detransient: Remove pupil samples that are physiologically unlikely

In eyeris: Flexible, Extensible, & Reproducible Pupillometry Preprocessing

Remove pupil samples that are physiologically unlikely

Description

The intended use of this method is for removing pupil samples that emerge more quickly than would be physiologically expected. This is accomplished by rejecting samples that exceed a "speed"-based threshold (i.e., median absolute deviation from sample-to-sample). This threshold is computed based on the constant n, which defaults to the value 16.

Usage

detransient(eyeris, n = 16, mad_thresh = NULL, call_info = NULL)

Arguments

eyeris	An object of class eyeris derived from load_asc()
n	A constant used to compute the median absolute deviation (MAD) threshold. Defaults to 16
mad_thresh	Default NULL. This parameter provides alternative options for handling edge cases where the computed properties here within detransient() mad\_val and median\_speed are very small. For example, if mad\_val = 0 \quad \text{and} \quad median\_speed = 1, then, with the default multiplier n = 16, mad\_thresh = median\_speed + (n \times mad\_val) = 1 + (16 \times 0) = 1. In this situation, any speed p_i \ge 1 would be flagged as a transient, which might be overly sensitive. To reduce this sensitivity, two possible adjustments are available: If mad\_thresh = 1, the transient detection criterion is modified from p_i \ge mad\_thresh to p_i > mad\_thresh . If mad\_thresh is very small, the user may manually adjust the sensitivity by supplying an alternative threshold value here directly via this mad_thresh parameter.
call_info	A list of call information and parameters. If not provided, it will be generated from the function call. Defaults to NULL

Details

This function is automatically called by glassbox() by default. If needed, customize the parameters for detransient by providing a parameter list. Use glassbox(detransient = FALSE) to disable this step as needed.

Users should prefer using glassbox() rather than invoking this function directly unless they have a specific reason to customize the pipeline manually.

Computed properties:

• pupil_speed: Compute speed of pupil by approximating the derivative of x (pupil) with respect to y (time) using finite differences.

  • Let x = (x_1, x_2, \dots, x_n) and y = (y_1, y_2, \dots, y_n) be two numeric vectors with n \ge 2; then, the finite differences are computed as:

    \delta_i = \frac{x_{i+1} - x_i}{y_{i+1} - y_i}, \quad i = 1, 2, \dots, n-1.

  • This produces an output vector p = (p_1, p_2, \dots, p_n) defined by:

    • For the first element:

      p_1 = |\delta_1|,

    • For the last element:

      p_n = |\delta_{n-1}|,

    • For the intermediate elements (i = 2, 3, \dots, n-1):

      p_i = \max\{|\delta_{i-1}|,\,|\delta_i|\}.

• median_speed: The median of the computed pupil_speed:

  median\_speed = median(p)

• mad_val: The median absolute deviation (MAD) of pupil_speed from the median:

  mad\_val = median(|p - median\_speed|)

• mad_thresh: A threshold computed from the median speed and the MAD, using a constant multiplier n (default value: 16):

  mad\_thresh = median\_speed + (n \times mad\_val)

Value

An eyeris object with a new column in timeseries: ⁠pupil_raw_{...}_detransient⁠

Note

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

Examples

demo_data <- eyelink_asc_demo_dataset()

demo_data |>
  eyeris::glassbox(
    detransient = list(n = 16) # set to FALSE to skip step (not recommended)
  ) |>
  plot(seed = 0)

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