.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_on_surface.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_on_surface.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_on_surface.py:


==================================
Plot sensors on realistic surfaces
==================================

Sometimes you may want to plot sensor positions on a realistic surface,
like an actual head.
The ``fsaverage`` standard brain template shipped with the Freesurfer
package provides surfaces of the head, so we are going to use them below.

For more information, check out these MNE resources:

- https://mne.tools/dev/auto_examples/visualization/eeg_on_scalp.html
- https://mne.tools/dev/auto_examples/visualization/montage_sgskip.html

.. currentmodule:: eeg_positions

.. GENERATED FROM PYTHON SOURCE LINES 20-24

We start by importing what we need.

This example furthermore assumes that you have installed eeg_positions
via ``pip install eeg_positions[docs]``.

.. GENERATED FROM PYTHON SOURCE LINES 24-30

.. code-block:: Python

    import matplotlib.pyplot as plt
    import mne
    import numpy as np

    from eeg_positions import get_elec_coords








.. GENERATED FROM PYTHON SOURCE LINES 31-32

Get fsaverage data via MNE-Python, this will download some data.

.. GENERATED FROM PYTHON SOURCE LINES 32-34

.. code-block:: Python

    subjects_dir = mne.datasets.fetch_fsaverage().parent





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Using default location ~/mne_data for montage coregistration...
    Attempting to create new mne-python configuration file:
    /home/docs/.mne/mne-python.json
    0 files missing from root.txt in /home/docs/mne_data/MNE-fsaverage-data
    0 files missing from bem.txt in /home/docs/mne_data/MNE-fsaverage-data/fsaverage




.. GENERATED FROM PYTHON SOURCE LINES 35-37

Get idealized sensor positions that were computed on a sphere
and export as :class:`mne.channels.DigMontage`.

.. GENERATED FROM PYTHON SOURCE LINES 37-39

.. code-block:: Python

    montage = get_elec_coords(as_mne_montage=True)








.. GENERATED FROM PYTHON SOURCE LINES 40-42

For the code below we need to provide an :class:`mne.Info` object.
The sampling frequency does not matter so we are setting it to 1.

.. GENERATED FROM PYTHON SOURCE LINES 42-45

.. code-block:: Python

    info = mne.create_info(ch_names=montage.ch_names, sfreq=1, ch_types="eeg")
    info.set_montage(montage)






