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"""
.. _example-sLORETA:
=============================================
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)
# %%
# View activation time-series
plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel("time (ms)")
plt.ylabel(f"{method} value")
plt.show()
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