Motivation

Despite decades of pupillometry research, many established packages and workflows unfortunately lack design principles based on (F)indability (A)ccessbility (I)nteroperability (R)eusability (FAIR) principles. eyeris, on the other hand follows a thoughtful design philosophy that results in an intuitive, modular, performant, and extensible pupillometry data preprocessing framework. Much of these design principles were heavily inspired by Nipype.

eyeris also provides a highly opinionated pipeline for tonic and phasic pupillometry preprocessing (inspired by fMRIPrep). These opinions are the product of many hours of discussions from core members and signal processing experts from the Stanford Memory Lab (Shawn Schwartz, Mingjian He, Haopei Yang, Alice Xue, and Anthony Wagner).

eyeris also introduces a BIDS-like structure for organizing derivative (preprocessed) pupillometry data, as well as an intuitive workflow for inspecting preprocessed pupillometry epochs within beautiful, interactive HTML report files (see demonstration below ⬇️)!

Installation

You can install the development version of eyeris from GitHub with:

# install.packages("devtools")
devtools::install_github("shawntz/eyeris")

# install.packages("pak")
pak::pak("shawntz/eyeris")

Example

the `glassbox()` “prescription” function

This is a basic example of how to use eyeris out of the box with our very opinionated set of steps and parameters that one should start out with when preprocessing pupillometry data. Critically, this is a “glassbox” – as opposed to a “blackbox” – since each step and parameter implemented herein is fully open and accessible to you. We designed each pipeline step / function to be like legos – they are intentionally and carefully designed in a way that allows you to flexibly construct and compare different pipelines.

We hope you enjoy! -shawn

set.seed(32)

library(eyeris)

demo_data <- system.file("extdata", "memory.asc", package = "eyeris")

eyeris_preproc <- glassbox(
  demo_data,
  detrend_data = F,
  lpfilt = list(plot_freqz = T)
)
#> ✔ [  OK  ] - Running eyeris::load_asc()
#> ✔ [  OK  ] - Running eyeris::deblink()
#> ✔ [  OK  ] - Running eyeris::detransient()
#> ✔ [  OK  ] - Running eyeris::interpolate()
#> ✔ [  OK  ] - Running eyeris::lpfilt()
#> ✔ [  OK  ] - Skipping eyeris::detrend()
#> ✔ [  OK  ] - Running eyeris::zscore()

step-wise correction of pupillary signal

plot(eyeris_preproc)
#> ! Plotting block 1 from possible blocks: 1

glassbox timeseries animation glassbox histograms animation

final pre-post correction of pupillary signal (raw ➡ preprocessed)

plot(eyeris_preproc,
  steps = c(1, 5),
  preview_window = c(0, nrow(eyeris_preproc$timeseries$block_1))
)
#> ! Plotting block 1 from possible blocks: 1

`eyeris` dependency graph 🙈

Suggestions, questions, issues?

Please use the issues tab (https://github.com/shawntz/eyeris/issues) to make note of any bugs, comments, suggestions, feedback, etc… all are welcomed and appreciated, thanks!

Code of Conduct

Please note that the eyeris project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

📚 Citing `eyeris`

If you use the eyeris package in your research, please cite it!

Run the following in R to get the citation:

citation("eyeris")
#> To cite package 'eyeris' in publications use:
#> 
#>   Schwartz S (2025). _eyeris: Flexible, Extensible, & Reproducible
#>   Processing of Pupil Data_. R package version 1.0.0,
#>   https://github.com/shawntz/eyeris/,
#>   <https://shawnschwartz.com/eyeris/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {eyeris: Flexible, Extensible, & Reproducible Processing of Pupil Data},
#>     author = {Shawn Schwartz},
#>     year = {2025},
#>     note = {R package version 1.0.0, https://github.com/shawntz/eyeris/},
#>     url = {https://shawnschwartz.com/eyeris/},
#>   }

eyeris: A Flexible, Extensible, and Reproducible Pupillometry Preprocessing Framework in R