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Morlet Wavelet Transform for EEG

Source: R/eeg-timefreq.R
eegMorletWavelet.Rd

Computes the continuous Morlet wavelet transform for multi-channel EEG data. For each specified frequency, a complex Morlet wavelet is constructed and convolved with each channel using FFT-based convolution for efficiency. Returns time-resolved power (and optionally phase) across frequencies.

Usage

eegMorletWavelet(
  x,
  frequencies = NULL,
  n_cycles = 7,
  assay_name = NULL,
  output_assay = "wavelet_power"
)

Arguments

x

A PhysioExperiment object with EEG data (2D: time x channels).

frequencies

Numeric vector of frequencies in Hz to analyze. If NULL, defaults to seq(1, 50, by = 1).

n_cycles

Number of cycles in the Morlet wavelet, controlling the trade-off between time and frequency resolution (default: 7).

assay_name

Name of the input assay. If NULL, the default assay is used.

output_assay

Name the result is stored under. Despite the argument's name this is a key in metadata, NOT an assay: after the call assay(x, "the value given") does not exist while metadata(x)$the value given holds the result (default: "the value given"). (default: "wavelet_power").

Value

Modified PhysioExperiment with:

  • 3D power array (time x frequencies x channels) in output_assay

  • Frequency vector and phase array in metadata(x)$wavelet, a list containing frequencies (numeric vector), n_cycles (integer), and phase (3D array of instantaneous phase values)

References

Tallon-Baudry, C., et al. (1997). Oscillatory gamma-band activity during conscious perception. Trends in Cognitive Sciences, 3(4), 151-162.

See also

eegSTFT(), eegMultitaper(), eegPlotSpectrogram(), eegERSP()

Examples

pe <- make_eeg(n_time = 1000, n_channels = 4, sr = 250)
pe_wt <- eegMorletWavelet(pe, frequencies = seq(5, 40, by = 5))
# results are stored in metadata(x)$wavelet
str(S4Vectors::metadata(pe_wt)$wavelet, max.level = 1)
#> List of 3
#>  $ frequencies: num [1:8] 5 10 15 20 25 30 35 40
#>  $ n_cycles   : num 7
#>  $ phase      : num [1:1000, 1:8, 1:4] 0.552 0.679 0.807 0.934 1.061 ...