Compute all-to-all connectivity in sensor space

Computes the Phase Lag Index (PLI) between all gradiometers and shows the connectivity in 3D using the helmet geometry. The left visual stimulation data are used which produces strong connectvitiy in the right occipital sensors.

# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)

import os.path as op

import mne
from mne.datasets import sample

from mne_connectivity import spectral_connectivity_epochs
from mne_connectivity.viz import plot_sensors_connectivity

print(__doc__)

Set parameters

data_path = sample.data_path()
raw_fname = op.join(data_path, "MEG", "sample", "sample_audvis_filt-0-40_raw.fif")
event_fname = op.join(data_path, "MEG", "sample", "sample_audvis_filt-0-40_raw-eve.fif")

# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)

# Add a bad channel
raw.info["bads"] += ["MEG 2443"]

# Pick MEG gradiometers
picks = mne.pick_types(
    raw.info, meg="grad", eeg=False, stim=False, eog=True, exclude="bads"
)

# Create epochs for the visual condition
event_id, tmin, tmax = 3, -0.2, 1.5  # need a long enough epoch for 5 cycles
epochs = mne.Epochs(
    raw,
    events,
    event_id,
    tmin,
    tmax,
    picks=picks,
    baseline=(None, 0),
    reject=dict(grad=4000e-13, eog=150e-6),
)

# Compute connectivity for band containing the evoked response.
# We exclude the baseline period:
fmin, fmax = 4.0, 9.0
sfreq = raw.info["sfreq"]  # the sampling frequency
tmin = 0.0  # exclude the baseline period
epochs.load_data().pick_types(meg="grad")  # just keep MEG and no EOG now
con = spectral_connectivity_epochs(
    epochs,
    method="pli",
    mode="multitaper",
    sfreq=sfreq,
    fmin=fmin,
    fmax=fmax,
    faverage=True,
    tmin=tmin,
    mt_adaptive=False,
    n_jobs=1,
)

# Now, visualize the connectivity in 3D:
plot_sensors_connectivity(epochs.info, con.get_data(output="dense")[:, :, 0])

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Not setting metadata
73 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
0 projection items activated
Loading data for 73 events and 256 original time points ...
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
24 bad epochs dropped
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Adding metadata with 3 columns
Connectivity computation...
only using indices for lower-triangular matrix
    computing connectivity for 20503 connections
    using t=0.000s..1.498s for estimation (226 points)
    frequencies: 4.7Hz..8.6Hz (7 points)
    connectivity scores will be averaged for each band
    Using multitaper spectrum estimation with 7 DPSS windows
    the following metrics will be computed: PLI
    computing cross-spectral density for epoch 1
    computing cross-spectral density for epoch 2
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    assembling connectivity matrix
[Connectivity computation done]

<mne.viz.backends._pyvista.PyVistaFigure object at 0x7f5bb376eb90>