Identifies SCR peaks in an EDA signal using either a gradient-based zero-crossing method or an amplitude threshold method. Returns onset, peak, amplitude, rise time, and recovery time for each detected SCR.
Usage
edaPeaks(
x,
method = c("gradient", "threshold"),
amplitude_min = 0.01,
rise_time_min = 0.1,
rise_time_max = 5,
assay_name = NULL
)Arguments
- x
A PhysioExperiment object containing EDA data.
- method
Detection method:
"gradient"(first-derivative zero-crossing) or"threshold"(amplitude threshold). Default is"gradient".- amplitude_min
Minimum SCR amplitude in microsiemens (default: 0.01).
- rise_time_min
Minimum rise time in seconds (default: 0.1).
- rise_time_max
Maximum rise time in seconds (default: 5.0).
- assay_name
Name of the input assay. If NULL, uses
"phasic"if available, otherwisedefaultAssay(x).
Value
A data.frame with one row per detected SCR and the following
columns:
- channel
Character channel label.
- onset_sample
Integer sample index of SCR onset.
- onset_sec
Numeric onset time in seconds.
- peak_sample
Integer sample index of SCR peak.
- peak_sec
Numeric peak time in seconds.
- amplitude
Numeric SCR amplitude in microsiemens (peak minus onset).
- rise_time
Numeric rise time from onset to peak in seconds.
- recovery_time
Numeric 50% recovery time in seconds, or
NA.
Returns an empty data.frame with the same columns if no peaks are
found.
References
Bach, D.R., et al. (2010). "Modelling event-related skin conductance responses." International Journal of Psychophysiology, 75(3), 349-356. doi:10.1016/j.ijpsycho.2010.01.005
Benedek, M., & Kaernbach, C. (2010). "A continuous measure of phasic electrodermal activity." Journal of Neuroscience Methods, 190(1), 80-91. doi:10.1016/j.jneumeth.2010.04.028
See also
edaDecompose for tonic/phasic decomposition (run first),
edaFeatures for summary feature extraction,
plotPeaks for peak visualization,
edaErscr for event-related SCR analysis