This function creates a time frequency represention of EEG time series data. Currently, the only available method is a Morlet wavelet transformation performed using convolution in the frequency domain.
compute_tfr(data, ...) # S3 method for default compute_tfr(data, ...) # S3 method for eeg_epochs compute_tfr(data, method = "morlet", foi, n_freq, n_cycles = 7, keep_trials = FALSE, output = "power", downsample = 1, verbose = TRUE, ...) # S3 method for eeg_evoked compute_tfr(data, method = "morlet", foi, n_freq, n_cycles = 7, keep_trials = FALSE, output = "power", downsample = 1, verbose = TRUE, ...)
data | An object of class |
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... | 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. Scalar. |
n_cycles | Scalar. Number of cycles at each frequency. Currently only supports a single number of cycles at all frequencies. |
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. |
verbose | Print informative messages in console. |
default
: Default method for compute_tfr
eeg_epochs
: Default method for compute_tfr
eeg_evoked
: Method for eeg_evoked
objects.
#>#>#> 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 : None, averaged. #> Epoch limits : -0.197 - 0.451 seconds #> Sampling rate : 128 Hz