Anatomy of an eyeris Object

knitr::opts_chunk$set(
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📦 Key Components

When preprocessing .asc EyeLink files with eyeris, returned objects will be of the class eyeris, and will contain key components used throughout the package's backend.

The key components are:

You'll notice that for each preprocessing step run, a new column will be added after the pupil_raw column; these new columns follow a structure where each subsequent step is appended to the previous columns name (i.e., pupil_raw_{previous steps}_{current_step}). To illustrate:

💡 Note: This vignette describes the structure of an `eyeris` object as returned by the main preprocessing pipeline. 1. Understanding these components will help you interpret results, debug issues, and extend the pipeline for your own research needs. 2. Furthermore, binocular data will have a `left` and `right` component to the `eyeris` object, which will contain the same components as the main `eyeris` object. 3. There is also a phantom `raw_binocular_object` component to the `eyeris` object, which contains the raw binocular data used internally to perform the binocular correlation analysis and plotting.

Now that we've explained what you can expect to see after running the eyeris glassbox() function, we'll demonstrate what the glassbox() wrapper is generally comprised of in terms of the steps and defaults that are implemented.

🧱 Building Blocks Under the Hood

While we strongly recommend against manually constructing the pipeline as will be shown below (given that using the glassbox() will provide maximum opportunities for reproducibility and reduction of accidental errors), more advanced users may want to see how the individual steps can be used like building blocks to iteratively test out parameters, switch steps around / remove steps (again, we strongly recommend against doing this unless you know what you're doing), etc.

The Default glassbox() Steps and Parameters, Deconstructed:

system.file("extdata", "memory.asc", package = "eyeris") |>
  eyeris::load_asc(block = "auto") |>
  eyeris::deblink(extend = 50) |>
  eyeris::detransient(n = 16) |>
  eyeris::interpolate() |>
  eyeris::lpfilt(wp = 4, ws = 8, rp = 1, rs = 35, plot_freqz = TRUE) |>
  # eyeris::downsample() |>  # optional (please read docs before enabling)
  # eyeris::bin() |>  # optional (please read docs before enabling)
  # eyeris::detrend() |>  # optional (please read docs before enabling)
  eyeris::zscore() |>
  eyeris::summarize_confounds()
💡 **For more detailed information on the implementation of functions within** **the `glassbox()` and thus how to create your own custom pipeline** **extensions that conform to the `eyeris` protocol, see the:** [🧩 Building your own Custom Pipeline Extensions vignette](custom-extensions.html).

📚 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")


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eyeris documentation built on Aug. 8, 2025, 7:51 p.m.