Independent Component Analysis for EEG data

Performs Independent Component Analysis for electroencephalographic data. Currently only available with on epoched data. Implements three different methods of ICA - 'fastica', 'extended Infomax', and 'Second-Order Blind Identification (SOBI)'. The resulting eeg_ICA objects can be used largely like eeg_epochs objects.

run_ICA(data, ...)

# S3 method for class 'eeg_epochs'
run_ICA(
  data,
  method = "sobi",
  maxit = 1000,
  tol = 1e-06,
  pca = NULL,
  centre = TRUE,
  alg = "gradient",
  rateanneal = c(60, 0.9),
  rate = 0.1,
  verbose = TRUE,
  return = c("full", "weights"),
  ...
)

Arguments

data: Data to be ICAed.
...: Other parameters passed to function.
method: "sobi" (default), "fastica", "infomax", or "imax". "infomax" uses the implementation from the ica package, whereas imax uses the implementation from the infomax package, which is based on the EEGLAB implementation.
maxit: Maximum number of iterations of the Infomax and Fastica ICA algorithms.
tol: Convergence tolerance for fastica and infomax. Defaults to 1e-06.
pca: Reduce the number of dimensions using PCA before running ICA. Numeric, >1 and < number of channels
centre: Defaults to TRUE. Centre the data on zero by subtracting the column mean. See notes on usage.
alg: Use "gradient descent" or "newton" algorithm for extended infomax. Defaults to "gradient". Ignored if method != "infomax".
rateanneal: Annealing rate for extended infomax. Ignored if method != "infomax".
rate: Learning rate for extended infomax. Ignored if method != "infomax".
verbose: Print informative messages to console.
return: "full" or "weights". "full" returns the mixing and unmixing matrices and the source timecourses. "weights" returns only the mixing and unmixing matrices. Defaults to "full".

Value

An eeg_ICA object containing an ICA decomposition

Methods (by class)

run_ICA(eeg_epochs): Run ICA on an eeg_epochs object

Notes on ICA usage

It is recommended to mean-centre your data appropriately before running ICA. The implementations of FASTICA and extended-Infomax from the ica package, and of SOBI ICA have this as an option which is enabled by default, while the implementation of FASTICA in the fICA package enforces mean-centring of the columns of the data. With epoched data, it is recommended to centre each epoch on zero, rather than centre on the overall channel mean. This can be achieved with the rm_baseline() function. SOBI ICA will do this automatically, whereas the other ICA implementations will centre on the channel means, not the epoch means.

In addition, PCA will be required if the data is not full rank. This is typical when using average reference, when the data rank will be n_electrodes - 1.

Author

Matt Craddock matt@mattcraddock.com

Examples

sobi_demo <-
  run_ICA(demo_epochs,
          pca = 10)
#> Reducing data to 10 dimensions using PCA.
#> Running SOBI ICA.
#> Setting tolerance to 0.0011
 sobi_demo
#> Epoched ICA decomposition
#> 
#> Number of components	: 10 
#> Number of epochs	: 80 
#> Epoch limits		: -0.197 - 0.451 seconds
#> Sampling rate		: 128  Hz
 # We can plot the resulting spatial filters using `topoplot()`
 topoplot(sobi_demo, 1:2)
#> Plotting 2 components
#> Using electrode locations from data.
#> Plotting head r 95 mm

 if (FALSE)  view_ica(sobi_demo)  # \dontrun{}