Remove ICA components

Reconstructs signals after removing specified ICA components (e.g., artifacts).

Usage

removeICAComponents(
  pe,
  ica_result,
  remove_components,
  assay_name = NULL,
  output_assay = "ica_cleaned"
)

Arguments

pe

A PhysioExperiment object.

ica_result

Result from runICA().

remove_components

Integer vector of component indices to remove.

assay_name

Name of the assay to reconstruct.

output_assay

Name for the cleaned output assay.

Value

PhysioExperiment with cleaned data in output_assay.

References

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

See also

runICA() for performing the ICA decomposition, icaRemove() for the alternative component removal implementation, detectBadChannels() for channel-level artifact detection.

Examples

pe <- PhysioExperiment(
  assays = list(raw = matrix(rnorm(1000), nrow = 100, ncol = 10)),
  colData = S4Vectors::DataFrame(label = paste0("Ch", 1:10)),
  samplingRate = 256
)
# \donttest{
if (requireNamespace("fastICA", quietly = TRUE)) {
  ica_result <- runICA(pe, n_components = 5)
  pe_clean <- removeICAComponents(pe, ica_result, remove_components = c(1, 3))
}
# }