diagnose_signal: Plot and Inspect Signals in Trace, Periodogram, and Histogram

In beauchamplab/rave: R Analysis and Visualization of 'ECOG/iEEG' Data

Plot and Inspect Signals in Trace, Periodogram, and Histogram

Description

Plot and Inspect Signals in Trace, Periodogram, and Histogram

Usage

diagnose_signal(
  s1,
  s2 = NULL,
  sc = NULL,
  srate,
  name = "",
  try_compress = TRUE,
  max_freq = 300,
  window = ceiling(srate * 2),
  noverlap = window/2,
  std = 3,
  cex = 1.5,
  lwd = 0.5,
  flim = NULL,
  nclass = 100,
  main = "Channel Inspection",
  col = c("black", "red"),
  which = NULL,
  start_time = 0,
  boundary = NULL,
  mar = c(5.2, 5.1, 4.1, 2.1),
  ...
)

Arguments

s1	Signal for inspection
s2	Signal to compare, default NULL
sc	compressed signal to speedup the trace plot, if not provided, then either the original s1 is used, or a compressed version will be used. See parameter try_compress.
srate	Sample rate of s1, note that s2 and s1 must have the same sample rate
name	Analysis name, for e.g. "CAR", "Notch", etc.
try_compress	If length of s1 is too large, it might take long to draw trace plot, my solution is to down-sample s1 first (like what Matlab does), and then plot the compressed signal. Some information will be lost during this process, however, the trade-off is the speed. try_compress=FALSE indicates that you don't want to compress signals under any situation (this might be slow).
max_freq	Max frequency to plot, should be no larger than half of the sampling rate.
window	Window length to draw the Periodogram
noverlap	Number of data points that each adjacent windows overlap
std	Error bar (red line) be drawn at standard deviations, by default is 3, meaning the error bars represent 3 standard deviations.
cex, lwd, mar, ...	passed to plot.default
flim	log10 of frequency range to plot
nclass	Number of classes for histogram
main	Plot title
col	Color for two signals, length of 2.
which	Which sub-plot to plot
start_time	When does signal starts
boundary	Boundary for signal plot, default is 1 standard deviation

Examples

library(stats)
time <- seq(0, 100, by = 1/200)
s2 <- sin(2 * pi * 60 * time) + rnorm(length(time))
diagnose_signal(s2, srate = 200)

# Apply notch filter
s1 = notch_filter(s2, 200, 58,62)
diagnose_signal(s1, s2, srate = 200)

beauchamplab/rave documentation built on Feb. 16, 2025, 8:12 p.m.