Find and interpolate stimulation pulses

Usage

stimpulse_find(
  signal,
  sample_rate,
  pulse_duration,
  n_pulses = NA,
  threshold = NA
)

stimpulse_extract(
  signal,
  pulse_info,
  expand_timepoints = c(-10, 20),
  center = TRUE
)

stimpulse_align(signal, pulse_info, expand_timepoints = c(-10, 20))

stimpulse_interpolate(
  signal,
  sample_rate,
  pulse_info,
  max_offset = c(-2e-04, 5e-04)
)

Arguments

signal: a channel signal trace
sample_rate: sample rate
pulse_duration: stimulation pulse duration in seconds
n_pulses: suggested number of pulses
threshold: suggested suggested threshold of responses to find stimulation pulses
pulse_info: a list containing number of pulses n_pulse, onset index (onset_index, first time-point is 1), offset index (offset_index); this can be generated by stimpulse_find; see 'Examples' below
expand_timepoints: point offsets allowed to align the pulses
center: whether to center the pulses by median; default is true
max_offset: maximum (edge) offsets in seconds to interpolate the pulses; default is -0.0002 seconds before stimulation onset and 0.0005 seconds after the stimulation offset

Value

stimpulse_find and stimpulse_align returns the pulse information (pulse_info) with the time-points of detected or corrected stimulation on-set and off-set. The time-points are 1-indexed. stimpulse_extract extract the signals around pulses; stimpulse_interpolate returns interpolated signals.

Examples




data("stimulation_signal")

signal <- stimulation_signal$signal
sample_rate <- stimulation_signal$sample_rate

# each pulse is roughly <0.001 seconds
pulse_durations <- 0.001

# Initial pulses
pulse_info <- stimpulse_find(signal, sample_rate, pulse_durations)

# number of pulses detected
pulse_info$n_pulses
#> [1] 200

# extract responses -10 points before onset ~ 20 points after offset
expand_timepoints <- c(-20, 80)
pulses_snippets <- stimpulse_extract(
  signal = signal,
  pulse_info = pulse_info,
  expand_timepoints = expand_timepoints
)

# Visualize the pulses
snippet_time <- seq(
  expand_timepoints[[1]], by = 1,
  length.out = nrow(pulses_snippets)) / sample_rate * 1000
matplot(snippet_time, pulses_snippets, type = 'l', lty = 1, col = 'gray80',
        xlab = "Time (ms)", ylab = "uV", main = "Initial find")


# Align the pulses
pulse_info <- stimpulse_align(signal, pulse_info)

# Estimated pulse duration
estimated_duration <-
  (pulse_info$offset_index - pulse_info$onset_index + 1) / sample_rate

# reload aligned pulses
pulses_snippets <- stimpulse_extract(
  signal = signal,
  pulse_info = pulse_info,
  expand_timepoints = expand_timepoints
)
matplot(snippet_time, pulses_snippets, type = 'l', lty = 1, col = 'gray80',
        xlab = "Time (ms)", ylab = "uV", main = "Aligned pulses")
lines(snippet_time, rowMeans(pulses_snippets), col = 'red')



# Interpolate the pulses
interpolated <- stimpulse_interpolate(
  signal = signal,
  sample_rate = sample_rate,
  pulse_info = pulse_info,
  max_offset = c(-0.0003, 0.0005)
)


interp_snippets <- stimpulse_extract(
  signal = interpolated,
  pulse_info = pulse_info,
  expand_timepoints = expand_timepoints
)

oldpar <- par(mfrow = c(1, 2))
on.exit(par(oldpar))
matplot(snippet_time, pulses_snippets, type = 'l', lty = 1,
        col = 'gray80', xlab = "Time (ms)", ylab = "uV",
        main = "Stim pulses", ylim = c(-600, 400))
lines(snippet_time, rowMeans(pulses_snippets), col = 'red')
abline(v = max(estimated_duration) * 1000, lty = 2)


matplot(snippet_time, interp_snippets, type = 'l', lty = 1,
        col = 'gray80', xlab = "Time (ms)", ylab = "uV",
        main = "Interpolated 0.5 ms bandwidth")
lines(snippet_time, rowMeans(interp_snippets), col = 'red')
abline(v = max(estimated_duration) * 1000, lty = 2, col = "gray40")
abline(v = max(estimated_duration) * 1000 + 0.5, lty = 2)