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Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes

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Published on: March 3, 2023

A new robust multivariate mode estimator for eye-tracking calibration.

Adrien Brilhault¹, Sergio Neuenschwander², Ricardo Araujo Rios³

¹Department of Computer Science, Federal University of Bahia, Salvador, Brazil.

Behavior Research Methods

|March 17, 2022

Summary

This summary is machine-generated.

We developed a new algorithm, BRIL, to accurately estimate the primary mode in contaminated eye-tracking calibration data. This method improves gaze mapping accuracy, even with significant outlier clusters.

Keywords:

Calibration Data depth Eye-tracking Multivariate mode

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Area of Science:

Data analysis
Biomedical engineering
Computer vision

Background:

Eye-tracking calibration data often contains outliers from non-cooperative subjects.
Existing central tendency measures struggle with multimodal distributions common in contaminated data.
Inaccurate calibration leads to errors in mapping gaze to screen coordinates.

Purpose of the Study:

To introduce a novel algorithm, BRIL, for accurately estimating the primary mode of multivariate distributions.
To address the challenge of high data contamination in eye-tracking calibration.
To improve the accuracy of gaze-to-screen coordinate mapping.

Main Methods:

Developed the BRIL algorithm utilizing recursive depth-based filtering.
Tested BRIL on artificial mixtures of Gaussian and Uniform distributions.
Compared BRIL's performance against conventional depth medians, robust estimators, and clustering methods.

Main Results:

BRIL demonstrated outstanding performance in estimating the primary mode, even with high proportions of clustered and random outliers.
The algorithm proved effective in highly contaminated distributions where other methods failed.
Successful application to real-world eye-tracking calibration data from Capuchin monkeys.

Conclusions:

BRIL offers a robust and accurate solution for estimating the primary mode in contaminated multivariate distributions.
The algorithm significantly enhances eye-tracking calibration accuracy, particularly in challenging datasets.
BRIL shows strong potential for applications in neuroscience and human-computer interaction research.