ArticleBehavior research methods2020
Characterizing gaze position signals and synthesizing noise during fixations in eye-tracking data.
Article in Behavior research methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
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- The effect of pupil size on data quality in head-mounted eye trackers.Behavior research methods · 2025Article
- The best fixation target revisited: New insights from retinal eye tracking.Behavior research methods · 2025Article
- A Bayesian model of distance perception from ocular convergence.PLoS computational biology · 2025Article
- When is enough enough? Empirical guidelines to determine participant sample size for scene viewing studies.Behavior research methods · 2025Article
- Hilbert-Huang transform based pupil changes analysis for concentration assessment in skilled mowing.Scientific reports · 2025Article
- TittaLSL: A toolbox for creating networked eye-tracking experiments in Python and MATLAB with Tobii eye trackers.Behavior research methods · 2025Article
- LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.Behavior research methods · 2025Article
- Unstable foveation's impact on reading, object tracking, and its implications for diagnosing and intervening in reading difficulties.Scientific reports · 2025Article
- The fundamentals of eye tracking part 3: How to choose an eye tracker.Behavior research methods · 2025Article
- The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study.Behavior research methods · 2025Article
- The percentile-matching technique for synthetic eye tracking signal degradation: A biometric case study.PloS one · 2025Article
- Eye tracker calibration: How well can humans refixate a target?Behavior research methods · 2024Article
- Enhancing eye tracking for nonhuman primates and other subjects unable to follow instructions: Adaptive calibration and validation of Tobii eye trackers with the Titta toolbox.Behavior research methods · 2024Article
- Webcam eye tracking close to laboratory standards: Comparing a new webcam-based system and the EyeLink 1000.Behavior research methods · 2024Article
- Foveolar Drusen Decrease Fixation Stability in Pre-Symptomatic AMD.Investigative ophthalmology & visual science · 2024Article
- A Functional Usability Analysis of Appearance-Based Gaze Tracking for Accessibility.Proceedings. Eye Tracking Research & Applications Symposium · 2024Article
- Precise localization of corneal reflections in eye images using deep learning trained on synthetic data.Behavior research methods · 2024Article
- GlassesValidator: A data quality tool for eye tracking glasses.Behavior research methods · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
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.
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