Compute Trap-Music on evoked data

Compute a Truncated Recursively Applied and Projected MUltiple Signal Classification (TRAP-MUSIC) [1] on evoked data.

# Author: Théodore Papadopoulo <Theodore.Papadopoulo@inria.fr>
#
# License: BSD-3-Clause

import mne

from mne.datasets import sample
from mne.beamformer import trap_music
from mne.viz import plot_dipole_locations, plot_dipole_amplitudes

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path / "subjects"
meg_path = data_path / "MEG" / "sample"
fwd_fname = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
evoked_fname = meg_path / "sample_audvis-ave.fif"
cov_fname = meg_path / "sample_audvis-cov.fif"

# Read the evoked response and crop it
condition = "Right Auditory"
evoked = mne.read_evokeds(evoked_fname, condition=condition, baseline=(None, 0))
# select N100
evoked.crop(tmin=0.05, tmax=0.15)

evoked.pick_types(meg=True, eeg=False)

# Read the forward solution
forward = mne.read_forward_solution(fwd_fname)

# Read noise covariance matrix
noise_cov = mne.read_cov(cov_fname)

dipoles, residual = trap_music(
    evoked, forward, noise_cov, n_dipoles=2, return_residual=True, verbose=True
)
trans = forward["mri_head_t"]
plot_dipole_locations(dipoles, trans, "sample", subjects_dir=subjects_dir)
plot_dipole_amplitudes(dipoles)

# Plot the evoked data and the residual.
evoked.plot(ylim=dict(grad=[-300, 300], mag=[-800, 800], eeg=[-6, 8]), time_unit="s")
residual.plot(ylim=dict(grad=[-300, 300], mag=[-800, 800], eeg=[-6, 8]), time_unit="s")

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Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    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
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right Auditory)
        0 CTF compensation matrices available
        nave = 61 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    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
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    Forward solutions combined: MEG, EEG
    Source spaces transformed to the forward solution coordinate frame
    366 x 366 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
info["bads"] and noise_cov["bads"] do not match, excluding bad channels from both
Computing inverse operator with 305 channels.
    305 out of 366 channels remain after picking
Selected 305 channels
Whitening the forward solution.
    Created an SSP operator (subspace dimension = 3)
Computing rank from covariance with rank=None
    Using tolerance 3.3e-13 (2.2e-16 eps * 305 dim * 4.8  max singular value)
    Estimated rank (mag + grad): 302
    MEG: rank 302 computed from 305 data channels with 3 projectors
    Setting small MEG eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
    305 out of 366 channels remain after picking
source 1 found: p = 1834
ori = -0.24703241413887647 0.776432601481173 0.5797649538579378
source 2 found: p = 5304
ori = -0.5154591819142723 0.5345116894131523 0.6697753994145853
    Explained  46.6% variance
[done]
3 projection items deactivated
Created an SSP operator (subspace dimension = 3)
3 projection items activated
SSP projectors applied...

References

[1]

Niko Mäkelä, Matti Stenroos, Jukka Sarvas, and Risto J. Ilmoniemi. Truncated rap-music (trap-music) for meg and eeg source localization. Neuroimage, 167():73–83, 2018. doi:10.1016/j.neuroimage.2017.11.013.

Estimated memory usage: 166 MB