A "glassbox" pipeline wrapper for eyeris

This glassbox function (in contrast to a "blackbox" function where you run it and get a result but have no (or little) idea as to how you got from input to output) has a few primary benefits over calling each exported function from eyeris separately.

Usage

glassbox(
  file,
  confirm = FALSE,
  detrend_data = FALSE,
  num_previews = 3,
  preview_duration = 5,
  preview_window = NULL,
  ...
)

Arguments

file: An SR Research EyeLink .asc file generated by the official EyeLink edf2asc command.
confirm: A flag to indicate whether to run the glassbox pipeline autonomously all the way through (set to FALSE by default), or to interactively provide a visualization after each pipeline step, where you must also indicate "(y)es" or "(n)o" to either proceed or cancel the current glassbox pipeline operation (set to TRUE).
detrend_data: A flag to indicate whether to run the detrend step (set to FALSE by default). Detrending your pupil timeseries can have unintended consequences; we thus recommend that users understand the implications of detrending – in addition to whether detrending is appropriate for the research design and question(s) – before using this function.
num_previews: Number of random example "epochs" to generate for previewing the effect of each preprocessing step on the pupil timeseries.
preview_duration: Time in seconds of each randomly selected preview.
preview_window: The start and stop raw timestamps used to subset the preprocessed data from each step of the eyeris workflow for visualization. Defaults to NULL, meaning random epochs as defined by num_previews and preview_duration will be plotted. To override the random epochs, set preview_window here to a vector with relative start and stop times (e.g., c(5000, 6000) to indicate the raw data from 5-6 seconds on data that were recorded at 1000 Hz). Note, the start/stop time values indicated here relate to the raw index position of each pupil sample from 1 to n (which will need to be specified manually by the user depending on the sampling rate of the recording; i.e., 5000-6000 for the epoch positioned from 5-6 seconds after the start of the timeseries, sampled at 1000 Hz).
...: Additional arguments to override the default, prescribed settings.

Details

First, this glassbox function provides a highly opinionated prescription of steps and starting parameters we believe any pupillometry researcher should use as their defaults when preprocessing pupillometry data.

Second, and not mutually exclusive from the first point, using this function should ideally reduce the probability of accidental mishaps when "reimplementing" the steps from the preprocessing pipeline both within and across projects. We hope to streamline the process in such a way that you could collect a pupillometry dataset and within a few minutes assess the quality of those data while simultaneously running a full preprocessing pipeline in 1-ish line of code!

Third, glassbox provides an "interactive" framework where you can evaluate the consequences of the parameters within each step on your data in real time, facilitating a fairly easy-to-use workflow for parameter optimization on your particular dataset. This process essentially takes each of the opinionated steps and provides a pre-/post-plot of the timeseries data for each step so you can adjust parameters and re-run the pipeline until you are satisfied with the choices of your paramters and their consequences on your pupil timeseries data.

Examples

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

# (1) examples using the default prescribed parameters and pipeline recipe

## (a) run an automated pipeline with no real-time inspection of parameters
output <- eyeris::glassbox(demo_data)
#> ✔ [  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()

plot(
  output,
  steps = c(1, 5),
  preview_window = c(0, nrow(output$timeseries)),
  seed = 0
)



## (b) run a interactive workflow (with confirmation prompts after each step)
# output <- eyeris::glassbox(demo_data, confirm = TRUE, seed = 0)


# (2) examples overriding the default parameters
output <- eyeris::glassbox(
  demo_data,
  confirm = FALSE, # TRUE if you want to visualize each step in real-time
  deblink = list(extend = 40),
  lpfilt = list(plot_freqz = FALSE)
)
#> ✔ [  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()

plot(output, seed = 0)