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Eye-tracker algorithms to detect saccades during static and dynamic tasks: a structured review.

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This review highlights the need for standardized saccade detection algorithms in eye-tracking research. Current methods, often velocity-based and used with static systems, show limitations in validity and comparability across studies.

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Area of Science:

  • Ophthalmology
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Eye-tracking devices are crucial for measuring saccadic eye movements in clinical and scientific applications.
  • The proliferation of diverse eye-tracking technologies necessitates robust algorithm development and validation.

Purpose of the Study:

  • To review current saccade detection algorithms used with eye-tracking data.
  • To assess the validation methods and applicability of these algorithms.
  • To identify limitations and propose improvements for future research.

Main Methods:

  • A systematic literature search was conducted across major scientific databases (Medline, Embase, PsychInfo, Scopus, IEEEXplore, ACM Digital Library).
  • Two independent reviewers and an adjudicator screened articles focusing on saccade detection from infrared/video-based eye-tracker data.
  • Thirteen articles met the inclusion criteria for the review.

Main Results:

  • Most saccade detection algorithms reviewed employ simple velocity-based classifications.
  • Algorithms are predominantly used with static eye-tracking systems, limiting experimental validity.
  • Significant heterogeneity exists in system design, proprietary algorithms, processing, and reporting.

Conclusions:

  • There is a critical need for standardized methodologies in saccade detection to enhance experimental validity.
  • Improved standardization will facilitate more reliable comparisons of results across different eye-tracking studies.
  • Future research should focus on developing and validating more robust and comparable saccade detection algorithms.