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%matplotlib inline

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.

.. important:: 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.

Note

`plot_montage` show all the standard montages in MNE-Python.

:depth: 2



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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.

import os.path as op

import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage

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

# The files live in:
subject = 'fsaverage'
trans = op.join(fs_dir, 'bem', 'fsaverage-trans.fif')
src = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')

Load the data

We use here EEG data from the BCI dataset.



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raw_fname, = eegbci.load_data(subject=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
ch_names = [c.replace('.', '') for c in raw.ch_names]
raw.rename_channels({old: new for old, new in zip(raw.ch_names, ch_names)})

# Read and set the EEG electrode locations
montage = mne.channels.read_montage('standard_1005', ch_names=raw.ch_names,
                                    transform=True)

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, dig=True)

Setup source space and compute forward



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fwd = mne.make_forward_solution(raw.info, trans=trans, src=src,
                                bem=bem, eeg=True, mindist=5.0, n_jobs=1)
print(fwd)

# for illustration purposes use fwd to compute the sensitivity map
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')
eeg_map.plot(time_label='EEG sensitivity', subjects_dir=subjects_dir,
             clim=dict(lims=[5, 50, 100]))