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Definition, Modeling, and Detection of Saccades in the Face of Post-saccadic Oscillations

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Eye Tracking

Part of the book series: Neuromethods ((NM,volume 183))

Abstract

When analyzing eye tracking data, one of the central tasks is the detection of saccades. Although many automatic saccade detection algorithms exist, the field still debates how to deal with brief periods of instability around saccade offset, so-called post-saccadic oscillations (PSOs), which are especially prominent in today’s widely used video-based eye tracking techniques. There is good evidence that PSOs are caused by inertial forces that act on the elastic components of the eye, such as the iris or the lens. As this relative movement can greatly distort estimates of saccade metrics, especially saccade duration and peak velocity, video-based eye tracking has recurrently been considered unsuitable for measuring saccade kinematics. In this chapter, we review recent biophysical models that describe the relationship between pupil motion and eyeball motion. We found that these models were well capable of accurately reproducing saccade trajectories and we implemented a we framework for the simulation of saccades, PSOs, and fixations, which can be used—just like datasets hand-labeled by human experts—to evaluate detection algorithms and train statistical models. Moreover, as only pupil and corneal-reflection signals are observable in video-based eye tracking, one may also be able to use these models to predict the unobservable motion of the eyeball. Testing these predictions by analyzing saccade data that was registered with video-based and search-coil eye tracking techniques revealed strong relationships between the two types of measurements, especially when saccade offset is defined as the onset of the PSO. To enable eye tracking researchers to make use of this definition, we present and evaluate two novel algorithms: one based on eye movement direction inversion and one based on linear classifiers previously trained on simulation data. These algorithms allow for the detection of PSO onset with high fidelity. Even though PSOs may still pose problems for a range of eye tracking applications, the techniques described here may help to alleviate these.

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Acknowledgments

We thank Jan Drewes, Guillaume Masson, and Anna Montagnini for sharing their co-registered search-coil and video-based eye tracking data. Furthermore, we thank Markus Nyström and Richard Andersson and Anna-Katharina Hauperich and colleagues for making their annotated datasets publicly available, as well as Ralf Engbert, Petra Sinn, Konstantin Mergenthaler, and Hans Trukenbrod for their R implementation of the Microsaccade Toolbox (http://read.psych.uni-potsdam.de/attachments/article/140/MS_Toolbox_R.zip). We thank Antonio Sánchez Garcia for his help with the Phantom high-speed camera and Sven Ohl and Wiebke Nörenberg for volunteering to provide feedback on an earlier version of the chapter. R.S. and M.R. were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135. M.R. was supported by the Deutsche Forschungsgemeinschaft (DFG; grants RO3579/8-1 and RO3579/12-1),as well as the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 865715).

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Schweitzer, R., Rolfs, M. (2022). Definition, Modeling, and Detection of Saccades in the Face of Post-saccadic Oscillations. In: Stuart, S. (eds) Eye Tracking. Neuromethods, vol 183. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2391-6_5

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  • DOI: https://doi.org/10.1007/978-1-0716-2391-6_5

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