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            <button class="channel-names-btn" onclick="alert('Good EEG:\n\nNz, T10, Iz, T9, Cz, NFpz, Fpz, AFpz, AFz, AFFz, Fz, FFCz, FCz, FCCz, CCPz, CPz, CPPz, Pz, PPOz, POz, POOz, Oz, OIz, N2h, N2, AFp10, AF10, AFF10, F10, FFT10, FT10, FTT10, TTP10, TP10, TPP10, P10, PPO10, PO10, POO10, I2, I2h, N1h, N1, AFp9, AF9, AFF9, F9, FFT9, FT9, FTT9, TTP9, TP9, TPP9, P9, PPO9, PO9, POO9, I1, I1h, T9h, T7, T7h, C5, C5h, C3, C3h, C1, C1h, C2h, C2, C4h, C4, C6h, C6, T8h, T8, T10h, NFp2h, NFp2, AFp10h, AF10h, AFF10h, F10h, FFT10h, FT10h, FTT10h, TTP10h, TP10h, TPP10h, P10h, PPO10h, PO10h, POO10h, OI2, OI2h, NFp1h, NFp1, AFp9h, AF9h, AFF9h, F9h, FFT9h, FT9h, FTT9h, TTP9h, TP9h, TPP9h, P9h, PPO9h, PO9h, POO9h, OI1, OI1h, Fp2h, Fp2, AFp8, AF8, AFF8, F8, FFT8, FT8, FTT8, TTP8, TP8, TPP8, P8, PPO8, PO8, POO8, O2, O2h, Fp1h, Fp1, AFp7, AF7, AFF7, F7, FFT7, FT7, FTT7, TTP7, TP7, TPP7, P7, PPO7, PO7, POO7, O1, O1h, AFp7h, AFp5, AFp5h, AFp3, AFp3h, AFp1, AFp1h, AFp2h, AFp2, AFp4h, AFp4, AFp6h, AFp6, AFp8h, AF7h, AF5, AF5h, AF3, AF3h, AF1, AF1h, AF2h, AF2, AF4h, AF4, AF6h, AF6, AF8h, AFF7h, AFF5, AFF5h, AFF3, AFF3h, AFF1, AFF1h, AFF2h, AFF2, AFF4h, AFF4, AFF6h, AFF6, AFF8h, F7h, F5, F5h, F3, F3h, F1, F1h, F2h, F2, F4h, F4, F6h, F6, F8h, FFT7h, FFC5, FFC5h, FFC3, FFC3h, FFC1, FFC1h, FFC2h, FFC2, FFC4h, FFC4, FFC6h, FFC6, FFT8h, FT7h, FC5, FC5h, FC3, FC3h, FC1, FC1h, FC2h, FC2, FC4h, FC4, FC6h, FC6, FT8h, FTT7h, FCC5, FCC5h, FCC3, FCC3h, FCC1, FCC1h, FCC2h, FCC2, FCC4h, FCC4, FCC6h, FCC6, FTT8h, TTP7h, CCP5, CCP5h, CCP3, CCP3h, CCP1, CCP1h, CCP2h, CCP2, CCP4h, CCP4, CCP6h, CCP6, TTP8h, TP7h, CP5, CP5h, CP3, CP3h, CP1, CP1h, CP2h, CP2, CP4h, CP4, CP6h, CP6, TP8h, TPP7h, CPP5, CPP5h, CPP3, CPP3h, CPP1, CPP1h, CPP2h, CPP2, CPP4h, CPP4, CPP6h, CPP6, TPP8h, P7h, P5, P5h, P3, P3h, P1, P1h, P2h, P2, P4h, P4, P6h, P6, P8h, PPO7h, PPO5, PPO5h, PPO3, PPO3h, PPO1, PPO1h, PPO2h, PPO2, PPO4h, PPO4, PPO6h, PPO6, PPO8h, PO7h, PO5, PO5h, PO3, PO3h, PO1, PO1h, PO2h, PO2, PO4h, PO4, PO6h, PO6, PO8h, POO7h, POO5, POO5h, POO3, POO3h, POO1, POO1h, POO2h, POO2, POO4h, POO4, POO6h, POO6, POO8h')" title="(Click to open in popup)&#13;&#13;Nz, T10, Iz, T9, Cz, NFpz, Fpz, AFpz, AFz, AFFz, Fz, FFCz, FCz, FCCz, CCPz, CPz, CPPz, Pz, PPOz, POz, POOz, Oz, OIz, N2h, N2, AFp10, AF10, AFF10, F10, FFT10, FT10, FTT10, TTP10, TP10, TPP10, P10, PPO10, PO10, POO10, I2, I2h, N1h, N1, AFp9, AF9, AFF9, F9, FFT9, FT9, FTT9, TTP9, TP9, TPP9, P9, PPO9, PO9, POO9, I1, I1h, T9h, T7, T7h, C5, C5h, C3, C3h, C1, C1h, C2h, C2, C4h, C4, C6h, C6, T8h, T8, T10h, NFp2h, NFp2, AFp10h, AF10h, AFF10h, F10h, FFT10h, FT10h, FTT10h, TTP10h, TP10h, TPP10h, P10h, PPO10h, PO10h, POO10h, OI2, OI2h, NFp1h, NFp1, AFp9h, AF9h, AFF9h, F9h, FFT9h, FT9h, FTT9h, TTP9h, TP9h, TPP9h, P9h, PPO9h, PO9h, POO9h, OI1, OI1h, Fp2h, Fp2, AFp8, AF8, AFF8, F8, FFT8, FT8, FTT8, TTP8, TP8, TPP8, P8, PPO8, PO8, POO8, O2, O2h, Fp1h, Fp1, AFp7, AF7, AFF7, F7, FFT7, FT7, FTT7, TTP7, TP7, TPP7, P7, PPO7, PO7, POO7, O1, O1h, AFp7h, AFp5, AFp5h, AFp3, AFp3h, AFp1, AFp1h, AFp2h, AFp2, AFp4h, AFp4, AFp6h, AFp6, AFp8h, AF7h, AF5, AF5h, AF3, AF3h, AF1, AF1h, AF2h, AF2, AF4h, AF4, AF6h, AF6, AF8h, AFF7h, AFF5, AFF5h, AFF3, AFF3h, AFF1, AFF1h, AFF2h, AFF2, AFF4h, AFF4, AFF6h, AFF6, AFF8h, F7h, F5, F5h, F3, F3h, F1, F1h, F2h, F2, F4h, F4, F6h, F6, F8h, FFT7h, FFC5, FFC5h, FFC3, FFC3h, FFC1, FFC1h, FFC2h, FFC2, FFC4h, FFC4, FFC6h, FFC6, FFT8h, FT7h, FC5, FC5h, FC3, FC3h, FC1, FC1h, FC2h, FC2, FC4h, FC4, FC6h, FC6, FT8h, FTT7h, FCC5, FCC5h, FCC3, FCC3h, FCC1, FCC1h, FCC2h, FCC2, FCC4h, FCC4, FCC6h, FCC6, FTT8h, TTP7h, CCP5, CCP5h, CCP3, CCP3h, CCP1, CCP1h, CCP2h, CCP2, CCP4h, CCP4, CCP6h, CCP6, TTP8h, TP7h, CP5, CP5h, CP3, CP3h, CP1, CP1h, CP2h, CP2, CP4h, CP4, CP6h, CP6, TP8h, TPP7h, CPP5, CPP5h, CPP3, CPP3h, CPP1, CPP1h, CPP2h, CPP2, CPP4h, CPP4, CPP6h, CPP6, TPP8h, P7h, P5, P5h, P3, P3h, P1, P1h, P2h, P2, P4h, P4, P6h, P6, P8h, PPO7h, PPO5, PPO5h, PPO3, PPO3h, PPO1, PPO1h, PPO2h, PPO2, PPO4h, PPO4, PPO6h, PPO6, PPO8h, PO7h, PO5, PO5h, PO3, PO3h, PO1, PO1h, PO2h, PO2, PO4h, PO4, PO6h, PO6, PO8h, POO7h, POO5, POO5h, POO3, POO3h, POO1, POO1h, POO2h, POO2, POO4h, POO4, POO6h, POO6, POO8h">
                345
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        <td>Head & sensor digitization</td>
    
