4D Neuroimaging/BTi phantom dataset tutorial#

Here we read 4DBTi epochs data obtained with a spherical phantom using four different dipole locations. For each condition we compute evoked data and compute dipole fits.

Data are provided by Jean-Michel Badier from MEG center in Marseille, France.

# Authors: Alex Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
from mne.datasets import phantom_4dbti
import mne

Read data and compute a dipole fit at the peak of the evoked response

data_path = phantom_4dbti.data_path()
raw_fname = op.join(data_path, '%d/e,rfhp1.0Hz')

dipoles = list()
sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=0.080)

t0 = 0.07  # peak of the response

pos = np.empty((4, 3))
ori = np.empty((4, 3))

for ii in range(4):
    raw = mne.io.read_raw_bti(raw_fname % (ii + 1,),
                              rename_channels=False, preload=True)
    raw.info['bads'] = ['A173', 'A213', 'A232']
    events = mne.find_events(raw, 'TRIGGER', mask=4350, mask_type='not_and')
    epochs = mne.Epochs(raw, events=events, event_id=8192, tmin=-0.2, tmax=0.4,
                        preload=True)
    evoked = epochs.average()
    evoked.plot(time_unit='s')
    cov = mne.compute_covariance(epochs, tmax=0.)
    dip = mne.fit_dipole(evoked.copy().crop(t0, t0), cov, sphere)[0]
    pos[ii] = dip.pos[0]
    ori[ii] = dip.ori[0]
  • Magnetometers (245 channels)
  • Magnetometers (245 channels)
  • Magnetometers (245 channels)
  • Magnetometers (245 channels)
Equiv. model fitting -> RV = 0.00365867 %
mu1 = 0.944092    lambda1 = 0.138723
mu2 = 0.665896    lambda2 = 0.684544
mu3 = -0.132233    lambda3 = -0.0128974
Set up EEG sphere model with scalp radius    80.0 mm

Reading 4D PDF file /home/circleci/mne_data/MNE-phantom-4DBTi/1/e,rfhp1.0Hz...
Creating Neuromag info structure ...
... Setting channel info structure.
... putting coil transforms in Neuromag coordinates
... Reading digitization points from /home/circleci/mne_data/MNE-phantom-4DBTi/1/hs_file
Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine.
Reading 0 ... 13599  =      0.000 ...    20.052 secs...
Trigger channel has a non-zero initial value of 4350 (consider using initial_event=True to detect this event)
50 events found
Event IDs: [8192]
Not setting metadata
50 matching events found
Setting baseline interval to [-0.20053968578577042, 0.0] sec
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 50 events and 408 original time points ...
2 bad epochs dropped
Computing rank from data with rank=None
    Using tolerance 3.7e-09 (2.2e-16 eps * 245 dim * 6.9e+04  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
Reducing data rank from 245 -> 245
Estimating covariance using EMPIRICAL
Done.
Number of samples used : 6576
[done]
BEM               : <ConductorModel | Sphere (3 layers): r0=[0.0, 0.0, 0.0] R=80 mm>
MRI transform     : identity
Sphere model      : origin at (   0.00    0.00    0.00) mm, rad =    0.1 mm
Guess grid        :   20.0 mm
Guess mindist     :    5.0 mm
Guess exclude     :   20.0 mm
Using normal MEG coil definitions.
Noise covariance  : <Covariance | size : 245 x 245, n_samples : 6575, data : [[ 9.50691838e-27  3.61763336e-27 -3.91409625e-28 ... -1.91576159e-27
  -8.16898444e-28 -1.75585208e-27]
 [ 3.61763336e-27  1.47442194e-26  1.45272203e-26 ...  2.55529894e-27
   3.46448717e-28  3.88484744e-27]
 [-3.91409625e-28  1.45272203e-26  3.05130802e-26 ...  6.14443569e-27
   3.70955369e-27  7.39503671e-27]
 ...
 [-1.91576159e-27  2.55529894e-27  6.14443569e-27 ...  1.16681391e-26
   7.65491197e-27  8.24338008e-27]
 [-8.16898444e-28  3.46448717e-28  3.70955369e-27 ...  7.65491197e-27
   1.53281664e-26  7.47448228e-27]
 [-1.75585208e-27  3.88484744e-27  7.39503671e-27 ...  8.24338008e-27
   7.47448228e-27  1.78257781e-26]]>

