get_confounds_for_step

Computes various metrics from pupil data including:<ul>
<li>Blink detection</li>
<li>Gaze on/off screen detection</li>
<li>Gap analysis</li>
<li>Gaze distance from screen center</li>
<li>Gaze variance</li>
<li>Blink rate</li>
<li>Blink duration</li>
<li>Blink time</li>
</ul>

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

get_confounds_for_step function

<dl><dt>pupil_df</dt>
<dd>A data frame containing pupil data</dd>
<dt>pupil_vec</dt>
<dd>A vector of pupil data for the current step</dd>
<dt>screen_width</dt>
<dd>The screen width in pixels</dd>
<dt>screen_height</dt>
<dd>The screen height in pixels</dd>
<dt>hz</dt>
<dd>The sampling rate in Hz</dd></dl>

Arguments

Calculate confounds for a single pupil data step — get_confounds_for_step

<dl>

<dt>pupil_df</dt>
<dd>A data frame containing pupil data</dd>


<dt>pupil_vec</dt>
<dd>A vector of pupil data for the current step</dd>


<dt>screen_width</dt>
<dd>The screen width in pixels</dd>


<dt>screen_height</dt>
<dd>The screen height in pixels</dd>


<dt>hz</dt>
<dd>The sampling rate in Hz</dd>

</dl>

get_confounds_for_step: Calculate confounds for a single pupil data step

Description

Usage

Value

Arguments