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Calculate confounds for a single pupil data step

Source: R/pipeline-confounds.R
get_confounds_for_step.Rd

Computes various metrics from pupil data including:

  • Missing data (count and proportion of NA samples)

  • Blink detection

  • Gaze on/off screen detection

  • Gap analysis

  • Gaze distance from screen center

  • Gaze variance

  • Blink rate

  • Blink duration

  • Blink time

Usage

get_confounds_for_step(pupil_df, pupil_vec, screen_width, screen_height, hz)

Arguments

pupil_df

A data frame containing pupil data

pupil_vec

A vector of pupil data for the current step

screen_width

The screen width in pixels

screen_height

The screen height in pixels

hz

The sampling rate in Hz

Value

A data frame containing confounds metrics for the current step, including n_missing and prop_missing (the count and proportion of missing/NA samples)

Details

Missing data is reported as both a raw count (n_missing) and a proportion (prop_missing, ranging from 0 to 1) of samples in the window for which the pupil signal is NA (e.g., blinks and signal dropout prior to interpolation). Multiply prop_missing by 100 to obtain a percentage. This is distinct from prop_invalid, which additionally folds in samples flagged as blinks or off-screen gaze.