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InvTfMxne

Namespace: INVERSELIB  ·  Library: Inverse Library

Python equivalent

mne.inverse_sparse.tf_mixed_norm in MNE-Python.

#include <inv/inv_tf_mxne.h>

class INVLIB::InvTfMxne

Time-Frequency Mixed-Norm Estimate (TF-MxNE) sparse inverse solver.

Solves the inverse problem in the time-frequency domain: min ||M - GPhiZ||^2_F + alpha_space * ||Z||_21 + alpha_time * ||Z||_1

where Phi is a Gabor dictionary (tight frame) and Z are the TF coefficients. The L21 penalty enforces spatial sparsity (few active sources) while L1 enforces temporal sparsity (focal activations in time-frequency).

Usage:

InvTfMxneParams params;
params.dAlphaSpace = 0.5;
params.dAlphaTime  = 0.1;
params.dSFreq      = 1000.0;
InvTfMxneResult result = InvTfMxne::compute(matGain, matData, params);

Static Methods​

compute(matGain, matData, params)​

Compute the TF-MxNE inverse solution.

Parameters:

  • matGain : const Eigen::MatrixXd & Forward gain matrix (n_channels × n_sources).

  • matData : const Eigen::MatrixXd & Measurement data (n_channels × n_times).

  • params : const InvTfMxneParams & TF-MxNE parameters.

Returns:

  • InvTfMxneResult — TF-MxNE result with sparse source estimate.

buildGaborDictionary(iNSamples, iNFreqs, dFMin, dFMax, dSFreq)​

Build a Gabor dictionary (tight frame) for time-frequency decomposition.

Parameters:

  • iNSamples : int Number of time samples.

  • iNFreqs : int Number of frequency bins.

  • dFMin : double Minimum frequency (Hz).

  • dFMax : double Maximum frequency (Hz).

  • dSFreq : double Sampling frequency (Hz).

Returns:

  • Eigen::MatrixXd — Gabor dictionary matrix (n_atoms × n_samples), where n_atoms = n_freqs * n_samples.

Authors of this file​