ArticleFrontiers in cellular neuroscience2021
EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking.
Article in Frontiers in cellular neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Joint infrared pupil images and near-corneal-plane spectral light exposure data under natural conditions.Scientific data · 2026Article
- Designing research interfaces for non-human minds: a biocentric design approach for animal cognition research.Animal cognition · 2026Review
- High-Resolution Eye-Tracking System for Accurate Measurement of Short-Latency Ocular Following Responses: Development and Observational Study.JMIR pediatrics and parenting · 2024Article
- Open Iris - An Open Source Framework for Video-Based Eye-Tracking Research and Development.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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