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Generalized eigenvalue decomposition based methods for EEG data

Source: R/signal_decomposition.R
eeg_decompose.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 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.

Usage

eeg_decompose(data, ...)

# S3 method for class 'eeg_epochs'
eeg_decompose(
  data,
  sig_range,
  noise_range,
  method = c("ssd", "ress"),
  verbose = TRUE,
  order = 2,
  ...
)

Arguments

data

An eeg_epochs 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. Defaults to "ssd"

verbose

Informative messages printed to console. Defaults to TRUE.

order

Filter order for filter applied to signal/noise

Value

An eeg_ICA object.

Methods (by class)

  • eeg_decompose(eeg_epochs): method for eeg_epochs objects

References

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

See also

Other decompositions: run_ICA()

Author

Matt Craddock matt@mattcraddock.com

Examples

# 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")
#> Performing ssd...
#> Band-pass IIR filter from 9 - 11 Hz
#> Effective filter order: 4 (two-pass)
#> Removing channel means per epoch...
#> Band-pass IIR filter from 8 - 12 Hz
#> Effective filter order: 4 (two-pass)
#> Removing channel means per epoch...
#> Band-stop IIR filter from 8.5 - 11.5 Hz.
#> Effective filter order: 4 (two-pass)
#> Removing channel means per epoch...
#> Input data is not full rank; returning 10components
 plot_psd(decomposed)
#> Removing channel means per epoch...
#> Computing Power Spectral Density using Welch's method.
#> FFT length: 256
#> Segment length: 84
#> Overlapping points: 42 (50% overlap)

 # We can plot the spatial filters using `topoplot()`
 topoplot(decomposed, 1:2)
#> Plotting 2 components
#> Using electrode locations from data.
#> Plotting head r 95 mm

 plot_timecourse(decomposed, 1)
#> Creating epochs based on combinations of variables: epoch_label participant_id 

# 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")
#> Performing ress...
#> Band-pass IIR filter from 9 - 11 Hz
#> Effective filter order: 4 (two-pass)
#> Removing channel means per epoch...
#> Band-pass IIR filter from 8 - 12 Hz
#> Effective filter order: 4 (two-pass)
#> Removing channel means per epoch...
#> Band-stop IIR filter from 8.5 - 11.5 Hz.
#> Effective filter order: 4 (two-pass)
#> Removing channel means per epoch...
#> Input data is not full rank; returning 10components
 plot_psd(with_RESS)
#> Removing channel means per epoch...
#> Computing Power Spectral Density using Welch's method.
#> FFT length: 256
#> Segment length: 84
#> Overlapping points: 42 (50% overlap)

 # The topographical plots are identical to those using "ssd", as the
 # spatial filters are the same.
 topoplot(with_RESS, 1:2)
#> Plotting 2 components
#> Using electrode locations from data.
#> Plotting head r 95 mm

 plot_timecourse(with_RESS, 1)
#> Creating epochs based on combinations of variables: epoch_label participant_id