Compute Rap-Music on evoked dataΒΆ

Compute a Recursively Applied and Projected MUltiple Signal Classification (RAP-MUSIC) on evoked dataset.

The reference for Rap-Music is: J.C. Mosher and R.M. Leahy. 1999. Source localization using recursively applied and projected (RAP) MUSIC. Trans. Sig. Proc. 47, 2 (February 1999), 332-340. DOI=10.1109/78.740118 http://dx.doi.org/10.1109/78.740118

  • ../../_images/sphx_glr_plot_rap_music_001.png
  • ../../_images/sphx_glr_plot_rap_music_002.png
  • ../../_images/sphx_glr_plot_rap_music_003.png
  • ../../_images/sphx_glr_plot_rap_music_004.png

Out:

Reading /home/ubuntu/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.
No baseline correction applied
Applying baseline correction (mode: mean)
Reading forward solution from /home/ubuntu/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)
    MEG and EEG forward solutions combined
    Source spaces transformed to the forward solution coordinate frame
    Converting to surface-based source orientations...
    Average patch normals will be employed in the rotation to the local surface coordinates....
[done]
    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
    305 out of 366 channels remain after picking
    Created an SSP operator (subspace dimension = 3)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
source 1 found: p = 1834
ori = -0.247032414246 0.776432601469 0.579764953829
source 2 found: p = 5304
ori = -0.515459181381 0.534511689551 0.669775399715
4 projection items deactivated
Created an SSP operator (subspace dimension = 3)
4 projection items activated
SSP projectors applied...
Triangle file: Converted tri file nvert = 2562 ntri = 5120

# Author: Yousra Bekhti <yousra.bekhti@gmail.com>
#
# License: BSD (3-clause)

import mne

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

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
cov_fname = data_path + '/MEG/sample/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))
evoked.crop(tmin=0.05, tmax=0.15)  # select N100

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

# Read the forward solution
forward = mne.read_forward_solution(fwd_fname, surf_ori=True,
                                    force_fixed=False)

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

dipoles, residual = rap_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]))
residual.plot(ylim=dict(grad=[-300, 300], mag=[-800, 800], eeg=[-6, 8]))

Total running time of the script: ( 0 minutes 13.843 seconds)

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