Compute sLORETA inverse solution on raw dataΒΆ

Compute sLORETA inverse solution on raw dataset restricted to a brain label and stores the solution in stc files for visualisation.

# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt

import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator

print(__doc__)

data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name

snr = 1.0  # use smaller SNR for raw data
lambda2 = 1.0 / snr ** 2
method = "sLORETA"  # use sLORETA method (could also be MNE or dSPM)

# Load data
raw = mne.io.read_raw_fif(fname_raw)
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)

raw.set_eeg_reference()  # set average reference.
start, stop = raw.time_as_index([0, 15])  # read the first 15s of data

# Compute inverse solution
stc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label,
                        start, stop, pick_ori=None)

# Save result in stc files
stc.save('mne_%s_raw_inverse_%s' % (method, label_name))

Out:

Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Current compensation grade : 0
add_eeg_ref defaults to True in 0.13, will default to False in 0.14, and will be removed in 0.15. We recommend to use add_eeg_ref=False and set_eeg_reference() instead.
Adding average EEG reference projection.
1 projection items deactivated
Reading inverse operator decomposition from /home/ubuntu/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
An average reference projection was already added. The data has been left untouched.
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 full noise covariance matrix (3 small eigenvalues omitted)
    Computing noise-normalization factors (sLORETA)...
[done]
Picked 305 channels from the data
Computing inverse...
(eigenleads need to be weighted)...
combining the current components...
[done]
Writing STC to disk...
[done]

View activation time-series

plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel('time (ms)')
plt.ylabel('%s value' % method)
plt.show()
../../_images/sphx_glr_plot_compute_mne_inverse_raw_in_label_001.png

Total running time of the script: ( 0 minutes 0.950 seconds)

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