View source: R/signal_decomposition.R
eeg_decompose | R Documentation |
Implements a selection of Generalized Eigenvalue based decomposition methods for EEG signals. Intended for isolating oscillations at specified frequencies, decomposing channel-based data into components reflecting distinct or combinations of sources of oscillatory signals. Currently, spatio-spectral decomposition (Nikulin, Nolte, & Curio, 2011) and Rhythmic Entrainment Source Separation (Cohen & Gulbinate, 2017) are implemented. The key difference between the two is that the former returns the results of the data-derived spatial filters applied to the bandpass-filtered "signal" data, whereas the latter returns the results of the filters applied to the original, broadband data.
eeg_decompose(data, ...) ## S3 method for class 'eeg_epochs' eeg_decompose( data, sig_range, noise_range, method = "ssd", verbose = TRUE, order = 2, ... )
data |
An |
... |
Additional parameters |
sig_range |
Vector with two inputs, the lower and upper bounds of the frequency range of interest |
noise_range |
Range of frequencies to be considered noise (e.g. bounds of flanker frequencies) |
method |
Type of decomposition to apply. Currently only "ssd" is supported. |
verbose |
Informative messages printed to console. Defaults to TRUE. |
order |
Filter order for filter applied to signal/noise |
An eeg_ICA
object. Note that
eeg_epochs
: method for eeg_epochs
objects
Matt Craddock matt@mattcraddock.com
Cohen, M. X., & Gulbinate, R. (2017). Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation. NeuroImage, 147, 43-56. https://doi.org/10.1016/j.neuroimage.2016.11.036
Haufe, S., Dähne, S., & Nikulin, V. V. (2014). Dimensionality reduction for the analysis of brain oscillations. NeuroImage, 101, 583–597. https://doi.org/10.1016/j.neuroimage.2014.06.073
Nikulin, V. V., Nolte, G., & Curio, G. (2011). A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition. NeuroImage, 55(4), 1528–1535. https://doi.org/10.1016/j.neuroimage.2011.01.057
Other decompositions:
run_ICA()
# The default method is Spatio-Spectral Decomposition, which returns # spatially and temporally filtered source timecourses. decomposed <- eeg_decompose(demo_epochs, sig_range = c(9, 11), noise_range = c(8, 12), method = "ssd") plot_psd(decomposed) # We can plot the spatial filters using `topoplot()` topoplot(decomposed, 1:2) plot_timecourse(decomposed, 1) # method = "ress" returns spatially but not temporally filtered timecourses. with_RESS <- eeg_decompose(demo_epochs, sig_range = c(9, 11), noise_range = c(8, 12), method = "ress") plot_psd(with_RESS) # The topographical plots are identical to those using "ssd", as the # spatial filters are the same. topoplot(with_RESS, 1:2) plot_timecourse(with_RESS, 1)
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