Compute Time-Frequency representation of EEG data

Source: R/tfr_analysis.R

This function creates a time frequency representation of EEG time series data. Currently, it is possible to use either Morlet wavelets or Hanning tapers during the decomposition, which uses convolution in the frequency domain.

compute_tfr(data, ...)

# Default S3 method
compute_tfr(data, ...)

# S3 method for class 'eeg_epochs'
compute_tfr(
  data,
  method = "morlet",
  foi,
  n_freq,
  spacing = "linear",
  n_cycles = 7,
  keep_trials = FALSE,
  output = "power",
  downsample = 1,
  trim_edges = TRUE,
  verbose = TRUE,
  ...
)

# S3 method for class 'eeg_evoked'
compute_tfr(
  data,
  method = "morlet",
  foi,
  n_freq,
  spacing = "linear",
  n_cycles = 7,
  keep_trials = FALSE,
  output = "power",
  downsample = 1,
  trim_edges = TRUE,
  verbose = TRUE,
  ...
)

Arguments

data

An object of class eeg_epochs.

...

Further TFR parameters

method

Time-frequency analysis method. Defaults to "morlet".

foi

Frequencies of interest. Scalar or character vector of the lowest and highest frequency to resolve.

n_freq

Number of frequencies to be resolved. Must be an integer number of frequencies.

spacing

Use "linear" or "log" spacing for the frequency vector and number of cycles.

n_cycles

Number of cycles at each frequency. If a single integer, use a constant number of cycles at each frequency. If a character vector of length 2, the number of cycles will scale with frequency from the minimum to the maximum.

keep_trials

Keep single trials or average over them before returning. Defaults to FALSE.

output

Sets whether output is power, phase, or fourier coefficients.

downsample

Downsampling factor. Integer. Selects every n samples after performing time-frequency analysis on the full sampling rate data.

trim_edges

Remove edges of time-frequency representation to avoid edge effects from overlap of wavelet with timepoints outside the epochs. Defaults to TRUE.

verbose

Print informative messages in console.

Value

An object of class eeg_tfr

Methods (by class)

  • compute_tfr(default): Default method for compute_tfr

  • compute_tfr(eeg_epochs): Default method for compute_tfr

  • compute_tfr(eeg_evoked): Method for eeg_evoked objects.

Author

Matt Craddock matt@mattcraddock.com

Examples

out <- compute_tfr(demo_epochs, method = "morlet", foi = c(4, 30), n_freq = 10, n_cycles = 3)
#> Computing TFR using Morlet wavelet convolution
#> Output frequencies using linear spacing: 4 6.89 9.78 12.67 15.56 18.44 21.33 24.22 27.11 30
#> Removing channel means per epoch...
#> Returning signal averaged over all trials.
out
#> Epoched EEG TFR data
#> 
#> Frequency range		:	 4 6.89 9.78 12.67 15.56 18.44 21.33 24.22 27.11 30 
#> Number of channels	:	 11 
#> Electrode names		:	 A5 A13 A21 A29 A31 B5 B6 B8 B16 B18 B26 
#> Number of epochs	:	 1 
#> Epoch limits		:	 -0.197 - 0.451 seconds
#> Sampling rate		:	 128  Hz
out$freq_info$morlet_resolution
#>    frequency   sigma_f    sigma_t n_cycles
#> 1   4.000000  1.333333 0.11936621        3
#> 2   6.888889  2.296296 0.06930941        3
#> 3   9.777778  3.259259 0.04883163        3
#> 4  12.666667  4.222222 0.03769459        3
#> 5  15.555556  5.185185 0.03069417        3
#> 6  18.444444  6.148148 0.02588665        3
#> 7  21.333333  7.111111 0.02238116        3
#> 8  24.222222  8.074074 0.01971185        3
#> 9  27.111111  9.037037 0.01761141        3
#> 10 30.000000 10.000000 0.01591549        3
out <- compute_tfr(demo_epochs, method = "morlet", foi = c(4, 30),
 n_freq = 10, n_cycles = c(3, 10))
#> Computing TFR using Morlet wavelet convolution
#> Output frequencies using linear spacing: 4 6.89 9.78 12.67 15.56 18.44 21.33 24.22 27.11 30
#> Removing channel means per epoch...
#> Returning signal averaged over all trials.
out$freq_info$morlet_resolution
#>    frequency  sigma_f    sigma_t  n_cycles
#> 1   4.000000 1.333333 0.11936621  3.000000
#> 2   6.888889 1.823529 0.08727852  3.777778
#> 3   9.777778 2.146341 0.07415173  4.555556
#> 4  12.666667 2.375000 0.06701261  5.333333
#> 5  15.555556 2.545455 0.06252516  6.111111
#> 6  18.444444 2.677419 0.05944341  6.888889
#> 7  21.333333 2.782609 0.05719631  7.666667
#> 8  24.222222 2.868421 0.05548521  8.444444
#> 9  27.111111 2.939759 0.05413877  9.222222
#> 10 30.000000 3.000000 0.05305165 10.000000
plot_tfr(out)

out <- compute_tfr(demo_epochs, method = "morlet", foi = c(4, 30),
 n_freq = 10, n_cycles = c(3, 10), trim_edges = FALSE)
#> Computing TFR using Morlet wavelet convolution
#> Output frequencies using linear spacing: 4 6.89 9.78 12.67 15.56 18.44 21.33 24.22 27.11 30
#> Removing channel means per epoch...
#> Returning signal averaged over all trials.
plot_tfr(out)