Coordinate transformation: MRI (surface RAS) -> head
     1.000000  0.000000  0.000000       0.00 mm
     0.000000  1.000000  0.000000       0.00 mm
     0.000000  0.000000  1.000000       0.00 mm
     0.000000  0.000000  0.000000       1.00
Coordinate transformation: MEG device -> head
     0.975564 -0.033891 -0.217085      -1.50 mm
     0.044586  0.998011  0.044560     -34.72 mm
     0.215143 -0.053150  0.975135      -6.31 mm
     0.000000  0.000000  0.000000       1.00
3 bad channels total
Read 268 MEG channels from info
Read 23 MEG compensation channels from info
0 compensation data sets in info
Setting up compensation data...
    No compensation set. Nothing more to do.
105 coil definitions read
Coordinate transformation: MEG device -> head
     0.975564 -0.033891 -0.217085      -1.50 mm
     0.044586  0.998011  0.044560     -34.72 mm
     0.215143 -0.053150  0.975135      -6.31 mm
     0.000000  0.000000  0.000000       1.00
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing rank from covariance with rank=None
    Using tolerance 3.9e-14 (2.2e-16 eps * 245 dim * 0.72  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
    Setting small MAG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 245 (0 small eigenvalues omitted)

---- Computing the forward solution for the guesses...
Making a spherical guess space with radius    72.0 mm...
Filtering (grid =     20 mm)...
Surface CM = (   0.0    0.0    0.0) mm
Surface fits inside a sphere with radius   72.0 mm
Surface extent:
    x =  -72.0 ...   72.0 mm
    y =  -72.0 ...   72.0 mm
    z =  -72.0 ...   72.0 mm
Grid extent:
    x =  -80.0 ...   80.0 mm
    y =  -80.0 ...   80.0 mm
    z =  -80.0 ...   80.0 mm
729 sources before omitting any.
178 sources after omitting infeasible sources not within 20.0 - 72.0 mm.
170 sources remaining after excluding the sources outside the surface and less than    5.0 mm inside.
Go through all guess source locations...
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[done 170 sources]
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---- Fitted :    69.3 ms
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No projector specified for this dataset. Please consider the method self.add_proj.
1 time points fitted
Reading 4D PDF file /home/circleci/mne_data/MNE-phantom-4DBTi/2/e,rfhp1.0Hz...
Creating Neuromag info structure ...
... Setting channel info structure.
... putting coil transforms in Neuromag coordinates
... Reading digitization points from /home/circleci/mne_data/MNE-phantom-4DBTi/2/hs_file
Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine.
Reading 0 ... 13599  =      0.000 ...    20.052 secs...
Trigger channel has a non-zero initial value of 4350 (consider using initial_event=True to detect this event)
50 events found
Event IDs: [8192]
Not setting metadata
50 matching events found
Setting baseline interval to [-0.20053968578577042, 0.0] sec
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 50 events and 408 original time points ...
2 bad epochs dropped
Computing rank from data with rank=None
    Using tolerance 3.5e-09 (2.2e-16 eps * 245 dim * 6.4e+04  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
Reducing data rank from 245 -> 245
Estimating covariance using EMPIRICAL
Done.
Number of samples used : 6576
[done]
BEM               : <ConductorModel | Sphere (3 layers): r0=[0.0, 0.0, 0.0] R=80 mm>
MRI transform     : identity
Sphere model      : origin at (   0.00    0.00    0.00) mm, rad =    0.1 mm
Guess grid        :   20.0 mm
Guess mindist     :    5.0 mm
Guess exclude     :   20.0 mm
Using normal MEG coil definitions.
Noise covariance  : <Covariance | size : 245 x 245, n_samples : 6575, data : [[ 9.43530566e-27  6.00499851e-27  3.47056513e-27 ...  1.49717001e-28
   1.01651220e-28 -6.39837298e-28]
 [ 6.00499851e-27  1.09320996e-26  9.53068976e-27 ...  2.44272962e-27
   2.07218608e-27  1.57916869e-27]
 [ 3.47056513e-27  9.53068976e-27  2.24522939e-26 ...  4.45459681e-27
   2.19250060e-27  4.30544307e-27]
 ...
 [ 1.49717001e-28  2.44272962e-27  4.45459681e-27 ...  1.26518629e-26
   7.97497347e-27  7.82472005e-27]
 [ 1.01651220e-28  2.07218608e-27  2.19250060e-27 ...  7.97497347e-27
   1.37787240e-26  7.96791371e-27]
 [-6.39837298e-28  1.57916869e-27  4.30544307e-27 ...  7.82472005e-27
   7.96791371e-27  1.70423989e-26]]>