        <td>348 points</td>
    
    </tr>
    







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        <td>Highpass</td>
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    </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-49

As a sanity check, let's create a spherical head model first.
We expect a perfect fit with our idealized electrode positions,
because they were also computed on a sphere.

.. GENERATED FROM PYTHON SOURCE LINES 49-70

.. code-block:: Python

    sphere = mne.make_sphere_model(r0="auto", head_radius="auto", info=info)

    # We need a transform from the coordinates of our montage to the surface.
    # Given that both are spheres, we do not need to actually transform, and
    # can just use the identity transform.
    trans = mne.Transform("head", "mri", trans=np.eye(4))

    fig = mne.viz.plot_alignment(
        info=info,
        trans=trans,
        bem=sphere,  # our spherical head model
        surfaces={"head": 0.8},  # alpha value
        coord_frame="head",
        eeg=["original", "projected"],
        dig="fiducials",
        show_axes=True,
        mri_fiducials=False,
    )

    mne.viz.set_3d_view(figure=fig, azimuth=65, elevation=75)




.. image-sg:: /auto_examples/images/sphx_glr_plot_on_surface_001.png
   :alt: plot on surface
   :srcset: /auto_examples/images/sphx_glr_plot_on_surface_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Fitted sphere radius:         95.0 mm
    Origin head coordinates:      0.0 0.0 0.0 mm
    Origin device coordinates:    0.0 0.0 0.0 mm

    Equiv. model fitting -> RV = 0.00349096 %%
    mu1 = 0.944696    lambda1 = 0.1372
    mu2 = 0.667439    lambda2 = 0.683766
    mu3 = -0.265718    lambda3 = -0.0105969
    Set up EEG sphere model with scalp radius    95.0 mm

    Using pyvistaqt 3d backend.
    Channel types:: eeg: 345
    Projecting sensors to the head surface




.. GENERATED FROM PYTHON SOURCE LINES 71-77

As can be seen above, the fit of our idealized sensor positions with a spherical head
model is perfect.

Now let's proceed to plot our positions on the fsaverage head.
We expect this to be potentially problematic, because we computed our positions
on a sphere ... and a sphere is usually a poor approximation of a human head.

