EEG source localization given electrode locations on an MRI¶
This tutorial explains how to compute the forward operator from EEG data when the electrodes are in MRI voxel coordinates.
This tutorial covers:
For this we will assume that you have:
raw EEG data
your subject’s MRI reconstrcted using FreeSurfer
an appropriate boundary element model (BEM)
an appropriate source space (src)
your EEG electrodes in Freesurfer surface RAS coordinates, stored in one of the formats
Let’s set the paths to these files for the
sample dataset, including
sample MRI showing the electrode locations plus a
file corresponding to the points in MRI coords (these were synthesized,
and thus are stored as part of the
data_path = mne.datasets.sample.data_path() subjects_dir = op.join(data_path, 'subjects') fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif') bem_dir = op.join(subjects_dir, 'sample', 'bem') fname_bem = op.join(bem_dir, 'sample-5120-5120-5120-bem-sol.fif') fname_src = op.join(bem_dir, 'sample-oct-6-src.fif') misc_path = mne.datasets.misc.data_path() fname_T1_electrodes = op.join(misc_path, 'sample_eeg_mri', 'T1_electrodes.mgz') fname_mon = op.join(misc_path, 'sample_eeg_mri', 'sample_mri_montage.elc')
Let’s take our MRI-with-eeg-locations and adjust the affine to put the data
in MNI space, and plot using
which does a maximum intensity projection (easy to see the fake electrodes).
This plotting function requires data to be in MNI space.
img.affine gives the voxel-to-world (RAS) mapping, if we apply a
RAS-to-MNI transform to it, it becomes the voxel-to-MNI transformation we
need. Thus we create a “new” MRI image in MNI coordinates and plot it as:
img = nibabel.load(fname_T1_electrodes) # original subject MRI w/EEG ras_mni_t = mne.transforms.read_ras_mni_t('sample', subjects_dir) # from FS mni_affine = np.dot(ras_mni_t['trans'], img.affine) # vox->ras->MNI img_mni = nibabel.Nifti1Image(img.dataobj, mni_affine) # now in MNI coords! plot_glass_brain(img_mni, cmap='hot_black_bone', threshold=0., black_bg=True, resampling_interpolation='nearest', colorbar=True)
If you have voxel coordinates in MRI voxels, you can transform these to
FreeSurfer surface RAS (called “mri” in MNE) coordinates using the
transformations that FreeSurfer computes during reconstruction.
nibabel calls this transformation the
and operates in millimeters, so we can load it, convert it to meters,
and then apply it:
You can also verify that these are correct (or manually convert voxels to MRI coords) by looking at the points in Freeview or tkmedit.
Creating RawArray with float64 data, n_channels=61, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready.
We can then get our transformation from the MRI coordinate frame (where our points are defined) to the head coordinate frame from the object.
<Transform | MRI (surface RAS)->head> [[ 0.99930957 0.00998471 -0.03578661 -0.00316747] [ 0.01275917 0.81240435 0.58295487 0.00685511] [ 0.03489383 -0.58300899 0.81171605 0.02888406] [ 0. 0. 0. 1. ]]
Let’s apply this digitization to our dataset, and in the process
automatically convert our locations to the head coordinate frame, as
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. Removing projector <Projection | PCA-v1, active : False, n_channels : 102> Removing projector <Projection | PCA-v2, active : False, n_channels : 102> Removing projector <Projection | PCA-v3, active : False, n_channels : 102> Reading 0 ... 166799 = 0.000 ... 277.714 secs...
Now we can do standard sensor-space operations like make joint plots of evoked data.
Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. 320 events found Event IDs: [ 1 2 3 4 5 32] Not setting metadata Not setting metadata 320 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] sec Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 1) 1 projection items activated Loading data for 72 events and 421 original time points ... 0 bad epochs dropped Loading data for 73 events and 421 original time points ... 0 bad epochs dropped Loading data for 73 events and 421 original time points ... 0 bad epochs dropped Loading data for 71 events and 421 original time points ... 0 bad epochs dropped Loading data for 15 events and 421 original time points ... 0 bad epochs dropped Loading data for 16 events and 421 original time points ... 0 bad epochs dropped Computing rank from data with rank=None Using tolerance 7.7e-11 (2.2e-16 eps * 59 dim * 5.9e+03 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Created an SSP operator (subspace dimension = 1) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 59 -> 58 Estimating covariance using EMPIRICAL Done. Number of samples used : 38720 [done] Projections have already been applied. Setting proj attribute to True.
New we have all of the components we need to compute a forward solution, but first we should sanity check that everything is well aligned:
Using lh.seghead for head surface.
Now we can actually compute the forward:
Source space : /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-oct-6-src.fif MRI -> head transform : instance of Transform Measurement data : instance of Info Conductor model : /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif Accurate field computations Do computations in head coordinates Free source orientations Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-oct-6-src.fif... Read 2 source spaces a total of 8196 active source locations Coordinate transformation: MRI (surface RAS) -> head 0.999310 0.009985 -0.035787 -3.17 mm 0.012759 0.812404 0.582955 6.86 mm 0.034894 -0.583009 0.811716 28.88 mm 0.000000 0.000000 0.000000 1.00 Read 60 EEG channels from info Head coordinate coil definitions created. Source spaces are now in head coordinates. Setting up the BEM model using /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif... Loading surfaces... Loading the solution matrix... Three-layer model surfaces loaded. Loaded linear_collocation BEM solution from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif Employing the head->MRI coordinate transform with the BEM model. BEM model sample-5120-5120-5120-bem-sol.fif is now set up Source spaces are in head coordinates. Checking that the sources are inside the surface (will take a few...) Skipping interior check for 846 sources that fit inside a sphere of radius 43.6 mm Skipping solid angle check for 2 points using Qhull 2 source space points omitted because they are outside the inner skull surface. Computing patch statistics... Patch information added... Skipping interior check for 875 sources that fit inside a sphere of radius 43.6 mm Skipping solid angle check for 0 points using Qhull 1 source space point omitted because it is outside the inner skull surface. Computing patch statistics... Patch information added... Setting up for EEG... Computing EEG at 8193 source locations (free orientations)... Finished.
Finally let’s compute the inverse and apply it:
Converting forward solution to surface orientation Average patch normals will be employed in the rotation to the local surface coordinates.... Converting to surface-based source orientations... [done] info["bads"] and noise_cov["bads"] do not match, excluding bad channels from both Computing inverse operator with 59 channels. 59 out of 60 channels remain after picking Selected 59 channels Creating the depth weighting matrix... 59 EEG channels limit = 8192/8193 = 10.040598 scale = 157084 exp = 0.8 Applying loose dipole orientations to surface source spaces: 0.2 Whitening the forward solution. Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 1.2e-13 (2.2e-16 eps * 59 dim * 9 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Creating the source covariance matrix Adjusting source covariance matrix. Computing SVD of whitened and weighted lead field matrix. largest singular value = 2.86184 scaling factor to adjust the trace = 3.24877e+19 Preparing the inverse operator for use... Scaled noise and source covariance from nave = 1 to nave = 72 Created the regularized inverter Created an SSP operator (subspace dimension = 1) Created the whitener using a noise covariance matrix with rank 58 (1 small eigenvalues omitted) Computing noise-normalization factors (dSPM)... [done] Applying inverse operator to "1"... Picked 59 channels from the data Computing inverse... Eigenleads need to be weighted ... Computing residual... Explained 85.0% variance Combining the current components... dSPM... [done] Using control points [ 4.17591891 4.939572 10.86348066]
Total running time of the script: ( 0 minutes 51.082 seconds)
Estimated memory usage: 680 MB