ravecore (version 0.1.0)

run_wavelet: Apply Morlet-Wavelet to subject

Description

Calculates time-frequency decomposition; not intended for direct use. Please use 'RAVE' pipelines (see 'Examples').

Usage

run_wavelet(
  subject,
  electrodes,
  freqs,
  cycles,
  target_sample_rate = 100,
  kernels_precision = "float",
  pre_downsample = 1,
  verbose = TRUE
)

Value

The decomposition results are stored in 'RAVE' subject data path; the function only returns the wavelet parameters.

Arguments

subject

'RAVE' subject or subject ID

electrodes

electrode channels to apply, must be imported and 'LFP' type

freqs

numeric vector of frequencies to apply

cycles

number of wavelet cycles at each freqs, integers

target_sample_rate

the resulting 'spectrogram' sampling frequency

kernels_precision

double or single (default) floating precision

pre_downsample

down-sample (integer) priory to the decomposition; set to 1 (default) to avoid

verbose

whether to verbose the progress

Details

The channel signals are first down-sampled (optional) by a ratio of pre_downsample via a 'FIR' filter. After the down-sample, 'Morlet' wavelet kernels are applied to the signals to calculate the wavelet coefficients (complex number) at each frequency in freqs. The number of cycles at each frequency controls the number of sine and cosine waves, allowing users to balance the time and power accuracy. After the decomposition, the 'spectrogram' is further down-sampled to target_sample_rate, assuming the brain power is a smooth function over time. This down-sample is done via time-point sampling to preserve the phase information (so the linear functions such as common-average or bi-polar reference can be carried over to the complex coefficients).

Examples

# Check https://rave.wiki for additional pipeline installation

if (FALSE) {

# ---- Recommended usage --------------------------------------------


pipeline <- ravepipeline::pipeline("wavelet_module")
pipeline$set_settings(
  project_name = "demo",
  subject_code = "DemoSubject",
  precision = "float",
  pre_downsample = 4,
  kernel_table = ravetools::wavelet_cycles_suggest(
    freqs = seq(1, 200, by = 1)),
  target_sample_rate = 100
)

# Internally, the above pipeline includes this function call below

# ---- For demonstration use, do not call this function directly ----

# Original sample rate: 2000 Hz
# Downsample by 4 to 500 Hz first - 250 Hz Nyquist
# Wavelet at each 1, 2, ..., 200 Hz
#   The number of cycles log-linear from 2 to 20
#   The wavelet coefficient sample rate is 500 Hz
# Further down-sample to 100 Hz to save storage space

run_wavelet(
  subject = "demo/DemoSubject",
  electrodes = c(13:16, 2),
  pre_downsample = 4,
  freqs = seq(1, 200, by = 1),
  cycles = c(2, 20),
  target_sample_rate = 100
)

}