.. GENERATED FROM PYTHON SOURCE LINES 77-98

.. code-block:: Python


    # instead of the identity transform as above, we are using the inbuilt transformation
    # from MNE-Python that can transform a Montage to fit fsaverage.
    # Note that this only works for "fsaverage" and not other surfaces.
    trans = "fsaverage"

    fig = mne.viz.plot_alignment(
        info=info,
        trans=trans,
        subject="fsaverage",  # this is fsaverage
        subjects_dir=subjects_dir,  # directory of fsaverage
        surfaces={"head": 0.8},  # alpha value
        coord_frame="head",
        eeg=["original", "projected"],
        dig="fiducials",
        show_axes=True,
        mri_fiducials=True,
    )

    mne.viz.set_3d_view(figure=fig, azimuth=135, elevation=80)




.. image-sg:: /auto_examples/images/sphx_glr_plot_on_surface_002.png
   :alt: plot on surface
   :srcset: /auto_examples/images/sphx_glr_plot_on_surface_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Using outer_skin.surf for head surface.
    Channel types:: eeg: 345
    Projecting sensors to the head surface




.. GENERATED FROM PYTHON SOURCE LINES 99-110

As expected, the fit above is very poor.

The electrode positions that were computed on a sphere are not easily projected
to the fsaverage head.

However, we can try to "scale" fsaverage in three dimensions to make the fit
slightly better.

Below we will try to scale fsaverage to minimize the distance of sensors to the scalp
surface, while still trying to have the landmarks (LPA, RPA, NAS) aligned between MRI
and montage.

.. GENERATED FROM PYTHON SOURCE LINES 110-147

.. code-block:: Python


    fiducials = "estimated"  # taken from fsaverage
    subject = "fsaverage"

    coreg = mne.coreg.Coregistration(info, subject, subjects_dir, fiducials=fiducials)

    coreg.set_scale_mode("3-axis")  # can also be "uniform", but the fit would be worse

    coreg.fit_fiducials(verbose=True)

    coreg.fit_icp(
        n_iterations=40,
        lpa_weight=1.0,
        nasion_weight=1.0,
        rpa_weight=1.0,
        hsp_weight=0,
        eeg_weight=1.0,
        hpi_weight=0,
        verbose=True,
    )
    trans = coreg.trans

    fig = mne.viz.plot_alignment(
        info=info,
        trans=trans,  # this is the optimized transform based on scaling fsaverage
        subject="fsaverage",
        subjects_dir=subjects_dir,
        surfaces={"head": 0.8},  # alpha value
        coord_frame="head",
        eeg=["original", "projected"],
        dig="fiducials",
        show_axes=True,
        mri_fiducials=True,
    )

    mne.viz.set_3d_view(figure=fig, azimuth=135, elevation=80)




.. image-sg:: /auto_examples/images/sphx_glr_plot_on_surface_003.png
   :alt: plot on surface
   :srcset: /auto_examples/images/sphx_glr_plot_on_surface_003.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Using high resolution head model in /home/docs/mne_data/MNE-fsaverage-data/fsaverage/bem/fsaverage-head-dense.fif
        Triangle neighbors and vertex normals...
    Estimating fiducials from fsaverage.
    Aligning using fiducials
    Start median distance:   8.99 mm
    Enforcing 1 scaling parameter for fit with fiducials.
    End   median distance:  12.84 mm
    Aligning using ICP
    Start     median distance:  12.84 mm
      ICP  1  median distance:   9.54 mm
      ICP  2  median distance:   6.57 mm
      ICP  3  median distance:   4.17 mm
      ICP  4  median distance:   3.34 mm
      ICP  5  median distance:   2.87 mm
      ICP  6  median distance:   2.69 mm
      ICP  7  median distance:   2.54 mm
      ICP  8  median distance:   2.54 mm
      ICP  9  median distance:   2.47 mm
      ICP 10  median distance:   2.36 mm
      ICP 11  median distance:   2.20 mm
      ICP 12  median distance:   2.15 mm
      ICP 13  median distance:   2.09 mm
      ICP 14  median distance:   2.05 mm
      ICP 15  median distance:   1.98 mm
      ICP 16  median distance:   1.93 mm
      ICP 17  median distance:   1.89 mm
      ICP 18  median distance:   1.80 mm
      ICP 19  median distance:   1.78 mm
      ICP 20  median distance:   1.79 mm
      ICP 21  median distance:   1.75 mm
      ICP 22  median distance:   1.75 mm
      ICP 23  median distance:   1.78 mm
      ICP 24  median distance:   1.79 mm
      ICP 25  median distance:   1.79 mm
      ICP 26  median distance:   1.80 mm
      ICP 27  median distance:   1.78 mm
      ICP 28  median distance:   1.80 mm
      ICP 29  median distance:   1.79 mm
      ICP 30  median distance:   1.75 mm
      ICP 31  median distance:   1.73 mm
      ICP 32  median distance:   1.71 mm
      ICP 33  median distance:   1.73 mm
      ICP 34  median distance:   1.74 mm
      ICP 35  median distance:   1.75 mm
      ICP 36  median distance:   1.75 mm
      ICP 37  median distance:   1.76 mm
      ICP 38  median distance:   1.77 mm
      ICP 39  median distance:   1.73 mm
      ICP 40  median distance:   1.71 mm
    End       median distance:   1.71 mm
    Using outer_skin.surf for head surface.
    Channel types:: eeg: 345
    Projecting sensors to the head surface




