ArticleJournal of vision2021
Detection of normal and slow saccades using implicit piecewise polynomial approximation.
Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- iPad Eye Tracking Reproduces Clinical Grade Oculomotor Differences in Parkinson's Disease.NPJ digital medicine · 2026Article
- Denoising eye movement signals using a novel signal processing framework to preserve oculomotor integrity in dyslexia.Scientific reports · 2026Article
- Towards scalable screening for the early detection of Parkinson's disease: validation of an iPad-based eye movement assessment system against a clinical-grade eye tracker.NPJ Parkinson's disease · 2025Article
- Eye tracking is more sensitive than skin conductance response in detecting mild environmental stimuli.PNAS nexus · 2024Article
- A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algorithms are not designed to detect slow saccades, and are correspondingly unreliable when saccades are unusually slow. In this article, we propose an algorithm that is effective for the detection of both normal and slow saccades. The proposed algorithm is partly based on modeling saccadic waveforms as piecewise-quadratic signals. The algorithm first decreases noise in acquired eye-tracking data using optimization to minimize a prescribed objective function, then uses velocity thresholding to detect saccades. Using both simulated saccades and real saccades generated by healthy subjects and patients, we evaluate the performance of the proposed algorithm and 10 other detection algorithms. We show the proposed algorithm is more accurate in detecting both normal and slow saccades than other algorithms.
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