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
|
# -*- coding: utf-8 -*-
"""
.. _ex-sim-evoked:
==============================
Generate simulated evoked data
==============================
Use :func:`~mne.simulation.simulate_sparse_stc` to simulate evoked data.
"""
# Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# %%
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.time_frequency import fit_iir_model_raw
from mne.viz import plot_sparse_source_estimates
from mne.simulation import simulate_sparse_stc, simulate_evoked
print(__doc__)
# %%
# Load real data as templates:
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
raw = mne.io.read_raw_fif(meg_path / 'sample_audvis_raw.fif')
proj = mne.read_proj(meg_path / 'sample_audvis_ecg-proj.fif')
raw.add_proj(proj)
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = meg_path / 'sample_audvis-no-filter-ave.fif'
cov_fname = meg_path / 'sample_audvis-cov.fif'
fwd = mne.read_forward_solution(fwd_fname)
fwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = mne.read_cov(cov_fname)
info = mne.io.read_info(ave_fname)
label_names = ['Aud-lh', 'Aud-rh']
labels = [mne.read_label(meg_path / 'labels' / f'{ln}.label')
for ln in label_names]
# %%
# Generate source time courses from 2 dipoles and the correspond evoked data
times = np.arange(300, dtype=np.float64) / raw.info['sfreq'] - 0.1
rng = np.random.RandomState(42)
def data_fun(times):
"""Function to generate random source time courses"""
return (50e-9 * np.sin(30. * times) *
np.exp(- (times - 0.15 + 0.05 * rng.randn(1)) ** 2 / 0.01))
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
random_state=42, labels=labels, data_fun=data_fun)
# %%
# Generate noisy evoked data
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
iir_filter = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1]
nave = 100 # simulate average of 100 epochs
evoked = simulate_evoked(fwd, stc, info, cov, nave=nave, use_cps=True,
iir_filter=iir_filter)
# %%
# Plot
plot_sparse_source_estimates(fwd['src'], stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=True)
plt.figure()
plt.psd(evoked.data[0])
evoked.plot(time_unit='s')
|
