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"""
.. _ex-psd-inverse:
=====================================================================
Compute Power Spectral Density of inverse solution from single epochs
=====================================================================
Compute PSD of dSPM inverse solution on single trial epochs restricted
to a brain label. The PSD is computed using a multi-taper method with
Discrete Prolate Spheroidal Sequence (DPSS) windows.
"""
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# 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 compute_source_psd_epochs, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
fname_inv = meg_path / "sample_audvis-meg-oct-6-meg-inv.fif"
fname_raw = meg_path / "sample_audvis_raw.fif"
fname_event = meg_path / "sample_audvis_raw-eve.fif"
label_name = "Aud-lh"
fname_label = meg_path / "labels" / f"{label_name}.label"
subjects_dir = data_path / "subjects"
event_id, tmin, tmax = 1, -0.2, 0.5
snr = 1.0 # use smaller SNR for raw data
lambda2 = 1.0 / snr**2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Load data
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
raw = mne.io.read_raw_fif(fname_raw)
events = mne.read_events(fname_event)
# Set up pick list
include = []
raw.info["bads"] += ["EEG 053"] # bads + 1 more
# pick MEG channels
picks = mne.pick_types(
raw.info, meg=True, eeg=False, stim=False, eog=True, include=include, exclude="bads"
)
# Read epochs
epochs = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=dict(mag=4e-12, grad=4000e-13, eog=150e-6),
)
# define frequencies of interest
fmin, fmax = 0.0, 70.0
bandwidth = 4.0 # bandwidth of the windows in Hz
# %%
# Compute source space PSD in label
# ---------------------------------
#
# ..note:: By using "return_generator=True" stcs will be a generator object
# instead of a list. This allows us so to iterate without having to
# keep everything in memory.
n_epochs_use = 10
stcs = compute_source_psd_epochs(
epochs[:n_epochs_use],
inverse_operator,
lambda2=lambda2,
method=method,
fmin=fmin,
fmax=fmax,
bandwidth=bandwidth,
label=label,
return_generator=True,
verbose=True,
)
# compute average PSD over the first 10 epochs
psd_avg = 0.0
for i, stc in enumerate(stcs):
psd_avg += stc.data
psd_avg /= n_epochs_use
freqs = stc.times # the frequencies are stored here
stc.data = psd_avg # overwrite the last epoch's data with the average
# %%
# Visualize the 10 Hz PSD:
brain = stc.plot(
initial_time=10.0,
hemi="lh",
views="lat", # 10 HZ
clim=dict(kind="value", lims=(20, 40, 60)),
smoothing_steps=3,
subjects_dir=subjects_dir,
)
brain.add_label(label, borders=True, color="k")
# %%
# Visualize the entire spectrum:
fig, ax = plt.subplots()
ax.plot(freqs, psd_avg.mean(axis=0))
ax.set_xlabel("Freq (Hz)")
ax.set_xlim(stc.times[[0, -1]])
ax.set_ylabel("Power Spectral Density")
|
