EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking

• Published in: Frontiers in cellular neuroscience Vol. 15; p. 779628
• Main Authors: Arvin, Simon, Rasmussen, Rune Nguyen, Yonehara, Keisuke
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 09.12.2021; Frontiers Media S.A
• Subjects: 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; oculographic tools; Python (programming language); software; eye movement abnormalities; eye movement; closed loop
• ISSN: 1662-5102; 1662-5102
• DOI: 10.3389/fncel.2021.779628

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.