GEDAI forward

This tutorial demonstrates how to compute a custom covariance matrix using a mne.Forward solution.

import mne
from mne.datasets import fetch_fsaverage
from mne.io import read_raw

from gedai.covariance import compute_covariance_from_forward
from gedai.data import get_contaminated_eeg_set_path

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"

0 files missing from root.txt in /home/runner/mne_data/MNE-fsaverage-data
0 files missing from bem.txt in /home/runner/mne_data/MNE-fsaverage-data/fsaverage

Load EEG data

raw = read_raw(str(get_contaminated_eeg_set_path()), preload=True)

Set the EEG electrode locations

raw.set_montage("standard_1005")

/home/runner/work/gedai/gedai/tutorials/advanced/10_gedai_forward.py:35: FutureWarning: Montage name 'standard_1005' is deprecated and will be removed in MNE 1.14. Use 'colin27_1005' instead.
  raw.set_montage("standard_1005")

	General
	Filename(s)	SNR=0.35481 contamination=25 clean_EEG_dataset_2.set + EOG_EMG_NOISE_dataset_1.set
	MNE object type	RawEEGLAB
	Measurement date	Unknown
	Participant	Unknown
	Experimenter	Unknown
	Acquisition
	Duration	00:01:00 (HH:MM:SS)
	Sampling frequency	200.00 Hz
	Time points	12,000
	Channels
	EEG	27
	Head & sensor digitization	30 points
	Filters
	Highpass	0.00 Hz
	Lowpass	100.00 Hz

Check that the locations of EEG electrodes is correct with respect to MRI

mne.viz.plot_alignment(
    raw.info,
    src=src,
    eeg=["projected"],
    trans=trans,
    show_axes=True,
    mri_fiducials=True,
    dig="fiducials",
)

Reading /home/runner/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-ico-5-src.fif...
Using pyvistaqt 3d backend.
For automatic theme detection, "darkdetect" has to be installed! You can install it with `pip install darkdetect`
For automatic theme detection, "darkdetect" has to be installed! You can install it with `pip install darkdetect`
Using outer_skin.surf for head surface.
Channel types:: eeg: 27
Projecting sensors to the head surface

<mne.viz.backends._pyvista.PyVistaFigure object at 0x7f772b113a90>

generate the forward solution

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

Source space          : /home/runner/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-ico-5-src.fif
MRI -> head transform : /opt/hostedtoolcache/Python/3.11.16/x64/lib/python3.11/site-packages/mne/data/fsaverage/fsaverage-trans.fif
Measurement data      : instance of Info
Conductor model   : /home/runner/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-5120-5120-5120-bem-sol.fif
Accurate field computations
Do computations in head coordinates
Free source orientations

Reading /home/runner/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-ico-5-src.fif...
Read 2 source spaces a total of 20484 active source locations

Coordinate transformation: MRI (surface RAS) -> head
    0.999994 0.003552 0.000202      -1.76 mm
    -0.003558 0.998389 0.056626      31.09 mm
    -0.000001 -0.056626 0.998395      39.60 mm
    0.000000 0.000000 0.000000       1.00

Read  27 EEG channels from info
Head coordinate coil definitions created.
Source spaces are now in head coordinates.

Setting up the BEM model using /home/runner/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-5120-5120-5120-bem-sol.fif...

Loading surfaces...

Loading the solution matrix...

Three-layer model surfaces loaded.
Loaded linear collocation BEM solution from /home/runner/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-5120-5120-5120-bem-sol.fif
Employing the head->MRI coordinate transform with the BEM model.
BEM model fsaverage-5120-5120-5120-bem-sol.fif is now set up


Source spaces are in head coordinates.
Checking that the sources are inside the surface and at least    5.0 mm away (will take a few...)
Checking surface interior status for 10242 points...
    Found  2433/10242 points inside  an interior sphere of radius   47.7 mm
    Found     0/10242 points outside an exterior sphere of radius   98.3 mm
    Found     0/ 7809 points outside using surface Qhull
    Found     0/ 7809 points outside using solid angles
    Total 10242/10242 points inside the surface
Interior check completed in 5350.1 ms
Checking surface interior status for 10242 points...
    Found  2241/10242 points inside  an interior sphere of radius   47.7 mm
    Found     0/10242 points outside an exterior sphere of radius   98.3 mm
    Found     0/ 8001 points outside using surface Qhull
    Found     0/ 8001 points outside using solid angles
    Total 10242/10242 points inside the surface
Interior check completed in 5515.2 ms
Setting up for EEG...
Computing EEG at 20484 source locations (free orientations)...

Finished.

compute the covariance matrix from the forward solution

reference_cov = compute_covariance_from_forward(fwd)

Estimated memory usage: 946 MB