.. _sphx_glr_auto_tutorials_plot_compute_evoked_inverse_solution.py:


.. _tut_compute_inverse_erf:

========================================
Compute inverse solution for evoked data
========================================

Here we'll use our knowledge from the other examples and tutorials
to compute an inverse solution and apply it on event related fields.



.. code-block:: python

    # Author: Denis A. Enegemann
    # License: BSD 3 clause

    import os.path as op
    import mne
    import hcp
    from hcp import preprocessing as preproc







we assume our data is inside a designated folder under $HOME



.. code-block:: python

    storage_dir = op.expanduser('~/mne-hcp-data')
    hcp_path = op.join(storage_dir, 'HCP')
    recordings_path = op.join(storage_dir, 'hcp-meg')
    subjects_dir = op.join(storage_dir, 'hcp-subjects')
    subject = '105923'  # our test subject
    data_type = 'task_working_memory'
    run_index = 0







We're reading the evoked data.
These are the same as in :ref:`tut_plot_evoked`



.. code-block:: python


    hcp_evokeds = hcp.read_evokeds(onset='stim', subject=subject,
                                       data_type=data_type, hcp_path=hcp_path)
    for evoked in hcp_evokeds:
        if not evoked.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
            continue







We'll now use a convenience function to get our forward and source models
instead of computing them by hand.



.. code-block:: python


    src_outputs = hcp.anatomy.compute_forward_stack(
        subject=subject, subjects_dir=subjects_dir,
        hcp_path=hcp_path, recordings_path=recordings_path,
        # speed up computations here. Setting `add_dist` to True may improve the
        # accuracy.
        src_params=dict(add_dist=False),
        info_from=dict(data_type=data_type, run_index=run_index))

    fwd = src_outputs['fwd']







Now we can compute the noise covariance. For this purpose we will apply
the same filtering as was used for the computations of the ERF in the first
place. See also :ref:`tut_reproduce_erf`.



.. code-block:: python


    raw_noise = hcp.read_raw(subject=subject, hcp_path=hcp_path,
                             data_type='noise_empty_room')
    raw_noise.load_data()

    # apply ref channel correction and drop ref channels
    preproc.apply_ref_correction(raw_noise)

    # Note: MNE complains on Python 2.7
    raw_noise.filter(0.50, None, method='iir',
                     iir_params=dict(order=4, ftype='butter'), n_jobs=1)
    raw_noise.filter(None, 60, method='iir',
                     iir_params=dict(order=4, ftype='butter'), n_jobs=1)





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

 Out::

    The default output type is "ba" in 0.13 but will change to "sos" in 0.14
    The default output type is "ba" in 0.13 but will change to "sos" in 0.14


Note that using the empty room noise covariance will inflate the SNR of the
evkoked and renders comparisons  to `baseline` rather uninformative.



.. code-block:: python

    noise_cov = mne.compute_raw_covariance(raw_noise, method='empirical')








Now we assemble the inverse operator, project the data and show the results
on the `fsaverage` surface, the freesurfer average brain.



.. code-block:: python


    inv_op = mne.minimum_norm.make_inverse_operator(
        evoked.info, fwd, noise_cov=noise_cov)

    stc = mne.minimum_norm.apply_inverse(  # these data have a pretty high SNR and
        evoked, inv_op, method='MNE', lambda2=1./9.**2)  # 9 is a lovely number.

    stc = stc.to_original_src(
        src_outputs['src_fsaverage'], subjects_dir=subjects_dir)

    brain = stc.plot(subject='fsaverage', subjects_dir=subjects_dir, hemi='both')
    brain.set_time(145)  # we take the peak seen in :ref:`tut_plot_evoked` and
    brain.show_view('caudal')  # admire wide spread visual activation.



.. image:: /auto_tutorials/images/sphx_glr_plot_compute_evoked_inverse_solution_001.png
    :align: center


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

 Out::

    Updating smoothing matrix, be patient..
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    colormap: fmin=2.35e-11 fmid=2.79e-11 fmax=9.14e-11 transparent=1
    Updating smoothing matrix, be patient..
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    colormap: fmin=2.35e-11 fmid=2.79e-11 fmax=9.14e-11 transparent=1


**Total running time of the script:** ( 1 minutes  33.565 seconds)



.. container:: sphx-glr-download

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



.. container:: sphx-glr-download

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

.. rst-class:: sphx-glr-signature

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