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.
eegUtils, this can be achieved using
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_epochs objects without first having to
compute_psd(). With epoched data, it will average over epochs before plotting.
library(eegUtils) #> Make sure to check for the latest development version at https://github.com/craddm/eegUtils! #> #> Attaching package: 'eegUtils' #> The following object is masked from 'package:stats': #> #> filter demo_psd <- compute_psd(demo_epochs) #> Removing channel means per epoch... #> Joining, by = "epoch" plot_psd(demo_epochs) #> Removing channel means per epoch...
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;
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.
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) #> Output frequencies: 4 6.36 8.73 11.09 13.45 15.82 18.18 20.55 22.91 25.27 27.64 30 #> Removing channel means per epoch... demo_tfr #> Epoched EEG TFR data #> #> Frequency range : 4 6.36 8.73 11.09 13.45 15.82 18.18 20.55 22.91 25.27 27.64 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
Note that the characteristics of the wavelets, in terms of temporal and frequency standard deviations, are stored inside the object:
demo_tfr$freq_info$morlet_resolution #> sigma_f sigma_t #> 1 1.333333 0.11936621 #> 2 2.121212 0.07503019 #> 3 2.909091 0.05470951 #> 4 3.696970 0.04305011 #> 5 4.484848 0.03548725 #> 6 5.272727 0.03018456 #> 7 6.060606 0.02626057 #> 8 6.848485 0.02323944 #> 9 7.636364 0.02084172 #> 10 8.424242 0.01889249 #> 11 9.212121 0.01727669 #> 12 10.000000 0.01591549
The results of the time-frequency transformation can be plotted using the
Baseline correction is common in the literature. Several different methods are possible, both for plotting only, and as a modification to the object using