Our proposed platform is a comprehensive solution for performing accurate, verifiable and reproducible pupil examinations, competitive with high-end commercial stereo eye-tracking systems. Therefore, we developed a freely available hardware and software platform for pupil measurements to support the increased interest of interdisciplinary research groups in studying the pupil behavior. Apart from commercial solutions, there is currently a lack of an end-to-end open-source measurement platform that can be easily set up for high-precision pupillometry under laboratory conditions. Additionally, with closed systems, it is not possible to identify the applied pupil detection algorithm, making it challenging to reproduce experiments since small inaccuracies in a range of 0.01 mm could propagate errors to the statistical evaluation of the pupil diameter. Closed commercial eye-tracking systems are common in pupil examinations, associated with high investments without offering the possibilities of validating the pupil detection’s measurement accuracy. Pupil changes of 0.015 to 0.5 mm are the range of interest in such studies, leading to increased resolution and robustness requirements for pupil measurement equipment. Additionally, the pupil diameter is used as a biomarker in research disciplines such as cognitive science ( Aminihajibashi et al., 2020 Cherng et al., 2020 Clewett et al., 2020 Sibley et al., 2020), circadian photoentrainment ( Münch et al., 2012 Bonmati-Carrion et al., 2016 Spitschan et al., 2019 Tähkämö et al., 2019 Van Egroo et al., 2019), clinical diagnostics ( Lim et al., 2016 Joyce et al., 2018 Chougule et al., 2019) or neuroscience ( Schwalm and Jubal, 2017 Carle et al., 2019). Since the early days of pupillary research ( Reeves, 1918), the modeling of the pupil light response and its retinal processing path was the main focus of investigations ( Zandi and Khanh, 2021). The pupil diameter is an essential metric in visual neuroscience, as it has a direct impact on the retinal irradiance, visual acuity and visual performance of the eye ( Campbell, 1957 Campbell and Gubisch, 1966 Woodhouse, 1975 Schwiegerling, 2000).
The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at.
A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone.
Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Diameter changes in the range of 10 –2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes.
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