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"""
.. _tut_inverse_mne_dspm:
Source localization with MNE/dSPM/sLORETA
=========================================
The aim of this tutorials is to teach you how to compute and apply a linear
inverse method such as MNE/dSPM/sLORETA on evoked/raw/epochs data.
"""
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import (make_inverse_operator, apply_inverse,
write_inverse_operator)
###############################################################################
# Process MEG data
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, add_eeg_ref=False)
raw.set_eeg_reference() # set EEG average reference
events = mne.find_events(raw, stim_channel='STI 014')
event_id = dict(aud_r=1) # event trigger and conditions
tmin = -0.2 # start of each epoch (200ms before the trigger)
tmax = 0.5 # end of each epoch (500ms after the trigger)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
exclude='bads')
baseline = (None, 0) # means from the first instant to t = 0
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=baseline, reject=reject, add_eeg_ref=False)
###############################################################################
# Compute regularized noise covariance
# ------------------------------------
#
# For more details see :ref:`tut_compute_covariance`.
noise_cov = mne.compute_covariance(
epochs, tmax=0., method=['shrunk', 'empirical'])
fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)
###############################################################################
# Compute the evoked response
# ---------------------------
evoked = epochs.average()
evoked.plot()
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag')
# Show whitening
evoked.plot_white(noise_cov)
###############################################################################
# Inverse modeling: MNE/dSPM on evoked and raw data
# -------------------------------------------------
# Read the forward solution and compute the inverse operator
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Restrict forward solution as necessary for MEG
fwd = mne.pick_types_forward(fwd, meg=True, eeg=False)
# make an MEG inverse operator
info = evoked.info
inverse_operator = make_inverse_operator(info, fwd, noise_cov,
loose=0.2, depth=0.8)
write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',
inverse_operator)
###############################################################################
# Compute inverse solution
# ------------------------
method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc = apply_inverse(evoked, inverse_operator, lambda2,
method=method, pick_ori=None)
del fwd, inverse_operator, epochs # to save memory
###############################################################################
# Visualization
# -------------
# View activation time-series
plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel('time (ms)')
plt.ylabel('%s value' % method)
plt.show()
###############################################################################
# Here we use peak getter to move visualization to the time point of the peak
# and draw a marker at the maximum peak vertex.
vertno_max, time_max = stc.get_peak(hemi='rh')
subjects_dir = data_path + '/subjects'
brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir,
clim=dict(kind='value', lims=[8, 12, 15]),
initial_time=time_max, time_unit='s')
brain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',
scale_factor=0.6)
brain.show_view('lateral')
###############################################################################
# Morph data to average brain
# ---------------------------
fs_vertices = [np.arange(10242)] * 2
morph_mat = mne.compute_morph_matrix('sample', 'fsaverage', stc.vertices,
fs_vertices, smooth=None,
subjects_dir=subjects_dir)
stc_fsaverage = stc.morph_precomputed('fsaverage', fs_vertices, morph_mat)
brain_fsaverage = stc_fsaverage.plot(surface='inflated', hemi='rh',
subjects_dir=subjects_dir,
clim=dict(kind='value', lims=[8, 12, 15]),
initial_time=time_max, time_unit='s')
brain_fsaverage.show_view('lateral')
###############################################################################
# Exercise
# --------
# - By changing the method parameter to 'sloreta' recompute the source
# estimates using the sLORETA method.
|
