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

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

.. _sphx_glr_auto_examples_plot_eeg_batch.py:


Complexity of every channel of a real EEG
=========================================

One minute of resting EEG, eyes open, from subject 1 of the PhysioNet EEG Motor
Movement/Imagery Dataset (64 channels, 160 Hz; Schalk et al. 2004, ODC-By 1.0).
MNE downloads and caches it on first run.

.. GENERATED FROM PYTHON SOURCE LINES 9-23

.. code-block:: Python


    import matplotlib.pyplot as plt
    import mne
    import numpy

    import complexeva

    (path,) = mne.datasets.eegbci.load_data(1, [1], update_path=False)
    raw = mne.io.read_raw_edf(path, preload=True, verbose=False)
    mne.datasets.eegbci.standardize(raw)
    raw.set_montage("colin27_1005")
    eeg = raw.get_data()  # (channels, time): time on the last axis
    print(eeg.shape)





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

 .. code-block:: none

    Using default location ~/mne_data for EEGBCI...
    Downloading EEGBCI data
    Downloading file 'S001/S001R01.edf' from 'https://physionet.org/files/eegmmidb/1.0.0/S001/S001R01.edf' to '/home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0'.
    Download complete in 00s (1.2 MB)
    (64, 9760)




.. GENERATED FROM PYTHON SOURCE LINES 24-25

Pass all 64 channels at once and get one value per channel.

.. GENERATED FROM PYTHON SOURCE LINES 25-40

.. code-block:: Python


    values = {
        "Higuchi FD": complexeva.hfd(eeg),
        "permutation entropy": complexeva.permutation_entropy(eeg),
        "sample entropy": complexeva.sample_entropy(eeg),
    }

    fig, axes = plt.subplots(1, 3, figsize=(9, 3.2), layout="constrained")
    for ax, (name, value) in zip(axes, values.items()):
        image, _ = mne.viz.plot_topomap(
            value, raw.info, axes=ax, cmap="viridis", vlim=(numpy.min(value), numpy.max(value)), show=False
        )
        fig.colorbar(image, ax=ax, shrink=0.7)
        ax.set_title(name)
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_eeg_batch_001.png
   :alt: Higuchi FD, permutation entropy, sample entropy
   :srcset: /auto_examples/images/sphx_glr_plot_eeg_batch_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 12.803 seconds)


.. _sphx_glr_download_auto_examples_plot_eeg_batch.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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