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Compute source power spectral density (PSD) in a label#
Returns an STC file containing the PSD (in dB) of each of the sources within a label.
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
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'
fname_label = meg_path / 'labels' / 'Aud-lh.label'
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, verbose=False)
events = mne.find_events(raw, stim_channel='STI 014')
inverse_operator = read_inverse_operator(fname_inv)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=False, exclude='bads')
tmin, tmax = 0, 120 # use the first 120s of data
fmin, fmax = 4, 100 # look at frequencies between 4 and 100Hz
n_fft = 2048 # the FFT size (n_fft). Ideally a power of 2
label = mne.read_label(fname_label)
stc = compute_source_psd(raw, inverse_operator, lambda2=1. / 9., method="dSPM",
tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
pick_ori="normal", n_fft=n_fft, label=label,
dB=True)
stc.save('psd_dSPM', overwrite=True)
320 events found
Event IDs: [ 1 2 3 4 5 32]
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
Reading inverse operator info...
[done]
Reading inverse operator decomposition...
[done]
305 x 305 full covariance (kind = 1) found.
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Noise covariance matrix read.
22494 x 22494 diagonal covariance (kind = 2) found.
Source covariance matrix read.
22494 x 22494 diagonal covariance (kind = 6) found.
Orientation priors read.
22494 x 22494 diagonal covariance (kind = 5) found.
Depth priors read.
Did not find the desired covariance matrix (kind = 3)
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
2 source spaces read
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Source spaces transformed to the inverse solution coordinate frame
Not setting metadata
70 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Considering frequencies 4 ... 100 Hz
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 1
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Reducing data rank 33 -> 33
Using hann windowing on at most 70 epochs
0%| | : 0/70 [00:00<?, ?it/s]
1%|1 | : 1/70 [00:00<00:08, 7.73it/s]
3%|2 | : 2/70 [00:00<00:05, 12.04it/s]
4%|4 | : 3/70 [00:00<00:04, 16.04it/s]
6%|5 | : 4/70 [00:00<00:03, 19.07it/s]
7%|7 | : 5/70 [00:00<00:02, 21.84it/s]
9%|8 | : 6/70 [00:00<00:02, 24.38it/s]
10%|# | : 7/70 [00:00<00:02, 26.54it/s]
11%|#1 | : 8/70 [00:00<00:02, 28.44it/s]
13%|#2 | : 9/70 [00:00<00:02, 30.13it/s]
14%|#4 | : 10/70 [00:00<00:01, 31.72it/s]
16%|#5 | : 11/70 [00:00<00:01, 33.05it/s]
17%|#7 | : 12/70 [00:00<00:01, 34.21it/s]
19%|#8 | : 13/70 [00:00<00:01, 35.37it/s]
20%|## | : 14/70 [00:00<00:01, 36.31it/s]
21%|##1 | : 15/70 [00:00<00:01, 37.30it/s]
23%|##2 | : 16/70 [00:00<00:01, 38.10it/s]
24%|##4 | : 17/70 [00:00<00:01, 38.62it/s]
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30%|### | : 21/70 [00:00<00:01, 39.16it/s]
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70%|####### | : 49/70 [00:01<00:00, 46.02it/s]
71%|#######1 | : 50/70 [00:01<00:00, 45.98it/s]
73%|#######2 | : 51/70 [00:01<00:00, 46.16it/s]
74%|#######4 | : 52/70 [00:01<00:00, 46.38it/s]
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77%|#######7 | : 54/70 [00:01<00:00, 46.76it/s]
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80%|######## | : 56/70 [00:01<00:00, 47.14it/s]
81%|########1 | : 57/70 [00:01<00:00, 47.31it/s]
83%|########2 | : 58/70 [00:01<00:00, 47.18it/s]
84%|########4 | : 59/70 [00:01<00:00, 47.07it/s]
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87%|########7 | : 61/70 [00:01<00:00, 46.94it/s]
89%|########8 | : 62/70 [00:01<00:00, 47.00it/s]
90%|######### | : 63/70 [00:01<00:00, 47.18it/s]
91%|#########1| : 64/70 [00:01<00:00, 47.30it/s]
93%|#########2| : 65/70 [00:01<00:00, 47.43it/s]
94%|#########4| : 66/70 [00:01<00:00, 47.46it/s]
96%|#########5| : 67/70 [00:01<00:00, 47.60it/s]
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100%|##########| : 70/70 [00:01<00:00, 47.85it/s]
100%|##########| : 70/70 [00:01<00:00, 43.58it/s]
Writing STC to disk...
[done]
View PSD of sources in label
plt.plot(stc.times, stc.data.T)
plt.xlabel('Frequency (Hz)')
plt.ylabel('PSD (dB)')
plt.title('Source Power Spectrum (PSD)')
plt.show()
Total running time of the script: ( 0 minutes 6.661 seconds)
Estimated memory usage: 9 MB