| First Author | Arvin S | Year | 2021 |
| Journal | Front Cell Neurosci | Volume | 15 |
| Pages | 779628 | PubMed ID | 34955752 |
| Mgi Jnum | J:320182 | Mgi Id | MGI:6841486 |
| Doi | 10.3389/fncel.2021.779628 | Citation | Arvin S, et al. (2021) EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking. Front Cell Neurosci 15:779628 |
| abstractText | 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. |
