Note
Click here to download the full example code
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, and understand the different coordinate frames involved in this process.
Topics
Let’s start out by loading some data.
import os.path as op
import numpy as np
from mayavi import mlab
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')
trans_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
raw = mne.io.read_raw_fif(raw_fname)
trans = mne.read_trans(trans_fname)
src = mne.read_source_spaces(op.join(subjects_dir, 'sample', 'bem',
'sample-oct-6-src.fif'))
For M/EEG source imaging, there are three coordinate frames that we must bring into alignment using two 3D transformation matrices that define how to rotate and translate points in one coordinate frame to their equivalent locations in another.
mne.viz.plot_alignment() is a very useful function for inspecting
these transformations, and the resulting alignment of EEG sensors, MEG
sensors, brain sources, and conductor models. If the subjects_dir and
subject parameters are provided, the function automatically looks for the
Freesurfer MRI surfaces to show from the subject’s folder.
We can use the show_axes argument to see the various coordinate frames
given our transformation matrices. These are shown by axis arrows for each
coordinate frame:
i.e., a RAS coordinate system in each case. We can also set
the coord_frame argument to choose which coordinate
frame the camera should initially be aligned with.
Let’s take a look:
mne.viz.plot_alignment(raw.info, trans=trans, subject='sample',
subjects_dir=subjects_dir, surfaces='head-dense',
show_axes=True, dig=True, eeg=[], meg='sensors',
coord_frame='meg')
mlab.view(45, 90, distance=0.6, focalpoint=(0., 0., 0.))
print('Distance from head origin to MEG origin: %0.1f mm'
% (1000 * np.linalg.norm(raw.info['dev_head_t']['trans'][:3, 3])))
print('Distance from head origin to MRI origin: %0.1f mm'
% (1000 * np.linalg.norm(trans['trans'][:3, 3])))
Out:
Distance from head origin to MEG origin: 65.0 mm
Distance from head origin to MRI origin: 29.9 mm
Defined by the intersection of 1) the line between the LPA (red sphere) and RPA (purple sphere), and 2) the line perpendicular to this LPA-RPA line one that goes through the Nasion (green sphere). The axes are oriented as X origin→RPA, Y origin→Nasion, Z origin→upward (orthogonal to X and Y).
Note
This gets defined during the head digitization stage during acquisition, often by use of a Polhemus or other digitizer.
This is defined by the MEG manufacturers. From the Elekta user manual:
The origin of the device coordinate system is located at the center of the posterior spherical section of the helmet with axis going from left to right and axis pointing front. The axis is, again normal to the plane with positive direction up.
Note
The device is coregistered with the head coordinate frame
during acquisition via emission of sinusoidal currents in
head position indicator (HPI) coils
(magenta spheres) at the beginning of the
recording. This is stored in raw.info['dev_head_t'].
Defined by Freesurfer, the MRI (surface RAS) origin is at the center of a 256×256×256 1mm anisotropic volume (may not be in the center of the head).
Note
This is aligned to the head coordinate frame that we
typically refer to in MNE as trans.
Let’s try using trans=None, which (incorrectly!) equates the MRI
and head coordinate frames.
mne.viz.plot_alignment(raw.info, trans=None, subject='sample', src=src,
subjects_dir=subjects_dir, dig=True,
surfaces=['head-dense', 'white'], coord_frame='meg')
It is quite clear that the MRI surfaces (head, brain) are not well aligned to the head digitization points (dots).
Here is the same plot, this time with the trans properly defined
(using a precomputed matrix).
mne.viz.plot_alignment(raw.info, trans=trans, subject='sample',
src=src, subjects_dir=subjects_dir, dig=True,
surfaces=['head-dense', 'white'], coord_frame='meg')
trans using the GUI¶You can try creating the head↔MRI transform yourself using
mne.gui.coregistration().
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().
For more information, see step by step instructions in these slides. Uncomment the following line to align the data yourself.
# mne.gui.coregistration(subject='sample', subjects_dir=subjects_dir)
The surface alignments above are possible if you have the surfaces available
from Freesurfer. mne.viz.plot_alignment() automatically searches for
the correct surfaces from the provided subjects_dir. Another option is
to use a spherical conductor model. It is passed through bem parameter.
sphere = mne.make_sphere_model(info=raw.info, r0='auto', head_radius='auto')
src = mne.setup_volume_source_space(sphere=sphere, pos=10.)
mne.viz.plot_alignment(
raw.info, eeg='projected', bem=sphere, src=src, dig=True,
surfaces=['brain', 'outer_skin'], coord_frame='meg', show_axes=True)
It is also possible to use mne.gui.coregistration()
to warp a subject (usually fsaverage) to subject digitization data, see
these slides.
Total running time of the script: ( 0 minutes 18.671 seconds)