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Related Experiment Video

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Video-oculography in Mice

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Published on: July 19, 2012

A Preprocessing Pipeline for Pupillometry Signal from Multimodal iMotion Data.

Jingxiang Ong¹, Wenjing He¹, Princess Maglanque¹

¹Department of Surgery, University of Manitoba Max Rady College of Medicine, Winnipeg, MB R3E 0W2, Canada.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study presents a standardized pipeline for preprocessing pupillometry data within multimodal research platforms. The developed method enhances data quality and integrates pupil diameter with facial expression data for reliable analysis.

Keywords:

linear regression median absolute deviation (MAD)moving average (MA) filter multimodal data piecewise cubic hermite interpolatingpolynomial (PCHIP)preprocessing pupillometry

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Area of Science:

Multimodal human performance research
Cognitive science and psychophysiology

Background:

Pupillometry is vital for assessing cognitive effort and attention.
Multimodal research platforms like iMotions integrate eye tracking and facial expression data.
Standardized pupillometry data preprocessing is lacking in multimodal research.

Purpose of the Study:

To introduce a systematic pipeline for preprocessing pupillometry data on the iMotions platform.
To address challenges in multimodal pupillometry data management, including timestamp misalignment and missing data.
To enhance the quality and integration of pupillometry data with other physiological signals.

Main Methods:

Developed a pipeline for pupil diameter preprocessing using iMotions software.
Implemented artifact removal, outlier detection (Median Absolute Deviation, Moving Average), and data interpolation (Piecewise Cubic Hermite Interpolating Polynomial).
Utilized linear regression for mean pupil diameter calculation, followed by normalization and integration with facial expression data.

Main Results:

The pipeline effectively reduces noise and enhances the quality of pupillometry data.
Successfully integrated processed pupil diameter data with facial expression datasets.
Demonstrated improved data reliability and preservation of critical information for analysis.

Conclusions:

The proposed pipeline offers a robust and organized method for pupillometry data preprocessing in multimodal research.
This approach facilitates seamless integration of pupillometry with other data streams.
The pipeline improves the overall reliability and utility of pupillometry data for scientific investigation.