EyeLoop: An open-source system for high-speed, closed-loop eye-tracking

TY - JOUR

T1 - EyeLoop: An open-source system for high-speed, closed-loop eye-tracking

AU - Arvin, Simon

AU - Rasmussen, Rune Nguyen

AU - Yonehara, Keisuke

PY - 2021/12/9

Y1 - 2021/12/9

N2 - 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.

AB - 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.

KW - Python (programming language)

KW - closed loop

KW - eye movement

KW - eye movement abnormalities

KW - oculographic tools

KW - software

UR - http://www.scopus.com/inward/record.url?scp=85121628852&partnerID=8YFLogxK

U2 - 10.3389/fncel.2021.779628

DO - 10.3389/fncel.2021.779628

M3 - Journal article

C2 - 34955752

SN - 1662-5102

VL - 15

JO - Frontiers in Cellular Neuroscience

JF - Frontiers in Cellular Neuroscience

M1 - 779628

ER -