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InvDICS

Namespace: INVERSELIB  ·  Library: Inverse Library

Python equivalent

mne.beamformer.make_dics / apply_dics in MNE-Python.

#include <inv/inv_dics.h>

class INVLIB::InvDICS

Dynamic Imaging of Coherent Sources (DICS) beamformer for frequency-domain source localization using cross-spectral density (CSD) matrices.

Typical workflow:

// 1. Compute CSD matrices for frequency bands of interest (externally)
//    csdMatrices[i] = CSD at frequency[i], shape (n_channels, n_channels)

// 2. Build the DICS spatial filter
InvBeamformer filters = InvDICS::makeDICS(info, forward, csdMatrices, frequencies,
                                           reg, realFilter, pickOri, weightNorm);

// 3. Apply to CSD to get source power per frequency
InvSourceEstimate power = InvDICS::applyDICSCsd(csdMatrices, frequencies, filters);

DICS beamformer (frequency-domain).

Static Methods​

makeDICS(info, forward, csdMatrices, frequencies, reg, realFilter, noiseCov, pickOri, weightNorm, reduceRank, invMethod)​

Compute DICS beamformer spatial filters for one or more frequencies.

Parameters:

  • info : const FiffInfo & Measurement info.

  • forward : const MNEForwardSolution & Forward solution containing the leadfield.

  • csdMatrices : const std::vector< Eigen::MatrixXd > & Cross-spectral density matrices, one per frequency bin. Each is (n_channels, n_channels), may be complex.

  • frequencies : const Eigen::VectorXd & Center frequency (Hz) for each CSD matrix.

  • reg : double Regularization parameter (default 0.05).

  • realFilter : bool If true, take real part of CSD before computing filter (default true).

  • noiseCov : const FiffCov & Noise covariance for whitening. If empty, identity whitening.

  • pickOri : BeamformerPickOri Orientation selection mode.

  • weightNorm : BeamformerWeightNorm Weight normalization strategy.

  • reduceRank : bool Reduce leadfield rank by 1.

  • invMethod : BeamformerInversion Denominator inversion method.

Returns:

  • InvBeamformer — Beamformer with one filter weight matrix per frequency.

applyDICSCsd(csdMatrices, frequencies, filters)​

Apply DICS beamformer to CSD matrices to estimate source power per frequency.

power_i(f) = trace(W_f_i @ CSD_f @ W_f_i^T)

Parameters:

  • csdMatrices : const std::vector< Eigen::MatrixXd > & CSD matrices (one per frequency).

  • frequencies : const Eigen::VectorXd & Center frequencies (Hz).

  • filters : const InvBeamformer & Pre-computed DICS beamformer from makeDICS().

Returns:

  • InvSourceEstimate — Source estimate where data has shape (n_sources, n_freqs) with power values.

applyDICS(data, tmin, tstep, filters, freqIdx)​

Apply DICS beamformer to time-domain data at a single frequency.

This is an approximate application — for accurate frequency-domain source estimation, use applyDICSCsd() with CSD matrices.

Parameters:

  • data : const Eigen::MatrixXd & Data matrix (n_channels, n_times).

  • tmin : float Start time (s).

  • tstep : float Time step (s).

  • filters : const InvBeamformer & Pre-computed DICS beamformer (must have exactly 1 frequency).

  • freqIdx : int Index of the frequency filter to use (default 0).

Returns:

applyDICSEpochs(epochs, tmin, tstep, filters, freqIdx)​

Apply DICS beamformer to each epoch in a list.

Parameters:

  • epochs : const QList< Eigen::MatrixXd > & List of epoch data matrices (n_channels x n_times each).

  • tmin : float Start time of each epoch (seconds).

  • tstep : float Time step (1/sfreq).

  • filters : const InvBeamformer & Pre-computed DICS beamformer from makeDICS().

  • freqIdx : int Index of the frequency filter to use (default 0).

Returns:

Authors of this file​