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]
  • ../../_images/sphx_glr_plot_phantom_4DBTi_001.png
  • ../../_images/sphx_glr_plot_phantom_4DBTi_002.png
  • ../../_images/sphx_glr_plot_phantom_4DBTi_003.png
  • ../../_images/sphx_glr_plot_phantom_4DBTi_004.png

Out:

Equiv. model fitting -> RV = 0.0037285 %
mu1 = 0.943943    lambda1 = 0.139085
mu2 = 0.665517    lambda2 = 0.684836
mu3 = -0.0972643    lambda3 = -0.0135513
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.
Current compensation grade : 0
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]
50 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 50 events and 408 original time points ...
2 bad epochs dropped
Computing data rank from raw 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 standard MEG coil definitions.

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 245 MEG channels from info
Read  23 MEG compensation channels from info
99 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
0 compensation data sets in info
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing data 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...
[done 170 sources]
---- Fitted :    69.3 ms
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.
Current compensation grade : 0
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]
50 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 50 events and 408 original time points ...
2 bad epochs dropped
Computing data rank from raw 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 standard MEG coil definitions.

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 245 MEG channels from info
Read  23 MEG compensation channels from info
99 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
0 compensation data sets in info
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing data 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...
[done 170 sources]
---- Fitted :    69.3 ms
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.
Current compensation grade : 0
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]
50 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 50 events and 408 original time points ...
2 bad epochs dropped
Computing data rank from raw 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 standard MEG coil definitions.

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 245 MEG channels from info
Read  23 MEG compensation channels from info
99 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
0 compensation data sets in info
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing data 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...
[done 170 sources]
---- Fitted :    69.3 ms
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.
Current compensation grade : 0
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]
50 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 50 events and 408 original time points ...
2 bad epochs dropped
Computing data rank from raw 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 standard MEG coil definitions.

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 245 MEG channels from info
Read  23 MEG compensation channels from info
99 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
0 compensation data sets in info
MEG coil definitions created in head coordinates.
Decomposing the sensor noise covariance matrix...
Computing data 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...
[done 170 sources]
---- Fitted :    69.3 ms
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)

Out:

errors (mm) : [1.4230156  1.37628851 1.24574753 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)
../../_images/sphx_glr_plot_phantom_4DBTi_005.png

Out:

Getting helmet for system Magnes_3600wh

Total running time of the script: ( 0 minutes 9.366 seconds)

Estimated memory usage: 8 MB

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