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
|
"""
.. _ex-source-space-tfr:
===================================================
Compute induced power in the source space with dSPM
===================================================
Returns STC files ie source estimates of induced power
for different bands in the source space. The inverse method
is linear based on dSPM inverse operator.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
# %%
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_band_induced_power
print(__doc__)
# %%
# Set parameters
data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_raw.fif"
fname_inv = meg_path / "sample_audvis-meg-oct-6-meg-inv.fif"
tmin, tmax, event_id = -0.2, 0.5, 1
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.find_events(raw, stim_channel="STI 014")
inverse_operator = read_inverse_operator(fname_inv)
include = []
raw.info["bads"] += ["MEG 2443", "EEG 053"] # bads + 2 more
# picks MEG gradiometers
picks = mne.pick_types(
raw.info, meg=True, eeg=False, eog=True, stim=False, include=include, exclude="bads"
)
# Load condition 1
event_id = 1
events = events[:10] # take 10 events to keep the computation time low
# Use linear detrend to reduce any edge artifacts
epochs = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6),
preload=True,
detrend=1,
)
# Compute a source estimate per frequency band
bands = dict(alpha=[9, 11], beta=[18, 22])
stcs = source_band_induced_power(
epochs, inverse_operator, bands, n_cycles=2, use_fft=False, n_jobs=None
)
for b, stc in stcs.items():
stc.save(f"induced_power_{b}", overwrite=True)
# %%
# plot mean power
plt.plot(stcs["alpha"].times, stcs["alpha"].data.mean(axis=0), label="Alpha")
plt.plot(stcs["beta"].times, stcs["beta"].data.mean(axis=0), label="Beta")
plt.xlabel("Time (ms)")
plt.ylabel("Power")
plt.legend()
plt.title("Mean source induced power")
plt.show()
|
