ArticleBehavior research methods2017
Noise-robust fixation detection in eye movement data: Identification by two-means clustering (I2MC).
Article in Behavior research methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed.
- Evaluation of the Wearable Eye Tracker Pupil Labs Neon for Gaze Fixation Analysis in Screen-Based Fixation Tasks: Comparison with Tobii Pro Fusion.Journal of eye movement research · 2026Article
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- Fixation-related potentials reveal that confusing program code elicits a late frontal positivity.Scientific reports · 2026Article
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- Unified modelling of gaze and pupil dynamics in saccadic tasks.Scientific reports · 2026Article
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- The fundamentals of eye tracking part 6: Working with areas of interest.Behavior research methods · 2026Article
- The Influence of Noise Perception and Parent-Rated Developmental Characteristics on White Noise Benefits in Children.Journal of eye movement research · 2026Article
- Lack of concordance among infant social attention measures.Scientific reports · 2026Article
- What is socially constructed about race? A cross-national study of racial perception and categorization.PloS one · 2026Article
- Introducing the kollaR package: A user-friendly open-access solution for eye-tracking analysis and visualization.Behavior research methods · 2025Article
- Development of attentional disengagement in typically developing children and children with elevated levels of attentional deficits.Experimental brain research · 2025Article
- When is enough enough? Empirical guidelines to determine participant sample size for scene viewing studies.Behavior research methods · 2025Article
- Reading out bodily cues to predict interactions.Scientific reports · 2025Article
- Sit and face the world: ontogenetic adaptation in infant vocal production and visual attention during the transition to independent sitting.BMC psychology · 2025Article
- The fundamentals of eye tracking part 2: From research question to operationalization.Behavior research methods · 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
16 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, & Kemner Infancy, 20, 601-633, 2015; Wass, Forssman, & Leppänen Infancy, 19, 427-460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, & Smith Behavior Research Methods, 47, 53-72, 2015). Here we introduce a fixation detection algorithm-identification by two-means clustering (I2MC)-built specifically for data across a wide range of noise levels and when periods of data loss may occur. We evaluated the I2MC algorithm against seven state-of-the-art event detection algorithms, and report that the I2MC algorithm's output is the most robust to high noise and data loss levels. The algorithm is automatic, works offline, and is suitable for eye-tracking data recorded with remote or tower-mounted eye-trackers using static stimuli. In addition to application of the I2MC algorithm in eye-tracking research with infants, school children, and certain patient groups, the I2MC algorithm also may be useful when the noise and data loss levels are markedly different between trials, participants, or time points (e.g., longitudinal research).
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