detransient_pupil

This function implements transient artifact removal by
identifying and removing samples that exceed a speed-based threshold.
The threshold is computed based on the constant <code>n</code>, which defaults to
the value <code>16</code>.
This function is called by the exposed wrapper <code>detransient()</code>.

internal

Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie (2018) <doi:10.3758/s13428-018-1075-y>; Strauch (2024) <doi:10.1016/j.tins.2024.06.002>]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44>]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology.

Shawn Schwartz

eyeris

Flexible, Extensible, & Reproducible Pupillometry Preprocessing

detransient_pupil function

<dl><dt>x</dt>
<dd>A data frame containing pupil data with columns <code>time_secs</code> and
the previous operation's pupil column</dd>
<dt>prev_op</dt>
<dd>The name of the previous operation's pupil column</dd>
<dt>n</dt>
<dd>The constant used to compute the median absolute deviation (MAD)
threshold. Defaults to <code>16</code></dd>
<dt>mad_thresh</dt>
<dd>The threshold used to identify transient artifacts.
Defaults to <code>NULL</code></dd></dl>

Arguments

Internal function to remove transient artifacts from pupil data — detransient_pupil

<dl>

<dt>x</dt>
<dd>A data frame containing pupil data with columns <code>time_secs</code> and
the previous operation's pupil column</dd>


<dt>prev_op</dt>
<dd>The name of the previous operation's pupil column</dd>


<dt>n</dt>
<dd>The constant used to compute the median absolute deviation (MAD)
threshold. Defaults to <code>16</code></dd>


<dt>mad_thresh</dt>
<dd>The threshold used to identify transient artifacts.
Defaults to <code>NULL</code></dd>

</dl>

detransient_pupil: Internal function to remove transient artifacts from pupil data

Description

Usage

Value

Arguments

Details