ArticleBehavior research methods2021
REMoDNaV: robust eye-movement classification for dynamic stimulation.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- The orbitofrontal-anterior temporal network complements the attention network for where to look in social situations.eNeuro · 2026Article
- Multiagent First-Person Perspective Analysis for Leadership Assessment in Pediatric Emergency Simulations: A Feasibility Study.Critical care explorations · 2026Article
- Systematic classification differences across eye movement detection algorithms.Behavior research methods · 2026Article
- Eye movement benchmark data for smooth-pursuit classification.Scientific data · 2026Article
- Mobile eye tracking in the real world: Best practices.Journal of vision · 2026Article
- Beyond the first glance: How human presence enhances visual entropy and promotes spatial learning.PLoS computational biology · 2026Article
- Mapping neural activity during naturalistic visual and memory search.Frontiers in human neuroscience · 2026Article
- Taking the eye-tracker out to dinner: characterizing spatial attention biases during an everyday behavior using computer vision.Scientific reports · 2025Article
- Article
- GMM-HMM-Based Eye Movement Classification for Efficient and Intuitive Dynamic Human-Computer Interaction Systems.Journal of eye movement research · 2025Article
- Neuroscientific Analysis of Logo Design: Implications for Luxury Brand Marketing.Behavioral sciences (Basel, Switzerland) · 2025Article
- The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study.Behavior research methods · 2025Article
- Characterizing eye gaze and mental workload for assistive device control.Wearable technologies · 2025Article
- Combining EEG and eye-tracking in virtual reality: Obtaining fixation-onset event-related potentials and event-related spectral perturbations.Attention, perception & psychophysics · 2025Article
- Evaluating Eye Tracking During Dichoptic Video Viewing With Varied Fellow Eye Contrasts in Amblyopia.Investigative ophthalmology & visual science · 2024Article
- A tutorial: Analyzing eye and head movements in virtual reality.Behavior research methods · 2024Article
- Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search Game.Journal of imaging · 2024Article
- Inhibitory control and working memory using saccadic eye movements in primary glaucoma.Attention, perception & psychophysics · 2024Article
- A Comparison of Head Movement Classification Methods.Sensors (Basel, Switzerland) · 2024Article
- Neuroimaging article reexecution and reproduction assessment system.Frontiers in neuroinformatics · 2024Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm-built on an existing velocity-based approach-that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.