EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking
Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design...
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| Published in | Frontiers in cellular neuroscience Vol. 15; p. 779628 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Research Foundation
09.12.2021
Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1662-5102 1662-5102 |
| DOI | 10.3389/fncel.2021.779628 |
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| Abstract | Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible. |
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| AbstractList | Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible. Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker - EyeLoop - that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop's utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker - EyeLoop - that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop's utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible. Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible. |
| Author | Rasmussen, Rune Nguyen Yonehara, Keisuke Arvin, Simon |
| AuthorAffiliation | 3 Department of Genetics, The Graduate University for Advanced Studies (SOKENDAI) , Mishima , Japan 1 Department of Biomedicine, Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus University , Aarhus , Denmark 2 Multiscale Sensory Structure Laboratory, National Institute of Genetics , Mishima , Japan |
| AuthorAffiliation_xml | – name: 1 Department of Biomedicine, Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus University , Aarhus , Denmark – name: 2 Multiscale Sensory Structure Laboratory, National Institute of Genetics , Mishima , Japan – name: 3 Department of Genetics, The Graduate University for Advanced Studies (SOKENDAI) , Mishima , Japan |
| Author_xml | – sequence: 1 givenname: Simon surname: Arvin fullname: Arvin, Simon – sequence: 2 givenname: Rune Nguyen surname: Rasmussen fullname: Rasmussen, Rune Nguyen – sequence: 3 givenname: Keisuke surname: Yonehara fullname: Yonehara, Keisuke |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34955752$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.16910/jemr.3.3.6 10.3389/fneur.2018.01029 10.1152/jn.00055.2017 10.1038/s41596-019-0176-0 10.1038/s41467-020-14643-z 10.1016/j.neures.2004.02.002 10.1159/000100348 10.1111/j.1444-0938.2010.00479.x 10.1126/sciadv.1500391 10.1063/1.3470126 10.1016/j.cub.2020.04.042 10.3389/fnint.2015.00021 10.1126/science.1077293 10.1016/j.visres.2013.09.009 10.1016/j.cub.2020.12.034 10.1016/j.neuron.2015.09.012 10.3389/fninf.2015.00011 10.1242/jeb.037945 10.1016/j.neuron.2015.11.032 10.1016/j.neuron.2015.12.031 10.1145/2168556.2168585 10.1098/rstb.1980.0089 10.1167/18.6.2 10.4103/2152-7806.82248 10.1371/journal.pone.0002055 10.1007/bf00337023 10.1101/457242 10.7554/eLife.54014 10.1136/bjo.65.11.754 10.1523/JNEUROSCI.5114-14.2015 10.1016/j.csda.2010.12.012 10.3758/bf03195489 10.1016/j.neuron.2015.03.034 |
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| Copyright | Copyright © 2021 Arvin, Rasmussen and Yonehara. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2021 Arvin, Rasmussen and Yonehara. 2021 Arvin, Rasmussen and Yonehara |
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| Keywords | oculographic tools Python (programming language) software eye movement abnormalities eye movement closed loop |
| Language | English |
| License | Copyright © 2021 Arvin, Rasmussen and Yonehara. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. cc-by |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: Frank Helbert Borsato, Federal Technological University of Paraná, Brazil; Björn Kampa, RWTH Aachen University, Germany Present address: Rune Nguyen Rasmussen, Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience Edited by: Daniel Hillier, Hungarian Academy of Sciences (MTA), Hungary |
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| SubjectTerms | Algorithms Cameras Cellular Neuroscience closed loop Cornea Experiments eye movement eye movement abnormalities Nervous system oculographic tools Open source software Proprietary Python (programming language) software User interface |
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| Title | EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking |
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