The aim of this tutorial is to show how to visually assess that the data are well aligned in space for computing the forward solution.
import os.path as op
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
tr_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw-trans.fif')
raw = mne.io.read_raw_fif(raw_fname)
mne.viz.plot_alignment()
is a very useful function for inspecting
the surface alignment before source analysis. If the subjects_dir
and
subject
parameters are provided, the function automatically looks for the
Freesurfer surfaces from the subject’s folder. Here we use trans=None, which
(incorrectly!) equates the MRI and head coordinate frames.
mne.viz.plot_alignment(raw.info, trans=None, subject='sample',
subjects_dir=subjects_dir, surfaces=['head', 'brain'])
It is quite clear that things are not well aligned for estimating the
sources. We need to provide the function with a transformation that aligns
the MRI with the MEG data. Here we use a precomputed matrix, but you can try
creating it yourself using mne.gui.coregistration()
.
Uncomment the following line to align the data yourself.
First you must load the digitization data from the raw file
(Head Shape Source
). The MRI data is already loaded if you provide the
subject
and subjects_dir
. Toggle Always Show Head Points
to see
the digitization points.
To set the landmarks, toggle Edit
radio button in MRI Fiducials
.
Set the landmarks by clicking the radio button (LPA, Nasion, RPA) and then clicking the corresponding point in the image.
After doing this for all the landmarks, toggle Lock
radio button. You
can omit outlier points, so that they don’t interfere with the finetuning.
Note
You can save the fiducials to a file and pass
mri_fiducials=True
to plot them in
mne.viz.plot_alignment()
. The fiducials are saved to the
subject’s bem folder by default.
Click Fit Head Shape
. This will align the digitization points to the
head surface. Sometimes the fitting algorithm doesn’t find the correct
alignment immediately. You can try first fitting using LPA/RPA or fiducials
and then align according to the digitization. You can also finetune
manually with the controls on the right side of the panel.
Click Save As...
(lower right corner of the panel), set the filename
and read it with mne.read_trans()
.
# mne.gui.coregistration(subject='sample', subjects_dir=subjects_dir)
trans = mne.read_trans(tr_fname)
src = mne.read_source_spaces(op.join(data_path, 'MEG', 'sample',
'sample_audvis-meg-oct-6-meg-inv.fif'))
mne.viz.plot_alignment(raw.info, trans=trans, subject='sample', src=src,
subjects_dir=subjects_dir, surfaces=['head', 'white'])
The previous is possible if you have the surfaces available from Freesurfer.
The function automatically searches for the correct surfaces from the
provided subjects_dir
. Otherwise it is possible to use the sphere
conductor model. It is passed through bem
parameter.
Note
bem
also accepts bem solutions (mne.read_bem_solution()
)
or a list of bem surfaces (mne.read_bem_surfaces()
).
sphere = mne.make_sphere_model(info=raw.info, r0='auto', head_radius='auto')
mne.viz.plot_alignment(raw.info, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
For more information see step by step instructions for subjects with structural MRI and for subjects for which no MRI is available.
Total running time of the script: ( 0 minutes 6.653 seconds)