title: 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.
authors: Rehabilitation Medicine, Department of Neuroscience and Locomotion IKE, Faculty of Health Sciences, Linköping University, Sweden. torbjorn.falkmer@hhj.hj.se
authors: Torbjörn
authors: T
authors: Falkmer
authors: Joakim
authors: J
authors: Dahlman
authors: Tania
authors: T
authors: Dukic
authors: Anna
authors: A
authors: Bjällmark
authors: Matilda
authors: M
authors: Larsson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003625:Data Collection; D005133:Eye Movements / Q000502:physiology*; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D009799:Ocular Physiological Phenomena; D016017:Odds Ratio; D011932:Reading; D012107:Research Design; D012438:Saccades / Q000502:physiology; D012984:Software; D013647:Task Performance and Analysis; D014743:Videotape Recording / Q000706:statistics & numerical data; D014794:Visual Fields / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*