.. _sphx_glr_auto_tutorials_plot_temporal_searchlight_decoding.py:


.. _tut_searchlight_decoding:

=======================================================
Run temporal searchlight decoding on event related data
=======================================================

In this tutorial we show how to run a temporal window decoding
on event related data. We'll try to decode tools VS faces in the
working memory data.



.. code-block:: python

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

    import os.path as op

    import numpy as np
    import mne
    import hcp
    from hcp import preprocessing as preproc
    from sklearn.preprocessing import LabelBinarizer
    from sklearn.metrics import roc_auc_score
    from sklearn.cross_validation import StratifiedKFold
    from mne.decoding import GeneralizationAcrossTime

    mne.set_log_level('WARNING')

    # we assume our data is inside its designated folder under $HOME
    storage_dir = op.expanduser('~')
    hcp_params = dict(
        hcp_path=op.join(storage_dir, 'mne-hcp-data', 'HCP'),
        subject='105923',
        data_type='task_working_memory')

    # these values are looked up from the HCP manual
    tmin, tmax = -1.5, 2.5
    decim = 3







We know from studying either the manual or the trial info about the mapping
of events.



.. code-block:: python

    event_id = dict(face=1, tool=2)







we first collect epochs across runs and essentially adopt the code
shown in :ref:`tut_reproduce_erf`.



.. code-block:: python


    epochs = list()
    for run_index in [0, 1]:
        hcp_params['run_index'] = run_index
        trial_info = hcp.read_trial_info(**hcp_params)

        events = np.c_[
            trial_info['stim']['codes'][:, 6] - 1,  # time sample
            np.zeros(len(trial_info['stim']['codes'])),
            trial_info['stim']['codes'][:, 3]  # event codes
        ].astype(int)

        # for some reason in the HCP data the time events may not always be unique
        unique_subset = np.nonzero(np.r_[1, np.diff(events[:, 0])])[0]
        events = events[unique_subset]  # use diff to find first unique events
        subset = np.in1d(events[:, 2], event_id.values())
        epochs_hcp = hcp.read_epochs(**hcp_params).decimate(decim)
        epochs_hcp = epochs_hcp[unique_subset][subset]
        epochs_hcp.events[:, 2] = events[subset, 2]
        epochs_hcp.event_id = event_id
        epochs_hcp.crop(-0.1, 0.5)
        epochs.append(preproc.interpolate_missing(epochs_hcp, **hcp_params))

    epochs = mne.concatenate_epochs(epochs)
    del epochs_hcp





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

 Out::

    The measurement information indicates a low-pass frequency of 1017.26 Hz. The decim=3 parameter will result in a sampling frequency of 169.543 Hz, which can cause aliasing artifacts.
    The measurement information indicates a low-pass frequency of 1017.26 Hz. The decim=3 parameter will result in a sampling frequency of 169.543 Hz, which can cause aliasing artifacts.


Now we can proceed as shown in the MNE-Python decoding tutorials



.. code-block:: python


    y = LabelBinarizer().fit_transform(epochs.events[:, 2]).ravel()

    cv = StratifiedKFold(y=y)  # do a stratified cross-validation

    gat = GeneralizationAcrossTime(predict_mode='cross-validation', n_jobs=1,
                                   cv=cv, scorer=roc_auc_score)
    # fit and score
    gat.fit(epochs, y=y)
    gat.score(epochs)







Ploting the temporal connectome and the evolution of discriminability.



.. code-block:: python

    gat.plot()
    gat.plot_diagonal()



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_tutorials/images/sphx_glr_plot_temporal_searchlight_decoding_001.png
            :scale: 47

    *

      .. image:: /auto_tutorials/images/sphx_glr_plot_temporal_searchlight_decoding_002.png
            :scale: 47




**Total running time of the script:** ( 0 minutes  54.662 seconds)



.. container:: sphx-glr-download

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



.. container:: sphx-glr-download

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

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

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