""" .. _ex-custom-inverse: ================================================ 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, eLORETA, LCMV, DICS, (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. """ # Authors: The MNE-Python contributors. # License: BSD-3-Clause # Copyright the MNE-Python contributors. # %% 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() meg_path = data_path / "MEG" / "sample" fwd_fname = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif" ave_fname = meg_path / "sample_audvis-ave.fif" cov_fname = meg_path / "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(picks="meg", exclude="bads") # Handling forward solution forward = mne.read_forward_solution(fwd_fname) # %% # Auxiliary function to run the solver def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8): """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. Parameters ---------- 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 : float in [0, 1] | 'auto' Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. If loose is 0 then the solution is computed with fixed orientation. If loose is 1, it corresponds to free orientations. The default value ('auto') is set to 0.2 for surface-oriented source space and set to 1.0 for volumic or discrete source space. depth : None | float in [0, 1] Depth weighting coefficients. If None, no depth weighting is performed. Returns ------- stc : instance of SourceEstimate The source estimates. """ # Import the necessary private functions from mne.inverse_sparse.mxne_inverse import ( _make_sparse_stc, _prepare_gain, _reapply_source_weighting, is_fixed_orient, ) all_ch_names = evoked.ch_names # Handle depth weighting and whitening (here is no weights) forward, gain, gain_info, whitener, source_weighting, mask = _prepare_gain( forward, evoked.info, noise_cov, pca=False, depth=depth, loose=loose, weights=None, weights_min=None, rank=None, ) # Select channels of interest sel = [all_ch_names.index(name) for name in gain_info["ch_names"]] M = evoked.data[sel] # Whiten data M = np.dot(whitener, 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) stc = _make_sparse_stc( X, active_set, forward, tmin=evoked.times[0], tstep=1.0 / evoked.info["sfreq"] ) return stc # %% # Define your solver def solver(M, G, n_orient): """Run L2 penalized regression and keep 10 strongest locations. Parameters ---------- 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. Returns ------- 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``. """ inner = np.dot(G, G.T) trace = np.trace(inner) K = linalg.solve(inner + 4e-6 * trace * np.eye(G.shape[0]), G).T K /= np.linalg.norm(K, axis=1)[:, None] X = np.dot(K, 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, 0.0 # corresponds to free orientation stc = apply_solver(solver, evoked, forward, noise_cov, loose, depth) # %% # View in 2D and 3D ("glass" brain like 3D plot) plot_sparse_source_estimates(forward["src"], stc, bgcolor=(1, 1, 1), opacity=0.1)