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

In neuroconductor-devel-releases/eegUtils: 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 frequencis, 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")

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.

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

neuroconductor-devel-releases/eegUtils documentation built on May 5, 2020, 3:49 a.m.