GEDAI forward

This tutorial demonstrates how to use a custom covariance matrix in GEDAI derived from the leadfield of a forward solution.

import mne
from mne.datasets import eegbci, fetch_fsaverage

from gedai import Gedai
from gedai.gedai.covariances import compute_covariance_from_forward
from gedai.viz import plot_mne_style_overlay_interactive

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_fname,) = eegbci.load_data(subjects=1, runs=[6])
raw = mne.io.read_raw_edf(raw_fname, preload=True)
eegbci.standardize(raw)
montage = mne.channels.make_standard_montage("standard_1005")
raw.set_montage(montage)
raw.crop(0, 15)
raw.pick("eeg").load_data().apply_proj()
raw.set_eeg_reference("average", projection=False)

	General
	Filename(s)	S001R06.edf
	MNE object type	RawEDF
	Measurement date	2009-08-12 at 16:15:00 UTC
	Participant	X
	Experimenter	Unknown
	Acquisition
	Duration	00:00:16 (HH:MM:SS)
	Sampling frequency	160.00 Hz
	Time points	2,401
	Channels
	EEG	64
	Head & sensor digitization	67 points
	Filters
	Highpass	0.00 Hz
	Lowpass	80.00 Hz

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",
)

<mne.viz.backends._pyvista.PyVistaFigure object at 0x7f5facc90210>

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
)

compute the covariance matrix from the forward solution

reference_cov = compute_covariance_from_forward(fwd)

gedai_broadband = Gedai()
gedai_broadband.fit_raw(raw, reference_cov=reference_cov, noise_multiplier=6.0)
raw_broadband_corrected = gedai_broadband.transform_raw(raw, verbose=False)

gedai_spectral = Gedai(wavelet_type="haar", wavelet_level=5, wavelet_low_cutoff=2)
gedai_spectral.fit_raw(
    raw_broadband_corrected, reference_cov=reference_cov, noise_multiplier=3.0
)
raw_spectral_corrected = gedai_spectral.transform_raw(
    raw_broadband_corrected, verbose=False
)

plot_mne_style_overlay_interactive(raw, raw_spectral_corrected)

(<Figure size 1200x3600 with 1 Axes>, <Axes: xlabel='Time (s)', ylabel='Channels'>)

Estimated memory usage: 1116 MB