compute_tfr: Compute Time-Frequency representation of EEG data

In neuroconductor-devel-releases/eegUtils: A collection of utilities for EEG analysis

Description

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.

Usage

compute_tfr(data, ...)

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

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

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

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. 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.

Methods (by class)

• default: Default method for compute_tfr

• eeg_epochs: Default method for compute_tfr

• eeg_evoked: Method for eeg_evoked objects.

Author(s)

Matt Craddock [e-mail]

Examples

compute_tfr(demo_epochs, method = "morlet", foi = c(4, 30), n_freq = 10, n_cycles = 3)

neuroconductor-devel-releases/eegUtils documentation built on May 5, 2020, 3:49 a.m.