Source localization with a custom inverse solver

The objective of this example is to show how to plug a custom inverse solver in MNE in order to facilate empirical comparison with the methods MNE already implements (wMNE, dSPM, sLORETA, LCMV, (TF-)MxNE etc.).

This script is educational and shall be used for methods evaluations and new developments. It is not meant to be an example of good practice to analyse your data.

The example makes use of 2 functions apply_solver and solver so changes can be limited to the solver function (which only takes three parameters: the whitened data, the gain matrix, and the number of orientations) in order to try out another inverse algorithm.

import numpy as np
from scipy import linalg
import mne
from mne.datasets import sample
from mne.viz import plot_sparse_source_estimates

data_path = sample.data_path()
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-shrunk-cov.fif'
subjects_dir = data_path + '/subjects'
condition = 'Left Auditory'

# Read noise covariance matrix
noise_cov = mne.read_cov(cov_fname)
# Handling average file
evoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))
evoked.crop(tmin=0.04, tmax=0.18)

evoked = evoked.pick_types(eeg=False, meg=True)
# Handling forward solution
forward = mne.read_forward_solution(fwd_fname, surf_ori=True)


365 x 365 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 59) active
Reading /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
Reading forward solution from /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    MEG and EEG forward solutions combined
    Source spaces transformed to the forward solution coordinate frame
    Converting to surface-based source orientations...
    Average patch normals will be employed in the rotation to the local surface coordinates....

Auxiliary function to run the solver

def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8):
    """Function to call a custom solver on evoked data

    This function does all the necessary computation:

    - to select the channels in the forward given the available ones in
      the data
    - to take into account the noise covariance and do the spatial whitening
    - to apply loose orientation constraint as MNE solvers
    - to apply a weigthing of the columns of the forward operator as in the
      weighted Minimum Norm formulation in order to limit the problem
      of depth bias.

    solver : callable
        The solver takes 3 parameters: data M, gain matrix G, number of
        dipoles orientations per location (1 or 3). A solver shall return
        2 variables: X which contains the time series of the active dipoles
        and an active set which is a boolean mask to specify what dipoles are
        present in X.
    evoked : instance of mne.Evoked
        The evoked data
    forward : instance of Forward
        The forward solution.
    noise_cov : instance of Covariance
        The noise covariance.
    loose : None | float in [0, 1]
        Value that weights the source variances of the dipole components
        defining the tangent space of the cortical surfaces. Requires surface-
        based, free orientation forward solutions.
    depth : None | float in [0, 1]
        Depth weighting coefficients. If None, no depth weighting is performed.

    stc : instance of SourceEstimate
        The source estimates.
    # Import the necessary private functions
    from mne.inverse_sparse.mxne_inverse import \
        (_prepare_gain, _to_fixed_ori, is_fixed_orient,
         _reapply_source_weighting, _make_sparse_stc)

    all_ch_names = evoked.ch_names
    # put the forward solution in fixed orientation if it's not already
    if loose is None and not is_fixed_orient(forward):
        forward = forward.copy()

    # Handle depth weighting and whitening (here is no weights)
    gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
        forward,, noise_cov, pca=False, depth=depth,
        loose=loose, weights=None, weights_min=None)

    # Select channels of interest
    sel = [all_ch_names.index(name) for name in gain_info['ch_names']]
    M =[sel]

    # Whiten data
    M =, M)

    n_orient = 1 if is_fixed_orient(forward) else 3
    X, active_set = solver(M, gain, n_orient)
    X = _reapply_source_weighting(X, source_weighting, active_set, n_orient)

    stc = _make_sparse_stc(X, active_set, forward, tmin=evoked.times[0],
                           tstep=1. /['sfreq'])

    return stc

Define your solver

def solver(M, G, n_orient):
    """Dummy solver

    It just runs L2 penalized regression and keep the 10 strongest locations

    M : array, shape (n_channels, n_times)
        The whitened data.
    G : array, shape (n_channels, n_dipoles)
        The gain matrix a.k.a. the forward operator. The number of locations
        is n_dipoles / n_orient. n_orient will be 1 for a fixed orientation
        constraint or 3 when using a free orientation model.
    n_orient : int
        Can be 1 or 3 depending if one works with fixed or free orientations.
        If n_orient is 3, then ``G[:, 2::3]`` corresponds to the dipoles that
        are normal to the cortex.

    X : array, (n_active_dipoles, n_times)
        The time series of the dipoles in the active set.
    active_set : array (n_dipoles)
        Array of bool. Entry j is True if dipole j is in the active set.
        We have ``X_full[active_set] == X`` where X_full is the full X matrix
        such that ``M = G X_full``.
    K = linalg.solve(, G.T) + 1e15 * np.eye(G.shape[0]), G).T
    K /= np.linalg.norm(K, axis=1)[:, None]
    X =, M)

    indices = np.argsort(np.sum(X ** 2, axis=1))[-10:]
    active_set = np.zeros(G.shape[1], dtype=bool)
    for idx in indices:
        idx -= idx % n_orient
        active_set[idx:idx + n_orient] = True
    X = X[active_set]
    return X, active_set

Apply your custom solver

# loose, depth = 0.2, 0.8  # corresponds to loose orientation
loose, depth = 1., 0.  # corresponds to free orientation
stc = apply_solver(solver, evoked, forward, noise_cov, loose, depth)


info["bads"] and noise_cov["bads"] do not match, excluding bad channels from both
Computing inverse operator with 305 channels.
    Created an SSP operator (subspace dimension = 3)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
Total rank is 302
Whitening lead field matrix.
combining the current components...

View in 2D and 3D (“glass” brain like 3D plot)

plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),
  • ../../_images/sphx_glr_plot_custom_inverse_solver_001.png
  • ../../_images/sphx_glr_plot_custom_inverse_solver_002.png


Total number of active sources: 10

Total running time of the script: ( 0 minutes 19.237 seconds)

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