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)

Estimated memory usage: 0 MB