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.
Highlights
At every moment, the brain uses its senses to produce increasingly complex features that describe its external world (Ehinger et al, 2015; Zeki, 2015)
Eye-tracking is widely used in neuroscience, from studying brain dynamics to investigating neuropathology and disease models (Yonehara et al, 2016; Wang et al, 2018; Meyer et al, 2020)
Frmd7TM mice are homozygous female or hemizygous male Frmd7tm1b(KOMP)Wtsi mice, which were obtained as Frmd7tm1a(KOMP)Wtsi from the Knockout Mouse Project (KOMP) Repository, Exon 4 and neo cassette flanked by loxP sequences were removed by crossing with female Cre-deleter Edil3T g(Sox2−cre)1Amc/J mice (Jackson laboratory stock 4,783) as confirmed by PCR of genome DNA and maintained in a C57BL/6J background
Summary
The brain uses its senses to produce increasingly complex features that describe its external world (Ehinger et al, 2015; Zeki, 2015). Eye-tracking is widely used in neuroscience, from studying brain dynamics to investigating neuropathology and disease models (Yonehara et al, 2016; Wang et al, 2018; Meyer et al, 2020). Current systems tend to be programmatically rigid, e.g., by being compiled into executable, proprietary software unavailable for modifications, or coded in a more complex syntax and system architecture with advanced software modules. To address these issues, we developed an opensource eye-tracker – EyeLoop – tailored to investigating visual dynamics at very high speeds. EyeLoop enables low-resource facilities access to eye-tracking and encourages community-based development of software code through a modular, tractable algorithm based on high-level Python 3
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