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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Bibliographic Details
Published in:Behavior research methods Vol. 53; no. 1; pp. 399 - 414
Main Authors: Dar, Asim H., Wagner, Adina S., Hanke, Michael
Format: Journal Article
Language:English
Published: New York Springer US 01.02.2021
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:1554-3528
1554-351X
1554-3528
DOI:10.3758/s13428-020-01428-x