.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/examples/30_real_time_evoked_responses.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_generated_examples_30_real_time_evoked_responses.py>`
        to download the full example code.

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

.. _sphx_glr_generated_examples_30_real_time_evoked_responses.py:


Real-time evoked responses
==========================

With a :class:`~mne_lsl.stream.EpochsStream`, we can build a real-time evoked response
visualization. This is useful to monitor the brain activity in real-time.

.. GENERATED FROM PYTHON SOURCE LINES 8-25

.. code-block:: Python


    import uuid

    import numpy as np
    from matplotlib import pyplot as plt
    from mne import EvokedArray, combine_evoked
    from mne.io import read_raw_fif

    from mne_lsl.datasets import sample
    from mne_lsl.player import PlayerLSL
    from mne_lsl.stream import EpochsStream, StreamLSL
    from mne_lsl.utils.logs import logger

    # dataset used in the example
    fname = sample.data_path() / "mne-sample" / "sample_audvis_raw.fif"
    raw = read_raw_fif(fname, preload=False).pick(("meg", "stim")).load_data()








.. GENERATED FROM PYTHON SOURCE LINES 26-36

First, we create a mock stream with :class:`mne_lsl.player.PlayerLSL` from the sample
dataset and connect a :class:`~mne_lsl.stream.StreamLSL` to it. Then, we attach a
:class:`~mne_lsl.stream.EpochsStream` object to create epochs from the LSL stream.
The epochs will be created around the event ID ``1`` from the ``'STI 014'`` channel.

.. note::

    A ``chunk_size`` of 200 samples is used here to ensure stability and reliability
    while building the documentation on the CI. In practice, a ``chunk_size`` of 200
    samples is too large to represent a real-time application.

.. GENERATED FROM PYTHON SOURCE LINES 36-89

.. code-block:: Python


    source_id = uuid.uuid4().hex
    with PlayerLSL(
        raw, chunk_size=200, name="real-time-evoked-example", source_id=source_id
    ):
        stream = StreamLSL(bufsize=4, name="real-time-evoked-example", source_id=source_id)
        stream.connect(acquisition_delay=0.1, processing_flags="all")
        stream.info["bads"] = ["MEG 2443"]
        stream.filter(None, 40, picks="grad")
        epochs = EpochsStream(
            stream,
            bufsize=20,
            event_id=1,
            event_channels="STI 014",
            tmin=-0.2,
            tmax=0.5,
            baseline=(None, 0),
            picks="grad",
        )
        epochs.connect(acquisition_delay=0.1)

        # create figure
        if not plt.isinteractive():
            plt.ion()
        fig, ax = plt.subplots()
        plt.show()

        # start looking for epochs
        n = 0  # number of epochs
        evoked = None
        while n <= 20:
            if epochs.n_new_epochs == 0:
                continue  # nothing new to do
            logger.info("Got %s / %s new epochs.", epochs.n_new_epochs, n)
            n += epochs.n_new_epochs
            # get data and create evoked array
            data = epochs.get_data(n_epochs=epochs.n_new_epochs)
            new_evoked = EvokedArray(
                np.average(data, axis=0), epochs.info, nave=data.shape[0]
            )
            evoked = (
                new_evoked
                if evoked is None
                else combine_evoked([evoked, new_evoked], weights="nave")
            )
            ax.clear()
            evoked.plot(axes=ax, time_unit="s")  # plot on current figure
            fig.canvas.draw()
            fig.canvas.flush_events()

        # clean-up
        epochs.disconnect()
        stream.disconnect()



.. image-sg:: /generated/examples/images/sphx_glr_30_real_time_evoked_responses_001.png
   :alt: Gradiometers (203 channels)
   :srcset: /generated/examples/images/sphx_glr_30_real_time_evoked_responses_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 59.993 seconds)

**Estimated memory usage:**  606 MB


.. _sphx_glr_download_generated_examples_30_real_time_evoked_responses.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 30_real_time_evoked_responses.ipynb <30_real_time_evoked_responses.ipynb>`

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

      :download:`Download Python source code: 30_real_time_evoked_responses.py <30_real_time_evoked_responses.py>`

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

      :download:`Download zipped: 30_real_time_evoked_responses.zip <30_real_time_evoked_responses.zip>`


.. only:: html

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

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