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"""Compute resolution matrix for beamformers."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from .._fiff.pick import pick_channels, pick_channels_forward, pick_info
from ..evoked import EvokedArray
from ..utils import fill_doc, logger
from ._lcmv import apply_lcmv
@fill_doc
def make_lcmv_resolution_matrix(filters, forward, info):
"""Compute resolution matrix for LCMV beamformer.
Parameters
----------
filters : instance of Beamformer
Dictionary containing filter weights from LCMV beamformer
(see mne.beamformer.make_lcmv).
forward : instance of Forward
Forward Solution with leadfield matrix.
%(info_not_none)s Used to compute LCMV filters.
Returns
-------
resmat : array, shape (n_dipoles_lcmv, n_dipoles_fwd)
Resolution matrix (filter matrix multiplied to leadfield from
forward solution). Numbers of rows (n_dipoles_lcmv) and columns
(n_dipoles_fwd) may differ by a factor depending on orientation
constraints of filter and forward solution, respectively (e.g. factor 3
for free dipole orientation versus factor 1 for scalar beamformers).
"""
# don't include bad channels from noise covariance matrix
bads_filt = filters["noise_cov"]["bads"]
ch_names = filters["noise_cov"]["names"]
# good channels
ch_names = [c for c in ch_names if (c not in bads_filt)]
# adjust channels in forward solution
forward = pick_channels_forward(forward, ch_names, ordered=True)
# get leadfield matrix from forward solution
leadfield = forward["sol"]["data"]
# get the filter weights for beamformer as matrix
filtmat = _get_matrix_from_lcmv(filters, forward, info)
# compute resolution matrix
resmat = filtmat.dot(leadfield)
logger.info(f"Dimensions of LCMV resolution matrix: {resmat.shape}.")
return resmat
def _get_matrix_from_lcmv(filters, forward, info, verbose=None):
"""Get inverse matrix for LCMV beamformer.
Returns
-------
invmat : array, shape (n_dipoles, n_channels)
Inverse matrix associated with LCMV beamformer filters.
"""
# number of channels for identity matrix
info = pick_info(info, pick_channels(info["ch_names"], filters["ch_names"]))
n_chs = len(info["ch_names"])
# create identity matrix as input for inverse operator
# set elements to zero for non-selected channels
id_mat = np.eye(n_chs)
# convert identity matrix to evoked data type (pretending it's an epochs
evo_ident = EvokedArray(id_mat, info=info, tmin=0.0)
# apply beamformer to identity matrix
stc_lcmv = apply_lcmv(evo_ident, filters, verbose=verbose)
# turn source estimate into numpsy array
invmat = stc_lcmv.data
return invmat
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