Recreate channel timecourses from ICA decompositions.

Source: R/run_ICA.R

This function can be used to either recreate "mixed" (i.e. channel level) timecourses from an ICA decomposition, or to apply a set of ICA weights to a given dataset for the purpose of removing specific ICA components from that dataset.

apply_ica(data, ...)

# S3 method for eeg_ICA
apply_ica(data, comps = NULL, ...)

# S3 method for eeg_epochs
apply_ica(data, decomp, comps, ...)

Arguments

data

An eeg_ICA or eeg_epochs object.
...

Other parameters.
comps

Components to remove.
decomp

An eeg_ICA object.

Methods (by class)

  • eeg_ICA: From given eeg_ICA object, recreate channel timecourses.

  • eeg_epochs: Combine a specific set of ICA weights with any eeg_epochs object.

Author

Matt Craddock matt@mattcraddock.com

Examples

test_ica <- run_ICA(demo_epochs, pca = 10)

#> Reducing data to 10 dimensions using PCA.
#> Running SOBI ICA.
#> Setting tolerance to 0.0011
plot_butterfly(demo_epochs)

#> Joining, by = c("epoch", "recording", "epoch_label", "participant_id")
# Reconstruct the original data from the ICA decomposition.
# Note that the ICA process subtracts the mean from each epoch,
# so the reconstructed plot may look slightly different to the original.
plot_butterfly(apply_ica(test_ica))

#> Joining, by = c("epoch", "recording", "epoch_label", "participant_id")
# Remove component 2 from the data
plot_butterfly(apply_ica(demo_epochs, test_ica, comps = 2))

#> Joining, by = c("epoch", "recording", "epoch_label", "participant_id")