bin: Bin pupil time series by averaging within time bins

In eyeris: Flexible, Extensible, & Reproducible Pupillometry Preprocessing

Bin pupil time series by averaging within time bins

Description

This function bins pupillometry data by dividing time into equal intervals and averaging the data within each bin. Unlike downsampling, binning averages data points within each time bin.

Usage

bin(eyeris, bins_per_second, method = "mean", call_info = NULL)

Arguments

eyeris	An object of class eyeris derived from load_asc()
bins_per_second	The number of bins to create per second of data
method	The binning method: "mean" (default) or "median"
call_info	A list of call information and parameters. If not provided, it will be generated from the function call. Defaults to NULL

Details

Binning divides one second of pupillary data into X bins and averages pupillometry data around each bin center. The resulting time points will be: 1/2X, 3/2X, 5/2X, ..., etc. where X is the number of bins per second.

This approach is commonly used in pupillometry research to study temporal dynamics of pupil dilatory response; however, it should be used with caution (as averaging within bins can distort the pupillary dynamics).

Value

An eyeris object with binned data and updated sampling rate

Note

This function is part of the glassbox() preprocessing pipeline and is not intended for direct use in most cases. Provide parameters via bin = list(...).

Advanced users may call it directly if needed.

See Also

glassbox() for the recommended way to run this step as part of the full eyeris glassbox preprocessing pipeline downsample() for downsampling functionality

Examples

demo_data <- eyelink_asc_demo_dataset()

# bin data into 10 bins per second using the (default) "mean" method
demo_data |>
  eyeris::glassbox(bin = list(bins_per_second = 10, method = "mean")) |>
  plot(seed = 0)

eyeris documentation built on July 3, 2025, 9:08 a.m.