ICA and Artifact Removal for PhysioExperiment

Functions for Independent Component Analysis (ICA) and artifact removal from physiological signals.

Usage

icaDecompose(
  x,
  n_components = NULL,
  method = c("fastica", "jade"),
  max_iter = 200L,
  tol = 1e-04
)

Arguments

x

A PhysioExperiment object.

n_components

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

method

ICA method: "fastica" (default) or "jade".

max_iter

Maximum iterations for convergence.

tol

Tolerance for convergence.

Value

A list with four elements:

components

The independent components as a matrix or 3D array (time by component by samples for 3D data).

mixing

The mixing matrix (channels x components).

unmixing

The unmixing matrix (components x channels).

object

The input PhysioExperiment with the ICA decomposition (components, mixing/unmixing matrices, column means) stored in metadata(x)$ica. The components are held in metadata rather than as an assay because their column count is n_components, which need not equal the object's channel count.

References

Hyvarinen, A. & Oja, E. (2000). "Independent component analysis: algorithms and applications." Neural Networks, 13(4-5), 411-430. doi:10.1016/S0893-6080(00)00026-5 Perform ICA decomposition

Decomposes the signal into independent components using FastICA algorithm.

Hyvarinen, A. & Oja, E. (2000). "Independent component analysis: algorithms and applications." Neural Networks, 13(4-5), 411-430. doi:10.1016/S0893-6080(00)00026-5

See also

icaRemove() for removing specific components after decomposition, runICA() for an alternative ICA implementation using the fastICA package, detectBadChannels() for channel-level artifact detection.