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InvMxneResult

Namespace: INVLIB  ·  Library: Inverse Library

#include <inv/inv_mxne.h>

struct INVLIB::InvMxneResult

Result structure for the MxNE solver.

src/examples/ex_inv_api/main.cpp
// Whitened data and a gain per nAm make the noise level 1, as mne.inverse_sparse.mixed_norm does.
const auto [whitener, rank] = computeWhitener(*inverse.noise_cov); // rank 360: 364 channels minus 4 projectors
QFile fixedFile(fwdFile.fileName());
const MNEForwardSolution fixedFwd = MNEForwardSolution(fixedFile, true, true).pick_regions({v1}).pick_channels(inverse.noise_cov->names);
const MatrixXd gain = whitener * fixedFwd.sol->data * 1e-9;
const MatrixXd measured = whitener * evoked.pick_channels(inverse.noise_cov->names).data.middleCols(170, 40); // 83-148 ms
const double alphaMax = (gain.transpose() * measured).rowwise().norm().maxCoeff();
const InvMxneResult mxne = InvMxne::compute(gain, measured, 0.5 * alphaMax, 1000, 1e-10);

Example​

Used in examples​

ex_inv_api​

Inverse library: minimum-norm estimates, resolution matrices, label time courses, sparse solvers.

src/examples/ex_inv_api/main.cpp, line 240
const MatrixXd gain = whitener * fixedFwd.sol->data * 1e-9;
const MatrixXd measured = whitener * evoked.pick_channels(inverse.noise_cov->names).data.middleCols(170, 40); // 83-148 ms
const double alphaMax = (gain.transpose() * measured).rowwise().norm().maxCoeff();
const InvMxneResult mxne = InvMxne::compute(gain, measured, 0.5 * alphaMax, 1000, 1e-10);
// mne mixed_norm_solver(M, G, 0.5 * alpha_max, n_orient=1, debias=False): sources 8, 30, 37 with 21.37, 1.019, 4.455 nAm
ok &= expect(rank == 360 && near(alphaMax, 14.508623946749108, 1e-4) && mxne.activeVertices == QVector<int>({8, 30, 37}) && near(mxne.stc.data.row(0).norm(), 21.373078745157443, 1e-3) && near(mxne.stc.data.row(2).norm(), 4.455019654645694, 1e-3),
             "computeWhitener/InvMxne: whitened rank 360, MxNE picks the 3 V1 sources mne.inverse_sparse finds");

Authors of this file​