Fixation Identification in Centroid versus Start-Point Modes Using Eye-Tracking Data

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.