REMoDNaV: robust eye-movement classification for dynamic stimulation

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 algo...
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Autor*in:	Dar, Asim H. [verfasserIn] Wagner, Adina S. [verfasserIn] Hanke, Michael [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Eye tracking Adaptive classification algorithm Saccade classification algorithm Statistical saccade analysis Glissade classification Adaptive threshold algorithm Data preprocessing

Übergeordnetes Werk:	Enthalten in: Behavior research methods, instruments & computers - Austin, Tex. : Psychonomic Society Publ., 1984, 53(2020), 1 vom: 24. Juli, Seite 399-414
Übergeordnetes Werk:	volume:53 ; year:2020 ; number:1 ; day:24 ; month:07 ; pages:399-414

Links:	Volltext

DOI / URN:	10.3758/s13428-020-01428-x

Katalog-ID:	SPR043150128

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source	Enthalten in Behavior research methods, instruments & computers 53(2020), 1 vom: 24. Juli, Seite 399-414 volume:53 year:2020 number:1 day:24 month:07 pages:399-414
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author	Dar, Asim H.
spellingShingle	Dar, Asim H. ddc 150 bkl 77.00 misc Eye tracking misc Adaptive classification algorithm misc Saccade classification algorithm misc Statistical saccade analysis misc Glissade classification misc Adaptive threshold algorithm misc Data preprocessing REMoDNaV: robust eye-movement classification for dynamic stimulation
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topic_title	150 ASE 77.00 bkl REMoDNaV: robust eye-movement classification for dynamic stimulation Eye tracking (dpeaa)DE-He213 Adaptive classification algorithm (dpeaa)DE-He213 Saccade classification algorithm (dpeaa)DE-He213 Statistical saccade analysis (dpeaa)DE-He213 Glissade classification (dpeaa)DE-He213 Adaptive threshold algorithm (dpeaa)DE-He213 Data preprocessing (dpeaa)DE-He213
topic	ddc 150 bkl 77.00 misc Eye tracking misc Adaptive classification algorithm misc Saccade classification algorithm misc Statistical saccade analysis misc Glissade classification misc Adaptive threshold algorithm misc Data preprocessing
topic_unstemmed	ddc 150 bkl 77.00 misc Eye tracking misc Adaptive classification algorithm misc Saccade classification algorithm misc Statistical saccade analysis misc Glissade classification misc Adaptive threshold algorithm misc Data preprocessing
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title	REMoDNaV: robust eye-movement classification for dynamic stimulation
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title_full	REMoDNaV: robust eye-movement classification for dynamic stimulation
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author_browse	Dar, Asim H. Wagner, Adina S. Hanke, Michael
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title_sort	remodnav: robust eye-movement classification for dynamic stimulation
title_auth	REMoDNaV: robust eye-movement classification for dynamic stimulation
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	REMoDNaV: robust eye-movement classification for dynamic stimulation
url	https://dx.doi.org/10.3758/s13428-020-01428-x
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author2	Wagner, Adina S. Hanke, Michael
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up_date	2024-07-03T16:54:11.655Z
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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. 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REMoDNaV: robust eye-movement classification for dynamic stimulation

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