Coordinate transformation: MRI (surface RAS) -> head
     1.000000  0.000000  0.000000       0.00 mm
     0.000000  1.000000  0.000000       0.00 mm
     0.000000  0.000000  1.000000       0.00 mm
     0.000000  0.000000  0.000000       1.00
Coordinate transformation: MEG device -> head
     0.975554 -0.034041 -0.217109      -1.51 mm
     0.044503  0.998063  0.043482     -34.64 mm
     0.215208 -0.052081  0.975178      -6.31 mm
     0.000000  0.000000  0.000000       1.00
3 bad channels total
Read 268 MEG channels from info
Read 23 MEG compensation channels from info
0 compensation data sets in info
Setting up compensation data...
    No compensation set. Nothing more to do.
105 coil definitions read
Coordinate transformation: MEG device -> head
     0.975554 -0.034041 -0.217109      -1.51 mm
     0.044503  0.998063  0.043482     -34.64 mm
     0.215208 -0.052081  0.975178      -6.31 mm
     0.000000  0.000000  0.000000       1.00
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing rank from covariance with rank=None
    Using tolerance 3.4e-14 (2.2e-16 eps * 245 dim * 0.63  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
    Setting small MAG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 245 (0 small eigenvalues omitted)

---- Computing the forward solution for the guesses...
Making a spherical guess space with radius    72.0 mm...
Filtering (grid =     20 mm)...
Surface CM = (   0.0    0.0    0.0) mm
Surface fits inside a sphere with radius   72.0 mm
Surface extent:
    x =  -72.0 ...   72.0 mm
    y =  -72.0 ...   72.0 mm
    z =  -72.0 ...   72.0 mm
Grid extent:
    x =  -80.0 ...   80.0 mm
    y =  -80.0 ...   80.0 mm
    z =  -80.0 ...   80.0 mm
729 sources before omitting any.
178 sources after omitting infeasible sources not within 20.0 - 72.0 mm.
170 sources remaining after excluding the sources outside the surface and less than    5.0 mm inside.
Go through all guess source locations...
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[done 170 sources]
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
---- Fitted :    69.3 ms
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished
No projector specified for this dataset. Please consider the method self.add_proj.
1 time points fitted
Reading 4D PDF file /home/circleci/mne_data/MNE-phantom-4DBTi/3/e,rfhp1.0Hz...
Creating Neuromag info structure ...
... Setting channel info structure.
... putting coil transforms in Neuromag coordinates
... Reading digitization points from /home/circleci/mne_data/MNE-phantom-4DBTi/3/hs_file
Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine.
Reading 0 ... 13599  =      0.000 ...    20.052 secs...
Trigger channel has a non-zero initial value of 4350 (consider using initial_event=True to detect this event)
50 events found
Event IDs: [8192]
Not setting metadata
50 matching events found
Setting baseline interval to [-0.20053968578577042, 0.0] sec
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 50 events and 408 original time points ...
2 bad epochs dropped
Computing rank from data with rank=None
    Using tolerance 2.6e-09 (2.2e-16 eps * 245 dim * 4.7e+04  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
Reducing data rank from 245 -> 245
Estimating covariance using EMPIRICAL
Done.
Number of samples used : 6576
[done]
BEM               : <ConductorModel | Sphere (3 layers): r0=[0.0, 0.0, 0.0] R=80 mm>
MRI transform     : identity
Sphere model      : origin at (   0.00    0.00    0.00) mm, rad =    0.1 mm
Guess grid        :   20.0 mm
Guess mindist     :    5.0 mm
Guess exclude     :   20.0 mm
Using normal MEG coil definitions.
Noise covariance  : <Covariance | size : 245 x 245, n_samples : 6575, data : [[ 6.58999814e-27  3.84421337e-27  1.28166457e-27 ...  9.67818745e-29
  -3.17428979e-28 -3.76211498e-28]
 [ 3.84421337e-27  7.09542089e-27  3.22401648e-27 ...  1.04141267e-27
   7.15865669e-28  9.76599754e-28]
 [ 1.28166457e-27  3.22401648e-27  1.17446488e-26 ...  1.49708010e-27
  -1.60822410e-28  1.73060305e-27]
 ...
 [ 9.67818745e-29  1.04141267e-27  1.49708010e-27 ...  1.05814038e-26
   6.01071067e-27  6.20173942e-27]
 [-3.17428979e-28  7.15865669e-28 -1.60822410e-28 ...  6.01071067e-27
   1.25861185e-26  6.85502070e-27]
 [-3.76211498e-28  9.76599754e-28  1.73060305e-27 ...  6.20173942e-27
   6.85502070e-27  1.47521229e-26]]>

