In neuroconductor/eegUtils: Utilities for Electroencephalographic (EEG) Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Periodicity is commonly observed in EEG signals. For example, oscillations in the alpha frequency range (approximately 8-13 Hz) were one of the first signals observed in the human EEG. One method of analysing this periodicity is to calculate the Power Spectral Density using a method such as Welch's FFT.

Frequency analysis

In eegUtils, this can be achieved using compute_psd() and plot_psd(). With epoched data, compute_psd() calculates the PSD for each trial separately. compute_psd() returns a data.frame with spectral power at each resolved frequency and for each electrode. Note that plot_psd() can be called directly on eeg_data or eeg_epochs objects without first having to compute_psd(). With epoched data, it will compute the PSD for each epoch and then average over epochs before plotting.

library(eegUtils)
demo_psd <- compute_psd(demo_epochs)
plot_psd(demo_epochs)

Time-frequency analysis

Frequency analysis necessarily discards temporal information. One problem is that it assumes stationarity - that the signal remains stable in terms of frequency and power across the whole analysed time window. However, this is rarely the case with EEG data, which exhibits dynamics across a wide range of timescales.

Time-frequency analysis is a method of accounting for non-stationarity by decomposing the signal using a moving-window analysis, tiling the time-frequency space to resolve power over relatively shorter time-windows.

In eegUtils, compute_tfr() can be used to calculate a time-frequency representation of eeg_epochs(). Currently, this is achieved using Morlet wavelets. Morlet wavelets are used to window the signal, controlling spectral leakage and time-frequency specificity. Morlet wavelets have a user-defined temporal extent, which in turn determines the frequency extent. We define the temporal extent of our wavelets by cycles; we define it as an integer number of cycles at each frequency of interest.

demo_tfr <- compute_tfr(demo_epochs,
                        method = "morlet",
                        foi = c(4, 30),
                        n_freq = 12,
                        n_cycles = 3)
demo_tfr

Note that the characteristics of the wavelets, in terms of temporal and frequency standard deviations, are stored inside the eeg_tfr object:

demo_tfr$freq_info$morlet_resolution

The results of the time-frequency transformation can be plotted using the plot_tfr() function.

plot_tfr(demo_tfr)

Baseline correction is common in the literature, which can serve two purposes. Several different methods are possible. both for plotting only, and as a modification to the eeg_tfr object using rm_baseline().

plot_tfr(demo_tfr, baseline_type = "absolute", baseline = c(-.1, 0))
plot_tfr(demo_tfr, baseline_type = "db", baseline = c(-.1, 0))

neuroconductor/eegUtils documentation built on Feb. 3, 2023, 5:33 p.m.