SacLab: An eye-tracking analysis toolbox to increase usability of eye-tracking systems in clinical and research applications

• DOI: 10.31234/osf.io/7fkp3_v2
• Publication Date: 2025-02-03T13:31:12Z
• Authors: Joshua Langmead; Kelsey Perrykkad; Elana Forbes; Kathryn Unruh; Matthew Mosconi; Michael Wybrow; Dalia Maron; Bryce Fleming; Rebecca Kerestes; Mark Bellgrove; Beth P Johnson
• Keywords: Toolbox; Tracking (education)

A short period of eye-tracking can produce large amounts data, making analysis challenging, particularly for clinical applications. Automated methods extracting basic oculomotor features saccades, fixation, or blinking are available. For more in-depth analysis, manual cleaning and processing is often required. This time-consuming variable across individuals over time, which limits scalability utility, even if identified markers have high potential significance. Automation provides an opportunity to reduce labour improve replicability precision through algorithmic approaches. Here we outline the design validation our SacLab Toolbox data from well-established paradigms commonly used in research such as prosaccade, antisaccade, smooth pursuit tasks. prosaccade antisaccade tasks, results showed good agreement between regarding trials were be included (86% 91% with F1-scores 0.89 0.79, respectively). The toolbox was also tested on both tasks independent laboratory a different protocol, level remained (87% 93% 0.93 0.94, extent concordance metrics extracted discussed. predominant reason discrepancy process related human error process. Overall, provide support benefits this these