Arvin Simon, Rasmussen Rune Nguyen, Yonehara Keisuke
Department of Biomedicine, Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark.
Multiscale Sensory Structure Laboratory, National Institute of Genetics, Mishima, Japan.
Front Cell Neurosci. 2021 Dec 9;15:779628. doi: 10.3389/fncel.2021.779628. eCollection 2021.
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.
眼动追踪仪被广泛用于研究神经系统动力学和神经病理学。尽管具有广泛的实用性,但眼动追踪仍然昂贵、硬件要求高且具有专有性,这限制了其仅能在资源丰富的设施中使用。它也不容易实现实时分析和闭环设计,难以将眼动与神经活动联系起来。为了解决这些问题,我们开发了一种开源眼动追踪仪——EyeLoop,它使用一种高效的矢量化瞳孔检测方法,能够提供不间断的追踪和快速的在线分析,其高精度可与诸如DeepLabCut等流行的眼动追踪模块相媲美。这种基于Python的软件使用代码模块轻松集成自定义功能,可追踪包括啮齿动物、人类和非人类灵长类动物在内的多只眼睛,并且在消费级硬件上以每秒1000多帧的速度运行。在本文中,我们展示了EyeLoop在开环实验和生物医学疾病识别这两种常见的眼动追踪应用中的实用性。EyeLoop成本极低且设置步骤最少,使高速眼动追踪广泛可用。