54 lines
No EOL
2.9 KiB
Markdown
54 lines
No EOL
2.9 KiB
Markdown
---
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title: 'REMoDNaV: Robust Eye-Movement Classification for Dynamic Stimulation'
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persons:
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- adina-wagner
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- 10847cce-cba3-415e-aa84-e681e9727697
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- michael-hanke
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topics:
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- naturalistic-neuroimaging
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- research-software-engineering
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params:
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graphRootNodePID: xyzrins:publications/4ea5c19f-d116-470f-a31c-dff14912c7b2
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pid: xyzrins:publications/4ea5c19f-d116-470f-a31c-dff14912c7b2
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doi: 10.3758/s13428-020-01428-x
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date: '2020-07-24'
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title: 'REMoDNaV: Robust Eye-Movement Classification for Dynamic Stimulation'
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description: "Tracking of eye movements is an established measurement for many types\
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\ of experimental paradigms. More complex and more prolonged visual stimuli have\
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\ made algorithmic approaches to eye-movement event classification the most pragmatic\
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\ option. A recent analysis revealed that many current algorithms are lackluster\
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\ when it comes to data from viewing dynamic stimuli such as video sequences. Here\
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\ we present an event classification algorithm\u2014built on an existing velocity-based\
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\ approach\u2014that is suitable for both static and dynamic stimulation, and is\
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\ capable of classifying saccades, post-saccadic oscillations, fixations, and smooth\
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\ pursuit events. We validated classification performance and robustness on three\
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\ public datasets: 1) manually annotated, trial-based gaze trajectories for viewing\
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\ static images, moving dots, and short video sequences, 2) lab-quality gaze recordings\
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\ for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting\
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\ conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the\
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\ same full-length movie. We found that the proposed algorithm performs on par or\
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\ better compared to state-of-the-art alternatives for static stimulation. Moreover,\
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\ it yields eye-movement events with biologically plausible characteristics on prolonged\
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\ dynamic recordings. Lastly, algorithm performance is robust on data acquired under\
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\ suboptimal conditions that exhibit a temporally varying noise level. These results\
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\ indicate that the proposed algorithm is a robust tool with improved classification\
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\ accuracy across a range of use cases. The algorithm is cross-platform compatible,\
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\ implemented using the Python programming language, and readily available as free\
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\ and open-source software from public sources."
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kind: bibo:AcademicArticle
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author:
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- pid: xyzrins:persons/adina-wagner
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given_name: Adina
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family_name: Wagner
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- pid: xyzrins:persons/10847cce-cba3-415e-aa84-e681e9727697
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given_name: Asim
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family_name: Dar
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- pid: xyzrins:persons/michael-hanke
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given_name: Michael
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family_name: Hanke
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topic:
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- pid: xyzrins:topics/naturalistic-neuroimaging
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display_label: Naturalistic neuroimaging
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- pid: xyzrins:topics/research-software-engineering
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display_label: Research software engineering (RSE)
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--- |