mne-python/examples/datasets/spm_faces_dataset.py at main · mne-tools/mne-python · GitHub

Repository navigation

History

100

101

102

"""

.. _ex-spm-faces:

==========================================

From raw data to dSPM on SPM Faces dataset

==========================================

Runs a full pipeline using MNE-Python. This example does quite a bit of processing, so

even on a fast machine it can take several minutes to complete.

"""

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>

# Denis Engemann <denis.engemann@gmail.com>

# License: BSD-3-Clause

# Copyright the MNE-Python contributors.

import mne

from mne import combine_evoked, io

from mne.datasets import spm_face

from mne.minimum_norm import apply_inverse, make_inverse_operator

from mne.preprocessing import ICA, create_eog_epochs

print(__doc__)

data_path = spm_face.data_path()

subjects_dir = data_path / "subjects"

spm_path = data_path / "MEG" / "spm"

# %%

# Load data, filter it, and fit ICA.

raw_fname = spm_path / "SPM_CTF_MEG_example_faces1_3D.ds"

raw = io.read_raw_ctf(raw_fname, preload=True) # Take first run

# Here to save memory and time we'll downsample heavily -- this is not

# advised for real data as it can effectively jitter events!

raw.resample(100)

raw.filter(1.0, None) # high-pass

reject = dict(mag=5e-12)

ica = ICA(n_components=0.95, max_iter="auto", rng=97)

ica.fit(raw, reject=reject)

# compute correlation scores, get bad indices sorted by score

eog_epochs = create_eog_epochs(raw, ch_name="MRT31-2908", reject=reject)

eog_inds, eog_scores = ica.find_bads_eog(eog_epochs, ch_name="MRT31-2908")

ica.plot_scores(eog_scores, eog_inds) # see scores the selection is based on

ica.plot_components(eog_inds) # view topographic sensitivity of components

ica.exclude += eog_inds[:1] # we saw the 2nd ECG component looked too dipolar

ica.plot_overlay(eog_epochs.average()) # inspect artifact removal

# %%

# Epoch data and apply ICA.

events = mne.find_events(raw, stim_channel="UPPT001")

event_ids = {"faces": 1, "scrambled": 2}

tmin, tmax = -0.2, 0.6

epochs = mne.Epochs(

raw,

events,

event_ids,

tmin,

tmax,

picks="meg",

baseline=None,

preload=True,

reject=reject,

)

del raw

ica.apply(epochs) # clean data, default in place

evoked = [epochs[k].average() for k in event_ids]

contrast = combine_evoked(evoked, weights=[-1, 1]) # Faces - scrambled

evoked.append(contrast)

for e in evoked:

e.plot(ylim=dict(mag=[-400, 400]))

# %%

# Estimate noise covariance and look at the whitened evoked data

noise_cov = mne.compute_covariance(epochs, tmax=0, method="shrunk", rank=None)

evoked[0].plot_white(noise_cov)

# %%

# Compute forward model

trans_fname = spm_path / "SPM_CTF_MEG_example_faces1_3D_raw-trans.fif"

src = subjects_dir / "spm" / "bem" / "spm-oct-6-src.fif"

bem = subjects_dir / "spm" / "bem" / "spm-5120-5120-5120-bem-sol.fif"

forward = mne.make_forward_solution(contrast.info, trans_fname, src, bem)

# %%

# Compute inverse solution and plot

# sphinx_gallery_thumbnail_number = 8

snr = 3.0

lambda2 = 1.0 / snr**2

inverse_operator = make_inverse_operator(contrast.info, forward, noise_cov)

stc = apply_inverse(contrast, inverse_operator, lambda2, method="dSPM", pick_ori=None)

brain = stc.plot(

hemi="both",

subjects_dir=subjects_dir,

initial_time=0.170,

views=["ven"],

clim={"kind": "value", "lims": [3.0, 6.0, 9.0]},

)

mne-python/examples/datasets/spm_faces_dataset.py at main · mne-tools/mne-python · GitHub

Repository navigation

Expand file tree

Breadcrumbs

spm_faces_dataset.py

Latest commit

History

Breadcrumbs

spm_faces_dataset.py

File metadata and controls