EEG Independent Component Analysis (ICA)

Decomposes multi-channel EEG into independent components using ICA. Supports FastICA, Infomax, and JADE algorithms. Results are stored in the output assay (component activations) and in metadata(x)$ica (mixing and unmixing matrices).

Usage

eegICA(
  x,
  n_components = NULL,
  method = c("fastica", "infomax", "jade"),
  max_iter = 200L,
  tol = 1e-06,
  assay_name = NULL,
  output_assay = "ica_components"
)

Arguments

x

A PhysioExperiment object with EEG data.

n_components

Number of independent components to extract. Defaults to the number of channels.

method

ICA algorithm: "fastica", "infomax", or "jade".

max_iter

Maximum number of iterations (default: 200).

tol

Convergence tolerance (default: 1e-6).

assay_name

Input assay name (default: first assay).

output_assay

Output assay name (default: "ica").

Value

Modified PhysioExperiment with component activations in output_assay and ICA metadata in metadata(x)$ica. The ICA metadata list contains: mixing (mixing matrix A), unmixing (unmixing matrix), mean (channel means), whiten (whitening matrix), and method (algorithm used). The output assay has dimensions n_time x n_components.

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

eegICAremove(), eegICAdetect(), eegICAmix(), eegFilter(), eegPreprocess()

Examples

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