Abstract Eye-tracking is a method for tracking the position of the eye and size of the pupil, often employed in neuroscience laboratories and clinics. Eye-trackers are widely used, from studying brain dynamics to investigating neuropathology and disease models. Despite this broad utility, eye-trackers are expensive, hardware-intensive, and proprietary, which have limited this approach to high-resource facilities. Besides, experiments have largely been confined to static open-loop designs and post hoc analysis due to the inflexibility of current systems. Here, we developed an open-source eye-tracking system, named EyeLoop, tailored to dynamic experiments. This Python-based software easily integrates custom functions via a modular logic, tracks a multitude of eyes, including rodent, human, and non-human primate eyes, and it operates well on inexpensive consumer-grade hardware. One of the most appealing applications of EyeLoop is closed-loop experiments, in which the eyes evoke stimulus feedback, such as rapid neuronal optogenetic stimulation. By using EyeLoop, we demonstrate its utility in an open-loop, a closed-loop, and a biomedical experiment. With a remarkably low minimal hardware cost amounting to 29 USD, EyeLoop makes dynamic eye-tracking accessible to low-resource facilities, such as high schools, small laboratories, and small clinics.

EyeLoop: An open-source, high-speed eye-tracker designed for dynamic experiments