Wavelet transform

Computes the continuous wavelet transform using Morlet wavelets.

Usage

waveletTransform(
  x,
  frequencies = seq(1, 40, by = 1),
  n_cycles = 7,
  channel = 1L,
  sample = 1L,
  normalization = c("L2", "L1")
)

Arguments

x

A PhysioExperiment object.

frequencies

Numeric vector of frequencies to analyze.

n_cycles

Number of wavelet cycles (can be scalar or vector).

channel

Channel index to analyze.

sample

Sample index (for 3D data).

normalization

Wavelet normalization method: "L2" (default, divides by square root of sum of squared absolute values) or "L1" (divides by sum of absolute values). L2 normalization preserves energy across frequencies and is preferred for power comparisons.

Value

A list with the following components:

power

Power matrix (frequency x time)

phase

Phase matrix in radians (frequency x time)

frequencies

Numeric vector of analyzed frequencies in Hz

times

Numeric vector of time points in seconds

sampling_rate

The sampling rate used

n_cycles

Number of wavelet cycles per frequency

References

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

See also

spectrogram() for STFT-based time-frequency analysis, bandPower() for band power extraction, hilbertTransform() for analytic signal computation.

Examples

pe <- PhysioExperiment(
  assays = list(raw = matrix(rnorm(500 * 4), nrow = 500)),
  samplingRate = 100
)

# Compute wavelet transform (1-30 Hz)
wt <- waveletTransform(pe, frequencies = seq(1, 30), channel = 1)

# Access power and phase
dim(wt$power)  # frequency x time
#> [1]  30 500