.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ERP/plot_classify_EEG_tangentspace.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_ERP_plot_classify_EEG_tangentspace.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_ERP_plot_classify_EEG_tangentspace.py:


====================================================================
ERP EEG decoding in Tangent space.
====================================================================

Decoding applied to EEG data in sensor space decomposed using Xdawn.
After spatial filtering, covariances matrices are estimated, then projected in
the tangent space and classified with a logistic regression.

.. GENERATED FROM PYTHON SOURCE LINES 11-31

.. code-block:: Python

    # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
    #
    # License: BSD (3-clause)

    import numpy as np
    from matplotlib import pyplot as plt
    import mne
    from mne import io
    from mne.datasets import sample
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import KFold
    from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
    from sklearn.pipeline import make_pipeline

    from pyriemann.estimation import XdawnCovariances
    from pyriemann.tangentspace import TangentSpace

    print(__doc__)









.. GENERATED FROM PYTHON SOURCE LINES 32-34

Set parameters and read data
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 34-69

.. code-block:: Python


    data_path = str(sample.data_path())
    raw_fname = data_path + "/MEG/sample/sample_audvis_filt-0-40_raw.fif"
    event_fname = data_path + "/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif"
    tmin, tmax = -0.0, 1
    event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)

    # Setup for reading the raw data
    raw = io.Raw(raw_fname, preload=True, verbose=False)
    raw.filter(2, None, method="iir")  # replace baselining with high-pass
    events = mne.read_events(event_fname)

    raw.info["bads"] = ["MEG 2443"]  # set bad channels
    picks = mne.pick_types(
        raw.info, meg=False, eeg=True, stim=False, eog=False, exclude="bads"
    )

    # Read epochs
    epochs = mne.Epochs(
        raw,
        events,
        event_id,
        tmin,
        tmax,
        proj=False,
        picks=picks,
        baseline=None,
        preload=True,
        verbose=False,
    )

    labels = epochs.events[:, -1]
    evoked = epochs.average()
    epochs_data = epochs.get_data(copy=False)





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

 .. code-block:: none

    Filtering raw data in 1 contiguous segment
    Setting up high-pass filter at 2 Hz

    IIR filter parameters
    ---------------------
    Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter:
    - Filter order 8 (effective, after forward-backward)
    - Cutoff at 2.00 Hz: -6.02 dB

    Removing projector <Projection | PCA-v1, active : False, n_channels : 102>
    Removing projector <Projection | PCA-v2, active : False, n_channels : 102>
    Removing projector <Projection | PCA-v3, active : False, n_channels : 102>




.. GENERATED FROM PYTHON SOURCE LINES 70-72

Decoding in tangent space with a logistic regression
----------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 72-100

.. code-block:: Python


    n_components = 2  # pick some components

    # Define a monte-carlo cross-validation generator (reduce variance):
    cv = KFold(n_splits=10, shuffle=True, random_state=42)

    clf = make_pipeline(
        XdawnCovariances(n_components),
        TangentSpace(metric="riemann"),
        LogisticRegression(),
    )

    preds = np.zeros(len(labels))

    for train_idx, test_idx in cv.split(epochs_data):
        y_train, y_test = labels[train_idx], labels[test_idx]

        clf.fit(epochs_data[train_idx], y_train)
        preds[test_idx] = clf.predict(epochs_data[test_idx])

    # Printing the results
    acc = np.mean(preds == labels)
    print("Classification accuracy: %f " % (acc))

    names = ["audio left", "audio right", "vis left", "vis right"]
    cm = confusion_matrix(labels, preds)
    ConfusionMatrixDisplay(cm, display_labels=names).plot()
    plt.show()



.. image-sg:: /auto_examples/ERP/images/sphx_glr_plot_classify_EEG_tangentspace_001.png
   :alt: plot classify EEG tangentspace
   :srcset: /auto_examples/ERP/images/sphx_glr_plot_classify_EEG_tangentspace_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Classification accuracy: 0.902778 





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

   **Total running time of the script:** (0 minutes 5.301 seconds)


.. _sphx_glr_download_auto_examples_ERP_plot_classify_EEG_tangentspace.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_