.. GENERATED FROM PYTHON SOURCE LINES 149-160

Luckily, the fit is now slightly better.

It is still not perfect, so for actual source reconstruction, you should use sensor
positions that are measured (digitized), rather than computed on a sphere
(idealized).

The 1005 system eeg electrode positions shipped with MNE-Python happen to not be
calculated on a sphrere (idealized), so they should be a better fit on a realistic
surface.

Let's have a look, first in 2D, then in 3D

.. GENERATED FROM PYTHON SOURCE LINES 160-176

.. code-block:: Python


    # Getting the MNE-Python inbuilt 1005 system positions
    montage_mne = mne.channels.make_standard_montage(kind="standard_1005")
    info_mne = mne.create_info(ch_names=montage_mne.ch_names, sfreq=1, ch_types="eeg")
    info_mne.set_montage(montage_mne)

    # Plot in 2D versus the eeg_positions montage
    fig, axs = plt.subplots(1, 2)
    fig.set_layout_engine("constrained")

    for i, this_info in enumerate([info, info_mne]):
        ax = axs.flat[i]
        this_info.plot_sensors(axes=ax, show=False)
        ax.set_title(["eeg_positions", "mne inbuilt"][i])

    fig



.. image-sg:: /auto_examples/images/sphx_glr_plot_on_surface_004.png
   :alt: eeg_positions, mne inbuilt
   :srcset: /auto_examples/images/sphx_glr_plot_on_surface_004.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 640x480 with 2 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 177-184

As is maybe apparent from the plot above, the inbuilt mne 1005 montage does not
look as clear when projected to a spherical 2D head model. The inbuilt
``eeg_positions`` montage, on the other hand, can really shine in this situation
(because that is what it was designed for).

Fortuntely, the inbuilt mne montage will give us a much better fit on a realistic
surface, see below, and compare to the two plots above.

.. GENERATED FROM PYTHON SOURCE LINES 184-202

.. code-block:: Python


    trans = "fsaverage"

    fig = mne.viz.plot_alignment(
        info=info_mne,
        trans=trans,
        subject="fsaverage",  # this is fsaverage
        subjects_dir=subjects_dir,  # directory of fsaverage
        surfaces={"head": 0.8},  # alpha value
        coord_frame="head",
        eeg=["original", "projected"],
        dig="fiducials",
        show_axes=True,
        mri_fiducials=True,
    )

    mne.viz.set_3d_view(figure=fig, azimuth=135, elevation=80)




.. image-sg:: /auto_examples/images/sphx_glr_plot_on_surface_005.png
   :alt: plot on surface
   :srcset: /auto_examples/images/sphx_glr_plot_on_surface_005.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Using outer_skin.surf for head surface.
    Channel types:: eeg: 343
    Projecting sensors to the head surface





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (2 minutes 23.987 seconds)


.. _sphx_glr_download_auto_examples_plot_on_surface.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_on_surface.ipynb <plot_on_surface.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_on_surface.py <plot_on_surface.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_on_surface.zip <plot_on_surface.zip>`