Source alignment and coordinate frames¶
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.
Let’s start out by loading some data.
import os.path as op import numpy as np 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'))
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 192599 = 42.956 ... 320.670 secs Ready. Current compensation grade : 0 Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] 2 source spaces read
For M/EEG source imaging, there are three coordinate frames (further explained in the next section) 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
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
shortest arrow is (R)ight/X
medium is forward/(A)nterior/Y
longest is up/(S)uperior/Z
i.e., a RAS coordinate system in each case. We can also set
coord_frame argument to choose which coordinate
frame the camera should initially be aligned with.
Let’s take a look:
fig = 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') mne.viz.set_3d_view(fig, 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]))) dists = mne.dig_mri_distances(raw.info, trans, 'sample', subjects_dir=subjects_dir) print('Distance from %s digitized points to head surface: %0.1f mm' % (len(dists), 1000 * np.mean(dists)))
Using lh.seghead for head surface. Distance from head origin to MEG origin: 65.0 mm Distance from head origin to MRI origin: 29.9 mm Using surface from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-head.fif. Distance from 72 digitized points to head surface: 1.7 mm
- Neuromag/Elekta/MEGIN head coordinate frame (“head”, pink axes)
The head coordinate frame is defined through the coordinates of anatomical landmarks on the subject’s head: Usually the Nasion (NAS), and the left and right preauricular points (LPA and RPA). Different MEG manufacturers may have different definitions of the coordinate head frame. A good overview can be seen in the FieldTrip FAQ on coordinate systems.
For Neuromag/Elekta/MEGIN, the head coordinate frame is defined by the intersection of
the line between the LPA (red sphere) and RPA (purple sphere), and
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→NAS, Z origin→upward (orthogonal to X and Y).
The required 3D coordinates for defining the head coordinate frame (NAS, LPA, RPA) are measured at a stage separate from the MEG data recording. There exist numerous devices to perform such measurements, usually called “digitizers”. For example, see the devices by the company Polhemus.
- MEG device coordinate frame (“meg”, blue axes)
The MEG device coordinate frame is defined by the respective MEG manufacturers. All MEG data is acquired with respect to this coordinate frame. To account for the anatomy and position of the subject’s head, we use so-called head position indicator (HPI) coils. The HPI coils are placed at known locations on the scalp of the subject and emit high-frequency magnetic fields used to coregister the head coordinate frame with the device coordinate frame.
From the Neuromag/Elekta/MEGIN user manual:
The origin of the device coordinate system is located at the center of the posterior spherical section of the helmet with X axis going from left to right and Y axis pointing front. The Z axis is, again normal to the plane with positive direction up.
The HPI coils are shown as magenta spheres. Coregistration happens at the beginning of the recording and the data is stored in
- MRI coordinate frame (“mri”, gray axes)
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).
We typically align the MRI coordinate frame to the head coordinate frame through a rotation and translation matrix, that we refer to in MNE as
Let’s try using
trans=None, which (incorrectly!) equates the MRI
and head coordinate frames.
Using lh.seghead for head surface. Getting helmet for system 306m
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).
Using lh.seghead for head surface. Getting helmet for system 306m
You can try creating the head↔MRI transform yourself using
First you must load the digitization data from the raw file (
Head Shape Source). The MRI data is already loaded if you provide the
Always Show Head Pointsto see the digitization points.
To set the landmarks, toggle
Editradio button in
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
Lockradio button. You can omit outlier points, so that they don’t interfere with the finetuning.
You can save the fiducials to a file and pass
mri_fiducials=Trueto plot them in
mne.viz.plot_alignment(). The fiducials are saved to the subject’s bem folder by default.
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.
Save As...(lower right corner of the panel), set the filename and read it with
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
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
Fitted sphere radius: 91.0 mm Origin head coordinates: -4.1 16.0 51.7 mm Origin device coordinates: 1.4 17.8 -10.3 mm Equiv. model fitting -> RV = 0.00349028 % mu1 = 0.944696 lambda1 = 0.137193 mu2 = 0.667458 lambda2 = 0.683737 mu3 = -0.26815 lambda3 = -0.0105603 Set up EEG sphere model with scalp radius 91.0 mm Sphere : origin at (-4.1 16.0 51.7) mm radius : 81.9 mm grid : 10.0 mm mindist : 5.0 mm Setting up the sphere... Surface CM = ( -4.1 16.0 51.7) mm Surface fits inside a sphere with radius 81.9 mm Surface extent: x = -86.0 ... 77.8 mm y = -65.9 ... 97.9 mm z = -30.2 ... 133.7 mm Grid extent: x = -90.0 ... 80.0 mm y = -70.0 ... 100.0 mm z = -40.0 ... 140.0 mm 6156 sources before omitting any. 2300 sources after omitting infeasible sources not within 0.0 - 81.9 mm. 1904 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside. Adjusting the neighborhood info. Source space : MRI voxel -> MRI (surface RAS) 0.010000 0.000000 0.000000 -90.00 mm 0.000000 0.010000 0.000000 -70.00 mm 0.000000 0.000000 0.010000 -40.00 mm 0.000000 0.000000 0.000000 1.00 Getting helmet for system 306m Triangle neighbors and vertex normals...
Total running time of the script: ( 0 minutes 44.439 seconds)
Estimated memory usage: 21 MB