fICA: Wrapper for FastICA methods for Independent Component...

In bnicenboim/eeguana: Flexible Manipulation of EEG Data

Wrapper for FastICA methods for Independent Component Analysis from fICA package

Description

adapt_fast_ICA() and fast_ICA2() are wrappers for fICA::adapt_fICA and fICA::fICA that only run in C, and return a transposed version of the original estimated unmixing matrix in W, and the mixing matrix A, consistent with the formulation X= S %*% A, where X is the matrix of data with N_samples by N_channels, and S is a matrix of sources with N_samples by N_sources. They are meant to be used with eeg_ica(). In order to save memory, these wrappers do not return the estimated source components.

Usage

adapt_fast_ICA(
  X,
  gs = fICA::gf,
  dgs = fICA::dgf,
  kj = 0,
  eps = 1e-06,
  maxiter = 1000
)

fast_ICA2(
  X,
  g = "tanh",
  dg = NULL,
  G = NULL,
  init = NULL,
  n.init = 1,
  method = "def",
  eps = 1e-06,
  maxiter = 1000
)

Arguments

X	A matrix of data frame.
gs	a list of functions containing the nonlinearities.
dgs	a list of functions containing the first derivatives of the nonlinearities.
kj	defines the initial estimate of the unmixing matrix, see details.
eps	convergence tolerance.
maxiter	maximum number of iterations.
g	the nonlinearity, tanh by default, see details.
dg	the first derivative of the nonlinearity, see details.
G	the integral function of the nonlinearity, see details.
init	a numeric matrix for the initial value of the algorithm
n.init	a positive integer for the number of initial values in symmetric algorithms, see details.
method	squared symmetric ("sym2"), symmetric ("sym") or deflation-based ("def")

Value

A list with the unmixing matrix W and the mixing matrix A.

See Also

Other ica methods: fast_ICA(), ica_matrix_lst()

bnicenboim/eeguana documentation built on March 16, 2024, 7:21 a.m.