EEG forward operator with a template MRI

This tutorial explains how to compute the forward operator from EEG data using the standard template MRI subject fsaverage.

Caution

Source reconstruction without an individual T1 MRI from the subject will be less accurate. Do not over interpret activity locations which can be off by multiple centimeters.

Adult template MRI (fsaverage)

First we show how fsaverage can be used as a surrogate subject.

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Joan Massich <mailsik@gmail.com>
#          Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne
from mne.datasets import eegbci, fetch_fsaverage

# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = fs_dir.parent

# The files live in:
subject = "fsaverage"
trans = "fsaverage"  # MNE has a built-in fsaverage transformation
src = fs_dir / "bem" / "fsaverage-ico-5-src.fif"
bem = fs_dir / "bem" / "fsaverage-5120-5120-5120-bem-sol.fif"

Load the data

We use here EEG data from the BCI dataset.

Note

See Plotting sensor layouts of EEG systems to view all the standard EEG montages available in MNE-Python.

(raw_fname,) = eegbci.load_data(subjects=1, runs=[6])
raw = mne.io.read_raw_edf(raw_fname, preload=True)

# Clean channel names to be able to use a standard 1005 montage
eegbci.standardize(raw)

# Read and set the EEG electrode locations using an anatomically
# shaped 10-05 montage:
montage = mne.channels.make_standard_montage("fsaverage_1005")
raw.set_montage(montage)
raw.set_eeg_reference(projection=True)  # needed for inverse modeling

# Check that the locations of EEG electrodes is correct with respect to MRI
mne.viz.plot_alignment(
    raw.info,
    src=src,
    eeg=["original", "projected"],
    trans=trans,
    show_axes=True,
    mri_fiducials=True,
    dig="fiducials",
)

Setup source space and compute forward

fwd = mne.make_forward_solution(
    raw.info, trans=trans, src=src, bem=bem, eeg=True, mindist=5.0, n_jobs=None
)
fwd

From here on, standard inverse imaging methods can be used!

Infant MRI surrogates

We don’t have a sample infant dataset for MNE, so let’s fake a 10-20 one:

ch_names = "Fz Cz Pz Oz Fp1 Fp2 F3 F4 F7 F8 C3 C4 T7 T8 P3 P4 P7 P8 O1 O2".split()
data = np.random.default_rng(0).standard_normal((len(ch_names), 1000))
info = mne.create_info(ch_names, 1000.0, "eeg")
raw = mne.io.RawArray(data, info)

Get an infant MRI template

To use an infant head model for M/EEG data, you can use mne.datasets.fetch_infant_template() to download an infant template:

subject = mne.datasets.fetch_infant_template("6mo", subjects_dir, verbose=True)

It comes with several helpful built-in files, including a 10-20 montage in the MRI coordinate frame, which can be used to compute the MRI<->head transform trans:

fname_1020 = subjects_dir / subject / "montages" / "10-20-montage.fif"
mon = mne.channels.read_dig_fif(fname_1020, verbose="error")  # should be named -dig.fif
mon.rename_channels({f"EEG{ii:03d}": ch_name for ii, ch_name in enumerate(ch_names, 1)})
trans = mne.channels.compute_native_head_t(mon)
raw.set_montage(mon)
print(trans)

There are also BEM and source spaces:

bem_dir = subjects_dir / subject / "bem"
fname_src = bem_dir / f"{subject}-oct-6-src.fif"
src = mne.read_source_spaces(fname_src)
print(src)
fname_bem = bem_dir / f"{subject}-5120-5120-5120-bem-sol.fif"
bem = mne.read_bem_solution(fname_bem)

You can ensure everything is as expected by plotting the result:

fig = mne.viz.plot_alignment(
    raw.info,
    subject=subject,
    subjects_dir=subjects_dir,
    trans=trans,
    src=src,
    bem=bem,
    coord_frame="mri",
    mri_fiducials=True,
    show_axes=True,
    surfaces=("white", "outer_skin", "inner_skull", "outer_skull"),
)
mne.viz.set_3d_view(fig, 25, 70, focalpoint=[0, -0.005, 0.01])

From here, standard forward and inverse operators can be computed

If you have digitized head positions or MEG data, consider using mne coreg to warp a suitable infant template MRI to your digitization information.