R/signal_decomposition.R
eeg_decomp.Rd
Implements a selection of Generalized Eigenvalue based decomposition methods for EEG signals. Intended for isolating oscillations at specified frequencies, decomposing channel-based data into distinct components reflecting distinct or combinations of sources of oscillatory signals. Currently only the spatio-spectral decomposition method (Nikulin et al, 2011) is implemented.
eeg_decomp(data, ...) # S3 method for eeg_epochs eeg_decomp(data, sig_range, noise_range = NULL, method = "ssd", verbose = TRUE, ...)
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. |
eeg_epochs
: method for eeg_epochs
objects
Cohen, M. X. (2016). Comparison of linear spatial filters for identifying oscillatory activity in multichannel data. BioRxiv, 097402. https://doi.org/10.1101/097402
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