ReviewSensors (Basel, Switzerland)2022
Review and Evaluation of Eye Movement Event Detection Algorithms.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Detecting gaze shifts of moving observers in dynamic environments.Behavior research methods · 2026Article
- Design and Performance Evaluation of a Low-Cost High-SNR EOG Sensing System for Arabic Locked-In Syndrome Communication.Sensors (Basel, Switzerland) · 2026Article
- Systematic classification differences across eye movement detection algorithms.Behavior research methods · 2026Article
- Comprehensive dataset of features describing eye-gaze dynamics across multiple tasks.Scientific data · 2026Article
- Eye Movement Classification Using Neuromorphic Vision Sensors.Journal of eye movement research · 2026Article
- Introducing the kollaR package: A user-friendly open-access solution for eye-tracking analysis and visualization.Behavior research methods · 2025Article
- Robust Camera-Based Eye-Tracking Method Allowing Head Movements and Its Application in User Experience Research.Journal of eye movement research · 2025Article
- Vision toolkit part 2. features and metrics for assessing oculomotor signal: a review.Frontiers in physiology · 2025Review
- Examining infants' visual paired comparison performance in the US and rural Malawi.Developmental science · 2024Article
- Reading with Screen Magnification: Eye Movement Analysis Using Compensated Gaze Tracks.Proceedings. Eye Tracking Research & Applications Symposium · 2024Article
- Identifying Autism Gaze Patterns in Five-Second Data Records.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Detecting Abnormal Eye Movements in Patients with Neurodegenerative Diseases - Current Insights.Eye and brain · 2024Review
- Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.Journal of vision · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods.
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Registered trials
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.