Abstract
Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm—built on an existing velocity-based approach—that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources.
Highlights
A spreading theme in cognitive neuroscience is to use dynamic and naturalistic stimuli such as video clips or movies as opposed to static and isolated stimuli (Matusz et al, 2019)
Velocity-based algorithm for fixation, saccade, and PSO classification by Nystrom and Holmqvist (2010), we have developed an improved algorithm that performs robustly on prolonged or short recordings with dynamic stimulation, with potentially variable noise levels, and supports the classification of smooth pursuit events
Through a series of validation analyses, we have shown that its performance is comparable to or better than ten other contemporary algorithms, and that plausible classification results are achieved on high and lower quality data. These aspects of algorithm capabilities and performance suggest that REMoDNaV is a state-of-the-art tool for eye-movement classification with particular relevance for emerging complex data collections paradigms with dynamic stimulation, such as the combination of eye tracking and functional MRI in simultaneous measurements
Summary
A spreading theme in cognitive neuroscience is to use dynamic and naturalistic stimuli such as video clips or movies as opposed to static and isolated stimuli (Matusz et al, 2019). Using dynamic stimuli promises to observe the nuances of cognition in a more life-like environment. Special Lab Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg, Germany. Psychoinformatics Lab, Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Julich, Julich, Germany In order to disentangle different cognitive, oculomotor, or perceptive states associated with different types of eye movements, most research relies on the classification of eye-gaze data into distinct eye-movement
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