Fixation identification in centroid versus start-point modes using eye-tracking data

Perceptual and Motor Skills
Torbjörn FalkmerMatilda Larsson

Abstract

Fixation-identification algorithms, needed for analyses of eye movements, may typically be separated into three categories, viz. (i) velocity-based algorithms, (ii) area-based algorithms, and (iii) dispersion-based algorithms. Dispersion-based algorithms are commonly used but this application introduces some difficulties, one being optimization. Basically, there are two modes to reach this goal of optimization, viz., the start-point mode and the centroid mode. The aim of the present study was to compare and evaluate these two dispersion-based algorithms. Manual inspections were made of 1,400 fixations in each mode. Odds ratios showed that by using the centroid mode for fixation detection, a valid fixation is 2.86 times more likely to be identified than by using the start-point mode. Moreover, the algorithm based on centroid mode dispersion showed a good interpretation speed, accuracy, robustness, and ease of implementation, as well as adequate parameter settings.

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Citations

Dec 25, 2008·Journal of Neuroengineering and Rehabilitation·Joakim DahlmanTorbjörn Falkmer
May 29, 2014·Developmental Neurorehabilitation·Annette JoostenTorbjörn Falkmer
Jan 30, 2019·Physiological Measurement·Samuel StuartAlan Godfrey
Jun 19, 2019·Journal of Autism and Developmental Disorders·Julia S Y TangSonya Girdler
Sep 19, 2018·Royal Society Open Science·Roy S HesselsIgnace T C Hooge

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