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"""
.. _tut-mne-fixed-free:
===============================
Computing various MNE solutions
===============================
This example shows example fixed- and free-orientation source localizations
produced by the minimum-norm variants implemented in MNE-Python:
MNE, dSPM, sLORETA, and eLORETA.
"""
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
# %%
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, apply_inverse_cov, make_inverse_operator
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path / "subjects"
# Read data (just MEG here for speed, though we could use MEG+EEG)
meg_path = data_path / "MEG" / "sample"
fname_evoked = meg_path / "sample_audvis-ave.fif"
evoked = mne.read_evokeds(fname_evoked, condition="Right Auditory", baseline=(None, 0))
fname_fwd = meg_path / "sample_audvis-meg-oct-6-fwd.fif"
fname_cov = meg_path / "sample_audvis-cov.fif"
fwd = mne.read_forward_solution(fname_fwd)
cov = mne.read_cov(fname_cov)
# crop for speed in these examples
evoked.crop(0.05, 0.15)
# %%
# Fixed orientation
# -----------------
# First let's create a fixed-orientation inverse, with the default weighting.
inv = make_inverse_operator(evoked.info, fwd, cov, loose=0.0, depth=0.8, verbose=True)
# %%
# Let's look at the current estimates using MNE. We'll take the absolute
# value of the source estimates to simplify the visualization.
snr = 3.0
lambda2 = 1.0 / snr**2
kwargs = dict(
initial_time=0.08,
hemi="lh",
subjects_dir=subjects_dir,
size=(600, 600),
clim=dict(kind="percent", lims=[90, 95, 99]),
smoothing_steps=10,
)
stc = abs(apply_inverse(evoked, inv, lambda2, "MNE", verbose=True))
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "MNE", "title", font_size=14)
# %%
# Next let's use the default noise normalization, dSPM:
stc = abs(apply_inverse(evoked, inv, lambda2, "dSPM", verbose=True))
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "dSPM", "title", font_size=14)
# %%
# And sLORETA:
stc = abs(apply_inverse(evoked, inv, lambda2, "sLORETA", verbose=True))
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "sLORETA", "title", font_size=14)
# %%
# And finally eLORETA:
stc = abs(apply_inverse(evoked, inv, lambda2, "eLORETA", verbose=True))
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "eLORETA", "title", font_size=14)
del inv
# %%
# Free orientation
# ----------------
# Now let's not constrain the orientation of the dipoles at all by creating
# a free-orientation inverse.
inv = make_inverse_operator(evoked.info, fwd, cov, loose=1.0, depth=0.8, verbose=True)
del fwd
# %%
# Let's look at the current estimates using MNE. We'll take the absolute
# value of the source estimates to simplify the visualization.
stc = apply_inverse(evoked, inv, lambda2, "MNE", verbose=True)
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "MNE", "title", font_size=14)
# %%
# Next let's use the default noise normalization, dSPM:
stc = apply_inverse(evoked, inv, lambda2, "dSPM", verbose=True)
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "dSPM", "title", font_size=14)
# %%
# sLORETA:
stc = apply_inverse(evoked, inv, lambda2, "sLORETA", verbose=True)
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "sLORETA", "title", font_size=14)
# %%
# And finally eLORETA:
stc = apply_inverse(
evoked, inv, lambda2, "eLORETA", verbose=True, method_params=dict(eps=1e-4)
) # larger eps just for speed
brain = stc.plot(**kwargs)
brain.add_text(0.1, 0.9, "eLORETA", "title", font_size=14)
# %%
# And one interesting property to note is the noise normalization of dSPM
# can be easily seen by visualizing the source reconstruction of the noise
# covariance used to compute the inverse operator -- it's takes on the
# value of 1. (orange in the colormap here) across the entire brain:
stc_baseline = apply_inverse_cov(
cov, evoked.info, inv, lambda2=lambda2, method="dSPM", verbose=True
)
kwargs_baseline = kwargs.copy()
kwargs_baseline["clim"] = dict(kind="value", lims=[0, 1, 2])
brain = stc_baseline.plot(**kwargs_baseline)
brain.add_text(0.1, 0.9, "dSPM of the baseline", "title", font_size=14)
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