Coordinate transformation: MRI (surface RAS) -> head
     1.000000  0.000000  0.000000       0.00 mm
     0.000000  1.000000  0.000000       0.00 mm
     0.000000  0.000000  1.000000       0.00 mm
     0.000000  0.000000  0.000000       1.00
Coordinate transformation: MEG device -> head
     0.975577 -0.033678 -0.217061      -1.49 mm
     0.044611  0.997960  0.045666     -34.78 mm
     0.215080 -0.054233  0.975089      -6.31 mm
     0.000000  0.000000  0.000000       1.00
3 bad channels total
Read 268 MEG channels from info
Read 23 MEG compensation channels from info
0 compensation data sets in info
Setting up compensation data...
    No compensation set. Nothing more to do.
105 coil definitions read
Coordinate transformation: MEG device -> head
     0.975577 -0.033678 -0.217061      -1.49 mm
     0.044611  0.997960  0.045666     -34.78 mm
     0.215080 -0.054233  0.975089      -6.31 mm
     0.000000  0.000000  0.000000       1.00
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing rank from covariance with rank=None
    Using tolerance 1.9e-14 (2.2e-16 eps * 245 dim * 0.34  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
    Setting small MAG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 245 (0 small eigenvalues omitted)

---- Computing the forward solution for the guesses...
Making a spherical guess space with radius    72.0 mm...
Filtering (grid =     20 mm)...
Surface CM = (   0.0    0.0    0.0) mm
Surface fits inside a sphere with radius   72.0 mm
Surface extent:
    x =  -72.0 ...   72.0 mm
    y =  -72.0 ...   72.0 mm
    z =  -72.0 ...   72.0 mm
Grid extent:
    x =  -80.0 ...   80.0 mm
    y =  -80.0 ...   80.0 mm
    z =  -80.0 ...   80.0 mm
729 sources before omitting any.
178 sources after omitting infeasible sources not within 20.0 - 72.0 mm.
170 sources remaining after excluding the sources outside the surface and less than    5.0 mm inside.
Go through all guess source locations...
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[done 170 sources]
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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---- Fitted :    69.3 ms
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No projector specified for this dataset. Please consider the method self.add_proj.
1 time points fitted
Reading 4D PDF file /home/circleci/mne_data/MNE-phantom-4DBTi/4/e,rfhp1.0Hz...
Creating Neuromag info structure ...
... Setting channel info structure.
... putting coil transforms in Neuromag coordinates
... Reading digitization points from /home/circleci/mne_data/MNE-phantom-4DBTi/4/hs_file
Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine.
Reading 0 ... 13599  =      0.000 ...    20.052 secs...
Trigger channel has a non-zero initial value of 4350 (consider using initial_event=True to detect this event)
50 events found
Event IDs: [8192]
Not setting metadata
50 matching events found
Setting baseline interval to [-0.20053968578577042, 0.0] sec
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 50 events and 408 original time points ...
2 bad epochs dropped
Computing rank from data with rank=None
    Using tolerance 2.8e-09 (2.2e-16 eps * 245 dim * 5.1e+04  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
Reducing data rank from 245 -> 245
Estimating covariance using EMPIRICAL
Done.
Number of samples used : 6576
[done]
BEM               : <ConductorModel | Sphere (3 layers): r0=[0.0, 0.0, 0.0] R=80 mm>
MRI transform     : identity
Sphere model      : origin at (   0.00    0.00    0.00) mm, rad =    0.1 mm
Guess grid        :   20.0 mm
Guess mindist     :    5.0 mm
Guess exclude     :   20.0 mm
Using normal MEG coil definitions.
Noise covariance  : <Covariance | size : 245 x 245, n_samples : 6575, data : [[ 8.40101009e-27  5.10513936e-27  3.52303974e-27 ...  4.27457778e-28
   5.38233857e-28  1.06793751e-28]
 [ 5.10513936e-27  8.00477278e-27  4.15792686e-27 ...  1.30206685e-28
  -1.40306943e-28 -4.32057779e-28]
 [ 3.52303974e-27  4.15792686e-27  1.22353020e-26 ... -1.11884928e-28
  -2.17314181e-29 -9.64256593e-28]
 ...
 [ 4.27457778e-28  1.30206685e-28 -1.11884928e-28 ...  1.23243102e-26
   8.97110034e-27  8.94566869e-27]
 [ 5.38233857e-28 -1.40306943e-28 -2.17314181e-29 ...  8.97110034e-27
   1.63267153e-26  1.08360546e-26]
 [ 1.06793751e-28 -4.32057779e-28 -9.64256593e-28 ...  8.94566869e-27
   1.08360546e-26  1.98210291e-26]]>

