1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
|
"""
Source localization with MNE/dSPM/sLORETA/eLORETA
=================================================
The aim of this tutorial is to teach you how to compute and apply a linear
inverse method such as MNE/dSPM/sLORETA/eLORETA on evoked/raw/epochs data.
"""
# sphinx_gallery_thumbnail_number = 10
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
###############################################################################
# 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) # already has an average reference
events = mne.find_events(raw, stim_channel='STI 014')
event_id = dict(aud_l=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)
###############################################################################
# Compute regularized noise covariance
# ------------------------------------
#
# For more details see :ref:`tut_compute_covariance`.
noise_cov = mne.compute_covariance(
epochs, tmax=0., method=['shrunk', 'empirical'], rank=None, verbose=True)
fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)
###############################################################################
# Compute the evoked response
# ---------------------------
# Let's just use MEG channels for simplicity.
evoked = epochs.average().pick_types(meg=True)
evoked.plot(time_unit='s')
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag',
time_unit='s')
# Show whitening
evoked.plot_white(noise_cov, time_unit='s')
del epochs # to save memory
###############################################################################
# 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)
# make an MEG inverse operator
info = evoked.info
inverse_operator = make_inverse_operator(info, fwd, noise_cov,
loose=0.2, depth=0.8)
del fwd
# You can write it to disk with::
#
# >>> from mne.minimum_norm import write_inverse_operator
# >>> write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',
# inverse_operator)
###############################################################################
# Compute inverse solution
# ------------------------
method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc, residual = apply_inverse(evoked, inverse_operator, lambda2,
method=method, pick_ori=None,
return_residual=True, verbose=True)
###############################################################################
# Visualization
# -------------
# View activation time-series
plt.figure()
plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel('time (ms)')
plt.ylabel('%s value' % method)
plt.show()
###############################################################################
# Examine the original data and the residual after fitting:
fig, axes = plt.subplots(2, 1)
evoked.plot(axes=axes)
for ax in axes:
ax.texts = []
for line in ax.lines:
line.set_color('#98df81')
residual.plot(axes=axes)
###############################################################################
# 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'
surfer_kwargs = dict(
hemi='rh', subjects_dir=subjects_dir,
clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',
initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)
brain = stc.plot(**surfer_kwargs)
brain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',
scale_factor=0.6, alpha=0.5)
brain.add_text(0.1, 0.9, 'dSPM (plus location of maximal activation)', 'title',
font_size=14)
###############################################################################
# Morph data to average brain
# ---------------------------
# setup source morph
morph = mne.compute_source_morph(
src=inverse_operator['src'], subject_from=stc.subject,
subject_to='fsaverage', spacing=5, # to ico-5
subjects_dir=subjects_dir)
# morph data
stc_fsaverage = morph.apply(stc)
brain = stc_fsaverage.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Morphed to fsaverage', 'title', font_size=20)
del stc_fsaverage
###############################################################################
# Dipole orientations
# -------------------
# The ``pick_ori`` parameter of the
# :func:`mne.minimum_norm.apply_inverse` function controls
# the orientation of the dipoles. One useful setting is ``pick_ori='vector'``,
# which will return an estimate that does not only contain the source power at
# each dipole, but also the orientation of the dipoles.
stc_vec = apply_inverse(evoked, inverse_operator, lambda2,
method=method, pick_ori='vector')
brain = stc_vec.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Vector solution', 'title', font_size=20)
del stc_vec
###############################################################################
# Note that there is a relationship between the orientation of the dipoles and
# the surface of the cortex. For this reason, we do not use an inflated
# cortical surface for visualization, but the original surface used to define
# the source space.
#
# For more information about dipole orientations, see
# :ref:`sphx_glr_auto_tutorials_plot_dipole_orientations.py`.
###############################################################################
# Now let's look at each solver:
for mi, (method, lims) in enumerate((('dSPM', [8, 12, 15]),
('sLORETA', [3, 5, 7]),
('eLORETA', [0.75, 1.25, 1.75]),)):
surfer_kwargs['clim']['lims'] = lims
stc = apply_inverse(evoked, inverse_operator, lambda2,
method=method, pick_ori=None)
brain = stc.plot(figure=mi, **surfer_kwargs)
brain.add_text(0.1, 0.9, method, 'title', font_size=20)
del stc
|
