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Analysis of eye tracking movements using innovations generated by a Kalman filter
D Sauter1, B J Martin, N Di Renzo
1Centre de Recherche en Automatique de Nancy, UA CNRS 821, Vandoeuvre, France.
Medical & Biological Engineering & Computing
|January 1, 1991
Summary
A new algorithm efficiently analyzes eye tracking movements by separating smooth pursuit and saccadic eye movements. This method uses an AR model and Kalman filter for reliable saccade detection, applicable to various eye movements.
Area of Science:
- Ophthalmology
- Neuroscience
- Computer Science
Background:
- Accurate analysis of eye movements is crucial for understanding visual perception and neurological function.
- Existing methods for separating smooth pursuit and saccadic eye movements can be complex and computationally intensive.
Purpose of the Study:
- To develop a simple yet efficient algorithm for computer-based analysis of eye tracking data.
- To accurately differentiate between smooth pursuit and saccadic eye movements.
Main Methods:
- A two-step saccade detection process was employed.
- An Autoregressive (AR) model identified the velocity of the smooth component to determine a Kalman filter.
- The Kalman filter's innovation sequence and a Hinkley algorithm precisely located saccade onset and offset.
Main Results:
- The algorithm successfully separated smooth pursuit and saccadic eye movements.
- Precise identification of saccade start and end points was achieved.
- The method demonstrated high reliability in analyzing eye movements during random target tracking.
Conclusions:
- The developed algorithm provides an efficient and reliable method for analyzing eye tracking movements.
- The approach is robust and can be extended to analyze other types of eye movements, such as nystagmus.