Abstract
A new method for detecting microsaccades in eye-movement data is presented, following a review of reported microsaccade properties between the 1940s and today. The review focuses on the parameter ranges within which certain physical markers of microsaccades are thought to occur, as well as any features of microsaccades that have been stably reported over time. One feature of microsaccades, their binocularity, drives the new microsaccade detection method. The binocular correlation method for microsaccade detection is validated on two datasets of binocular eye-movements recorded using video-based systems: one collected as part of this study, and one from Nyström et al, 2017. Comparisons between detection methods are made using precision-recall statistics. This confirms that the binocular correlation method performs well when compared to manual coders and performs favourably compared to the commonly used Engbert & Kliegl (2003) method with subsequent modifications (Engbert & Mergenthaler, 2006). The binocular correlation microsaccade detection method is easy to implement and MATLAB code is made available to download.
Highlights
Microsaccades have been the object of systematic study for over 70 years and still their correct identification remains difficult
These include (i) limbus trackers, which track the change in intensity of light reflected from the between the iris-sclera boundary (e.g. Tse, Baumgartner, & Greenlee, 2010; Zuber & Stark, 1965), but often have low spatial resolution; (ii) search coils, which record the current induced by an external magnetic field in a coil of wire worn on the eye (e.g. Winterson & Collewijn, 1976), but still require large, uncomfortable lenses to be worn; and (iii) Dual Purkinje Image (DPI) trackers, which compare the first and fourth Purkinje reflections to calculate eye position (e.g. Rucci & Desbordes, 2003; Snodderly, 1987)
In the future microsaccade detection has the potential to benefit from the accurate reporting of the physical parameters of microsaccades
Summary
Microsaccades have been the object of systematic study for over 70 years and still their correct identification remains difficult. Over time the focus of research on microsaccades has shifted, from trying to learn about their properties and purpose, to using them as a measure of cognitive processes such as covert attention (Engbert & Kliegl, 2003; Hafed & Clark, 2002; Laubrock, Engbert, & Kliegl, 2005). With an increase in the number of published papers on microsaccades (see Fig. 1) the central problem of how to detect microsaccades in a stream of eye-position samples is more important than ever. A review of 70 years of research prompts a new detection method.
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