``````

In [ ]:

%matplotlib inline

``````

# 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. :depth: 2

``````

In [ ]:

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')
'sample-oct-6-src.fif'))

``````

## Understanding coordinate frames

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 <rotation and translation matrix_>`_ that define how to rotate and translate points in one coordinate frame to their equivalent locations in another.

:func:`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:

• 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 the `coord_frame` argument to choose which coordinate frame the camera should initially be aligned with.

Let's take a look:

``````

In [ ]:

fig = mne.viz.plot_alignment(raw.info, trans=trans, subject='sample',
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'
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)))

``````

Coordinate frame definitions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. raw:: html

.. role:: pink .. role:: blue .. role:: gray .. role:: magenta .. role:: purple .. role:: green .. role:: red

1. Neuromag/Elekta/MEGIN head coordinate frame ("head", :pink:`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

1. the line between the LPA (:red:`red sphere`) and RPA (:purple:`purple sphere`), and
2. the line perpendicular to this LPA-RPA line one that goes through the Nasion (:green:`green sphere`).

The axes are oriented as X origin→RPA, Y origin→NAS, Z origin→upward (orthogonal to X and Y).

.. note:: 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`_.``````
2. MEG device coordinate frame ("meg", :blue:`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.

``````

.. note:: The HPI coils are shown as :magenta:`magenta spheres`.

``````        Coregistration happens at the beginning of the recording and
the data is stored in ``raw.info['dev_head_t']``.``````
3. MRI coordinate frame ("mri", :gray:`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).

.. note:: 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 ``trans``.``````

Let's try using `trans=None`, which (incorrectly!) equates the MRI and head coordinate frames.

``````

In [ ]:

mne.viz.plot_alignment(raw.info, trans=None, subject='sample', src=src,
subjects_dir=subjects_dir, dig=True,

``````

It is quite clear that the MRI surfaces (head, brain) are not well aligned to the head digitization points (dots).

## A good example

Here is the same plot, this time with the `trans` properly defined (using a precomputed matrix).

``````

In [ ]:

mne.viz.plot_alignment(raw.info, trans=trans, subject='sample',
src=src, subjects_dir=subjects_dir, dig=True,

``````

## Defining the head↔MRI `trans` using the GUI

You can try creating the head↔MRI transform yourself using :func:`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
:func:`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 :func:`mne.read_trans`.

For more information, see step by step instructions ```in these slides <https://www.slideshare.net/mne-python/mnepython-coregistration>```_. Uncomment the following line to align the data yourself.

``````

In [ ]:

# mne.gui.coregistration(subject='sample', subjects_dir=subjects_dir)

``````

## Alignment without MRI

The surface alignments above are possible if you have the surfaces available from Freesurfer. :func:`mne.viz.plot_alignment` automatically searches for the correct surfaces from the provided `subjects_dir`. Another option is to use a `spherical conductor model <eeg_sphere_model>`. It is passed through `bem` parameter.

``````

In [ ]:

It is also possible to use :func:`mne.gui.coregistration` to warp a subject (usually `fsaverage`) to subject digitization data, see ```these slides <https://www.slideshare.net/mne-python/mnepython-scale-mri>```_.