eeg_decomp: Generalized eigenvalue decomposition based methods for EEG...

In kusumikakd/EEG: A collection of utilities for EEG analysis

Description

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.

Usage

eeg_decomp(data, ...)

## S3 method for class 'eeg_epochs'
eeg_decomp(
  data,
  sig_range,
  noise_range = NULL,
  method = "ssd",
  verbose = TRUE,
  ...
)

Arguments

data	An eeg_data object
...	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.

Methods (by class)

• eeg_epochs: method for eeg_epochs objects

References

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

kusumikakd/EEG documentation built on June 28, 2020, 12:30 a.m.