Characterizing gaze position signals and synthesizing noise during fixations in eye-tracking data
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Characterizing gaze position signals and synthesizing noise during fixations in eye-tracking data Diederick C. Niehorster1 · Raimondas Zemblys2 · Tanya Beelders3 · Kenneth Holmqvist3,4,5
© The Author(s) 2020
Abstract The magnitude of variation in the gaze position signals recorded by an eye tracker, also known as its precision, is an important aspect of an eye tracker’s data quality. However, data quality of eye-tracking signals is still poorly understood. In this paper, we therefore investigate the following: (1) How do the various available measures characterizing eye-tracking data during fixation relate to each other? (2) How are they influenced by signal type? (3) What type of noise should be used to augment eye-tracking data when evaluating eye-movement analysis methods? To support our analysis, this paper presents new measures to characterize signal type and signal magnitude based on RMS-S2S and STD, two established measures of precision. Simulations are performed to investigate how each of these measures depends on the number of gaze position samples over which they are calculated, and to reveal how RMS-S2S and STD relate to each other and to measures characterizing the temporal spectrum composition of the recorded gaze position signal. Further empirical investigations were performed using gaze position data recorded with five eye trackers from human and artificial eyes. We found that although the examined eye trackers produce gaze position signals with different characteristics, the relations between precision measures derived from simulations are borne out by the data. We furthermore conclude that data with a range of signal type values should be used to assess the robustness of eye-movement analysis methods. We present a method for generating artificial eye-tracker noise of any signal type and magnitude. Keywords Eye tracking · Precision · Data quality · Fixational eye movements · Power spectrum · Signal color
Introduction Eye-tracking recordings are used in many fields of science, often to study where participants look or how their eyes move. For screen-based experiments using only static Diederick C. Niehorster
diederick [email protected] Kenneth Holmqvist [email protected] 1
Lund University Humanities Laboratory and Department of Psychology, Lund University, Lund, Sweden
2
ˇ ˇ Siauliai University, Siauliai, Lithuania
3
Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa
4
Institute of Psychology, Nicolaus Copernicus University in Torun, Torun, Poland
5
Department of Psychology, Regensburg University, Regensburg, Germany
stimuli, eye-tracker data are often classified into two types of episodes, fixations (periods during which the participant continuously looks at a specific location on the screen) and saccades (periods during which gaze rapidly shifts to another position on the screen). See Hessels et al. (2018) for an in-depth discussion of fixation and saccade definitions. In this paper, we examin
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