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Remove ICA Components from EEG

Source: R/eeg-ica.R
eegICAremove.Rd

Reconstructs EEG data with specified independent components removed. The removed components are zeroed out in the mixing matrix before back-projecting to channel space.

Usage

eegICAremove(
  x,
  components,
  ica_assay = "ica_components",
  output_assay = "ica_cleaned"
)

Arguments

x

A PhysioExperiment object with ICA results (from eegICA).

components

Integer vector of component indices to remove.

ica_assay

Assay name containing ICA component activations (default: "ica").

output_assay

Output assay name (default: "ica_cleaned").

Value

Modified PhysioExperiment with cleaned data in output_assay. The cleaned assay contains reconstructed channel data with specified components removed. Dimensions match the original data.

References

Hyvarinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430.

Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159.

See also

eegICA(), eegICAdetect(), eegICAmix()

Examples

if (FALSE) { # \dontrun{
pe <- make_eeg(n_time = 5000, sr = 500)
pe <- eegICA(pe, n_components = 4, method = "fastica")
pe <- eegICAremove(pe, components = c(1, 2))
} # }