Compute sLORETA inverse solution on raw data

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@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

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" / f"{label_name}.label"

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("average", projection=True)  # 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(f"mne_{method}_raw_inverse_{label_name}", overwrite=True)

Opening raw data file /home/circleci/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.
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
EEG channel type selected for re-referencing
Adding average EEG reference projection.
1 projection items deactivated
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 (sLORETA)...
[done]
Applying inverse to raw...
    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(f"{method} value")
plt.show()