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PicardIca

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Python equivalent

mne.preprocessing.ICA (picard) in MNE-Python.

#include <dsp/picard_ica.h>

class UTILSLIB::PicardIca

Independent Component Analysis using the Picard algorithm.

Picard uses a preconditioned L-BFGS strategy to minimise the mutual information of the components. Compared to FastICA it typically converges in fewer iterations and handles badly conditioned data more robustly.

IcaResult res = PicardIca::run(matRawData, 20);
QVector<int> exclude = {0, 3};
MatrixXd clean = ICA::excludeComponents(matRawData, res, exclude);

Static Methods​

run(matData, nComponents, maxIter, tol, lbfgsMemory, randomSeed)​

Run the Picard ICA algorithm.

Parameters:

  • matData : const Eigen::MatrixXd & Input data (n_channels x n_samples).

  • nComponents : int Number of components to extract (-1 = all channels).

  • maxIter : int Maximum number of iterations (default 200).

  • tol : double Convergence tolerance (default 1e-7).

  • lbfgsMemory : int L-BFGS memory length (default 7).

  • randomSeed : int Seed for initial weight randomisation (default 42).

Returns:

  • IcaResult — IcaResult with mixing/unmixing matrices and source time series.

Authors of this file​