REMoDNaV: robust eye-movement classification for dynamic stimulation

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 ar...

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Veröffentlicht in:	Behavior research methods Jg. 53; H. 1; S. 399 - 414
Hauptverfasser:	Dar, Asim H., Wagner, Adina S., Hanke, Michael
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2021 Springer Nature B.V
Schlagworte:	Algorithms Behavioral Science and Psychology Classification Cognitive Psychology Eye Eye fixation Eye Movements Humans Magnetic resonance imaging Measurement Motion pictures Oscillations Photic Stimulation Programming languages Psychology Pursuit, Smooth Robustness Saccades Saccadic eye movements Sequences Smooth pursuit eye movements Software Stimulation Stimuli Tracking Visual stimuli Glissade classification Data preprocessing Saccade classification algorithm Adaptive threshold algorithm Eye tracking Statistical saccade analysis Adaptive classification algorithm
ISSN:	1554-3528, 1554-351X, 1554-3528
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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.
AbstractList	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.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. 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.
Author	Hanke, Michael Wagner, Adina S. Dar, Asim H.
Author_xml	– sequence: 1 givenname: Asim H. surname: Dar fullname: Dar, Asim H. organization: Special Lab Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology – sequence: 2 givenname: Adina S. surname: Wagner fullname: Wagner, Adina S. organization: Psychoinformatics Lab, Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich – sequence: 3 givenname: Michael orcidid: 0000-0001-6398-6370 surname: Hanke fullname: Hanke, Michael email: michael.hanke@gmail.com organization: Psychoinformatics Lab, Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32710238$$D View this record in MEDLINE/PubMed
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Keywords	Glissade classification Data preprocessing Saccade classification algorithm Adaptive threshold algorithm Eye tracking Statistical saccade analysis Adaptive classification algorithm
Language	English
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Snippet	Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made...
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SubjectTerms	Algorithms Behavioral Science and Psychology Classification Cognitive Psychology Eye Eye fixation Eye Movements Humans Magnetic resonance imaging Measurement Motion pictures Oscillations Photic Stimulation Programming languages Psychology Pursuit, Smooth Robustness Saccades Saccadic eye movements Sequences Smooth pursuit eye movements Software Stimulation Stimuli Tracking Visual stimuli
Title	REMoDNaV: robust eye-movement classification for dynamic stimulation
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