Coordinate transformation: MRI (surface RAS) -> head
     1.000000  0.000000  0.000000       0.00 mm
     0.000000  1.000000  0.000000       0.00 mm
     0.000000  0.000000  1.000000       0.00 mm
     0.000000  0.000000  0.000000       1.00
Coordinate transformation: MEG device -> head
     0.975557 -0.033946 -0.217110      -1.50 mm
     0.044391  0.998071  0.043409     -34.60 mm
     0.215218 -0.051986  0.975181      -6.32 mm
     0.000000  0.000000  0.000000       1.00
3 bad channels total
Read 268 MEG channels from info
Read 23 MEG compensation channels from info
0 compensation data sets in info
Setting up compensation data...
    No compensation set. Nothing more to do.
105 coil definitions read
Coordinate transformation: MEG device -> head
     0.975557 -0.033946 -0.217110      -1.50 mm
     0.044391  0.998071  0.043409     -34.60 mm
     0.215218 -0.051986  0.975181      -6.32 mm
     0.000000  0.000000  0.000000       1.00
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing rank from covariance with rank=None
    Using tolerance 2.1e-14 (2.2e-16 eps * 245 dim * 0.39  max singular value)
    Estimated rank (mag): 245
    MAG: rank 245 computed from 245 data channels with 0 projectors
    Setting small MAG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 245 (0 small eigenvalues omitted)

---- Computing the forward solution for the guesses...
Making a spherical guess space with radius    72.0 mm...
Filtering (grid =     20 mm)...
Surface CM = (   0.0    0.0    0.0) mm
Surface fits inside a sphere with radius   72.0 mm
Surface extent:
    x =  -72.0 ...   72.0 mm
    y =  -72.0 ...   72.0 mm
    z =  -72.0 ...   72.0 mm
Grid extent:
    x =  -80.0 ...   80.0 mm
    y =  -80.0 ...   80.0 mm
    z =  -80.0 ...   80.0 mm
729 sources before omitting any.
178 sources after omitting infeasible sources not within 20.0 - 72.0 mm.
170 sources remaining after excluding the sources outside the surface and less than    5.0 mm inside.
Go through all guess source locations...
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[done 170 sources]
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---- Fitted :    69.3 ms
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No projector specified for this dataset. Please consider the method self.add_proj.
1 time points fitted

Compute localisation errors

actual_pos = 0.01 * np.array([[0.16, 1.61, 5.13],
                              [0.17, 1.35, 4.15],
                              [0.16, 1.05, 3.19],
                              [0.13, 0.80, 2.26]])
actual_pos = np.dot(actual_pos, [[0, 1, 0], [-1, 0, 0], [0, 0, 1]])

errors = 1e3 * np.linalg.norm(actual_pos - pos, axis=1)
print("errors (mm) : %s" % errors)
errors (mm) : [1.44409481 1.37628851 1.1860296  1.17616473]

Plot the dipoles in 3D

actual_amp = np.ones(len(dip))  # misc amp to create Dipole instance
actual_gof = np.ones(len(dip))  # misc GOF to create Dipole instance
dip = mne.Dipole(dip.times, pos, actual_amp, ori, actual_gof)
dip_true = mne.Dipole(dip.times, actual_pos, actual_amp, ori, actual_gof)

fig = mne.viz.plot_alignment(evoked.info, bem=sphere, surfaces=[])

# Plot the position of the actual dipole
fig = mne.viz.plot_dipole_locations(dipoles=dip_true, mode='sphere',
                                    color=(1., 0., 0.), fig=fig)
# Plot the position of the estimated dipole
fig = mne.viz.plot_dipole_locations(dipoles=dip, mode='sphere',
                                    color=(1., 1., 0.), fig=fig)
90 phantom 4DBTi
Getting helmet for system Magnes_3600wh
Channel types:: mag: 245

Total running time of the script: ( 0 minutes 11.094 seconds)

Estimated memory usage: 24 MB

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