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Review and Evaluation of Eye Movement Event Detection Algorithms.

Birtukan Birawo1, Pawel Kasprowski1

  • 1Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

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Summary

Evaluating eye-tracking event detection algorithms is challenging due to varied methods. This study compared threshold-based, machine learning, and deep learning algorithms, finding CNN and Random Forest generally outperform others for fixation and saccade detection.

Keywords:
event detection algorithmseye movement eventseye trackingfixationssaccades

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Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Data Science

Background:

  • Eye tracking technology analyzes gaze direction, crucial for understanding human behavior and interaction.
  • Event detection algorithms classify eye movements like fixations and saccades from raw eye-tracking data.
  • Standardized evaluation procedures for comparing these algorithms are currently lacking, hindering progress.

Purpose of the Study:

  • To evaluate and compare the performance of various eye-tracking event detection algorithms.
  • To assess the impact of threshold values on threshold-based algorithms and determine optimal settings.
  • To benchmark machine learning and deep learning algorithms against traditional methods using a unified dataset.

Main Methods:

  • Utilized data from a high-speed SMI HiSpeed 1250 eye-tracker system.
  • Performed sample-by-sample comparisons of event detection algorithms (threshold-based, machine learning, deep learning) and human coders.
  • Focused evaluation on the classification of fixations, saccades, and post-saccadic oscillations.

Main Results:

  • All evaluated methods demonstrated good performance in detecting fixations and saccades.
  • Significant differences were observed in the classification accuracy among the algorithms.
  • Convolutional Neural Networks (CNN) and Random Forest (RF) algorithms generally showed superior performance compared to threshold-based methods.

Conclusions:

  • Standardized evaluation using a consistent dataset is crucial for comparing eye-tracking event detection algorithms.
  • Machine learning and deep learning approaches, particularly CNN and RF, offer improved accuracy for classifying eye movements.
  • Further research into optimized algorithms and evaluation metrics is warranted for advancing eye-tracking analysis.