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
|
"""
=====================================
Compute DICS beamfomer on evoked data
=====================================
Compute a Dynamic Imaging of Coherent Sources (DICS) beamformer from single
trial activity in a time-frequency window to estimate source time courses based
on evoked data.
The original reference for DICS is:
Gross et al. Dynamic imaging of coherent sources: Studying neural interactions
in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699
"""
# Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
print(__doc__)
import mne
import matplotlib.pyplot as plt
import numpy as np
from mne.io import Raw
from mne.datasets import sample
from mne.time_frequency import compute_epochs_csd
from mne.beamformer import dics
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
subjects_dir = data_path + '/subjects'
###############################################################################
# Read raw data
raw = Raw(raw_fname)
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels
# Set picks
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
# Read epochs
event_id, tmin, tmax = 1, -0.2, 0.5
events = mne.read_events(event_fname)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=(None, 0), preload=True,
reject=dict(grad=4000e-13, mag=4e-12))
evoked = epochs.average()
# Read forward operator
forward = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Computing the data and noise cross-spectral density matrices
# The time-frequency window was chosen on the basis of spectrograms from
# example time_frequency/plot_time_frequency.py
data_csd = compute_epochs_csd(epochs, mode='multitaper', tmin=0.04, tmax=0.15,
fmin=6, fmax=10)
noise_csd = compute_epochs_csd(epochs, mode='multitaper', tmin=-0.11, tmax=0.0,
fmin=6, fmax=10)
evoked = epochs.average()
# Compute DICS spatial filter and estimate source time courses on evoked data
stc = dics(evoked, forward, noise_csd, data_csd)
plt.figure()
ts_show = -30 # show the 40 largest responses
plt.plot(1e3 * stc.times,
stc.data[np.argsort(stc.data.max(axis=1))[ts_show:]].T)
plt.xlabel('Time (ms)')
plt.ylabel('DICS value')
plt.title('DICS time course of the 30 largest sources.')
plt.show()
# Plot brain in 3D with PySurfer if available. Note that the subject name
# is already known by the SourceEstimate stc object.
brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir)
brain.set_data_time_index(180)
brain.scale_data_colormap(fmin=4, fmid=6, fmax=8, transparent=True)
brain.show_view('lateral')
# Uncomment to save image
#brain.save_image('DICS_map.png')
|
