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ICA

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Python equivalent

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

#include <dsp/ica.h>

class UTILSLIB::ICA

Independent Component Analysis using the FastICA algorithm (deflationary, logcosh nonlinearity).

Typical MEG/EEG usage:

// Fit ICA on raw sensor data (n_channels x n_samples)
IcaResult result = ICA::run(matRawData, 20);

// Inspect source waveforms and mark artifact components (e.g. {0, 3})
QVector<int> exclude = {0, 3};
Eigen::MatrixXd matClean = ICA::excludeComponents(matRawData, result, exclude);

Static Methods​

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

Fit FastICA on the given data matrix.

Parameters:

  • matData : const Eigen::MatrixXd & Input data (n_channels x n_samples). Each row is one sensor channel.

  • nComponents : int Number of independent components to extract. Pass -1 (default) to use all channels.

  • maxIter : int Maximum iterations per component (default 200).

  • tol : double Convergence tolerance on |w_new · w_old| - 1 (default 1e-4).

  • randomSeed : int Seed for the random weight initialisation (default 42).

Returns:

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

applyUnmixing(matData, result)​

Project new data through a previously fitted unmixing matrix.

The same mean centering as during fitting is applied.

Parameters:

  • matData : const Eigen::MatrixXd & New data (n_channels x n_samples), same channel order as training data.

  • result : const IcaResult & Result from a previous ICA::run() call.

Returns:

  • Eigen::MatrixXd — Source matrix (n_components x n_samples).

excludeComponents(matData, result, excludedComponents)​

Reconstruct sensor-space data after suppressing selected components.

The excluded component(s) are zeroed in source space before back-projection.

Parameters:

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

  • result : const IcaResult & Result from ICA::run() on matData (or a representative segment).

  • excludedComponents : const QVector< int > & 0-based indices of components to suppress (e.g. ECG, EOG artifacts).

Returns:

  • Eigen::MatrixXd — Cleaned sensor data (n_channels x n_samples).

Authors of this file​