Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states (see Clewett et al., 2020 <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie, 2018 <doi:10.3758/s13428-018-1075-y>; Strauch, 2024 <doi:10.1016/j.tins.2024.06.002>). Yet, despite decades of pupillometry research, many established packages and workflows to date unfortunately lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles (see Wilkinson et al., 2016 <doi:10.1038/sdata.2016.18> for more information). 'eyeris', on the other hand, follows a design philosophy that provides users with an intuitive, modular, performant, and extensible pupillometry data preprocessing framework out-of-the-box. 'eyeris' introduces a Brain Imaging Data Structure (BIDS)-like organization for derivative (i.e., preprocessed) pupillometry data as well as an intuitive workflow for inspecting preprocessed pupil epochs using interactive output report files (Esteban et al., 2019 <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al., 2016 <doi:10.1038/sdata.2016.44>).
Package details |
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Author | Shawn Schwartz [aut, cre, cph] (<https://orcid.org/0000-0001-6444-8451>) |
Maintainer | Shawn Schwartz <stschwartz@stanford.edu> |
License | MIT + file LICENSE |
Version | 1.0.0 |
URL | https://shawnschwartz.com/eyeris/ https://github.com/shawntz/eyeris/ |
Package repository | View on CRAN |
Installation |
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