ICA-Based Artifact Removal for PhysioExperiment

Functions for Independent Component Analysis (ICA) decomposition and artifact removal using the fastICA algorithm. Perform ICA decomposition

Usage

runICA(
  pe,
  n_components = NULL,
  assay_name = NULL,
  method = "fastica",
  max_iter = 200,
  ...
)

Arguments

pe

A PhysioExperiment object.

n_components

Number of components to extract. If NULL, uses all channels.

assay_name

Name of the assay to decompose.

method

ICA algorithm: "fastica" (default).

max_iter

Maximum number of iterations.

...

Additional arguments passed to fastICA::fastICA.

Value

A list with components:

S

Source matrix (time x components)

A

Mixing matrix (channels x components)

W

Unmixing matrix (components x channels)

Details

Decomposes signals into independent components using the fastICA algorithm.

References

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

See also

removeICAComponents() for reconstructing signals after removing artifact components, icaDecompose() for the built-in ICA 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)
}
# }