Time-frequency wavelet PLV

Computes Phase Locking Value in the time-frequency domain using complex Morlet wavelets. The phase difference between the two signals is computed at each time-frequency point, and PLV is the magnitude of the smoothed unit-phase vector:

Usage

waveletPLV(
  x,
  y = NULL,
  sr = NULL,
  frequencies = seq(1, 40, by = 1),
  n_cycles = 7,
  smoothing_cycles = 3,
  modality_x = NULL,
  modality_y = NULL,
  channels_x = 1L,
  channels_y = 1L,
  ...
)

Arguments

x: Numeric vector, PhysioExperiment, or MultiPhysioExperiment.
y: Numeric vector or PhysioExperiment, or NULL when x is an MPE.
sr: Numeric sampling rate in Hz (required when x/y are numeric).
frequencies: Numeric vector of centre frequencies in Hz (default seq(1, 40, by = 1)).
n_cycles: Numeric number of wavelet cycles (default 7).
smoothing_cycles: Numeric number of cycles for the temporal smoothing Gaussian (default 3).
modality_x, modality_y: Character modality names for MPE input.
channels_x, channels_y: Integer channel indices (default 1).
...: Currently unused.

Value

A list with components:

plv: Numeric matrix [time x frequency] of PLV values in $[0, 1]$.
frequencies: Numeric vector of centre frequencies.
times: Numeric vector of time points (seconds from start).
coi: Numeric vector of Cone of Influence frequencies. At each time point, frequencies below this value are affected by edge artifacts.

Details

$$\text{PLV}(t,f) = \left|\langle e^{i\Delta\phi(t,f)} \rangle\right|$$

References

Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.

Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208.

Examples

sr <- 200
t <- seq(0, 2, length.out = sr * 2)
x <- sin(2 * pi * 10 * t) + 0.3 * rnorm(length(t))
y <- sin(2 * pi * 10 * t + pi/4) + 0.3 * rnorm(length(t))
result <- waveletPLV(x, y, sr = sr, frequencies = seq(5, 20))