.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/examples/20_bandpower.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_20_bandpower.py>`
        to download the full example code.

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

.. _sphx_glr_generated_examples_20_bandpower.py:


Bandpower rolling window
========================

With a :class:`~mne_lsl.stream.StreamLSL`, we can compute the bandpower on a time
rolling window. For this example, we will look at the alpha band power, between 8 and
13 Hz.

.. GENERATED FROM PYTHON SOURCE LINES 9-32

.. code-block:: Python



    import time
    import uuid

    import numpy as np
    from matplotlib import colormaps
    from matplotlib import pyplot as plt
    from mne.io import read_raw_fif
    from mne.time_frequency import psd_array_multitaper
    from numpy.typing import NDArray
    from scipy.integrate import simpson
    from scipy.signal import periodogram, welch

    from mne_lsl.datasets import sample
    from mne_lsl.player import PlayerLSL
    from mne_lsl.stream import StreamLSL

    # dataset used in the example
    raw = read_raw_fif(sample.data_path() / "sample-ant-raw.fif", preload=False)
    raw.crop(40, 60).load_data()
    raw






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                <span class="collapse-uncollapse-caret"></span>
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    <tr class="repr-element general-913f12a3-be2f-43f0-9d57-33383c719ccb ">
        <td class="repr-section-toggle-col"></td>
        <td>Filename(s)</td>
        <td>
        
            sample-ant-raw.fif
        
        
        </td>
    </tr>

    <tr class="repr-element general-913f12a3-be2f-43f0-9d57-33383c719ccb ">
        <td class="repr-section-toggle-col"></td>
        <td>MNE object type</td>
        <td>Raw</td>
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    <tr class="repr-element general-913f12a3-be2f-43f0-9d57-33383c719ccb ">
        <td class="repr-section-toggle-col"></td>
        <td>Measurement date</td>
    
        <td>Unknown</td>
    
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    <tr class="repr-element general-913f12a3-be2f-43f0-9d57-33383c719ccb ">
        <td class="repr-section-toggle-col"></td>
        <td>Participant</td>
    
        <td>Unknown</td>
    
    </tr>
    <tr class="repr-element general-913f12a3-be2f-43f0-9d57-33383c719ccb ">
        <td class="repr-section-toggle-col"></td>
        <td>Experimenter</td>
    
        <td>mne_anonymize</td>
    
    </tr>
    







    <tr class="repr-section-header acquisition-6cb75002-468c-4348-ad91-87fa9099d65c" 
        title="Hide section"  onclick="toggleVisibility('acquisition-6cb75002-468c-4348-ad91-87fa9099d65c')">
        <th class="repr-section-toggle-col">
            <button>
            
                <span class="collapse-uncollapse-caret"></span>
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            <strong>Acquisition</strong>
        </th>
    </tr>

    <tr class="repr-element acquisition-6cb75002-468c-4348-ad91-87fa9099d65c ">
        <td class="repr-section-toggle-col"></td>
        <td>Duration</td>
        <td>00:00:20 (HH:MM:SS)</td>
    </tr>








    <tr class="repr-element acquisition-6cb75002-468c-4348-ad91-87fa9099d65c ">
        <td class="repr-section-toggle-col"></td>
        <td>Sampling frequency</td>
        <td>1024.00 Hz</td>
    </tr>


    <tr class="repr-element acquisition-6cb75002-468c-4348-ad91-87fa9099d65c ">
        <td class="repr-section-toggle-col"></td>
        <td>Time points</td>
        <td>20,481</td>
    </tr>


    







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        onclick="toggleVisibility('channels-136bb32f-5e7d-41c0-9921-7ba0093e6f13')">
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        </th>
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                63
            </button>

        
        </td>
    </tr>


    <tr class="repr-element channels-136bb32f-5e7d-41c0-9921-7ba0093e6f13 ">
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        <td>
            <button class="channel-names-btn" onclick="alert('Good EOG:\n\nvEOG, hEOG')" title="(Click to open in popup)&#13;&#13;vEOG, hEOG">
                2
            </button>

        
        </td>
    </tr>


    <tr class="repr-element channels-136bb32f-5e7d-41c0-9921-7ba0093e6f13 ">
        <td class="repr-section-toggle-col"></td>
        <td>ECG</td>
        <td>
            <button class="channel-names-btn" onclick="alert('Good ECG:\n\nECG')" title="(Click to open in popup)&#13;&#13;ECG">
                1
            </button>

        
        </td>
    </tr>


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                1
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            <button class="channel-names-btn" onclick="alert('Good Galvanic skin response:\n\nEDA')" title="(Click to open in popup)&#13;&#13;EDA">
                1
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        <td>Not available</td>
    
    </tr>
    







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        onclick="toggleVisibility('filters-8b9d80df-0e70-471b-a1fb-676b70d19279')">
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            <button>
            
                <span class="collapse-uncollapse-caret"></span>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 34-48

Preprocessing
-------------

In a real-time scenario, we would want to apply artifact rejection methods online to
estimate the bandpower on brain signals, not on artifacts. For this example, we will
only apply a bandpass filter to the data.

Estimating the bandpower
------------------------

First, we will define the function estimating the bandpower on a time window. The
bandpower will be estimated by integrating the power spectral density (PSD) on the
frequency band of interest, using the composite Simpson's rule
(:func:`scipy.integrate.simpson`).

.. GENERATED FROM PYTHON SOURCE LINES 48-113

.. code-block:: Python



    def bandpower(
        data: NDArray[np.float64],
        fs: float,
        method: str,
        band: tuple[float, float],
        relative: bool = True,
        **kwargs,
    ) -> NDArray[np.float64]:
        """Compute the bandpower of the individual channels.

        Parameters
        ----------
        data : array of shape (n_channels, n_samples)
            Data on which the the bandpower is estimated.
        fs : float
            Sampling frequency in Hz.
        method : 'periodogram' | 'welch' | 'multitaper'
            Method used to estimate the power spectral density.
        band : tuple of shape (2,)
            Frequency band of interest in Hz as 2 floats, e.g. ``(8, 13)``. The
            edges are included.
        relative : bool
            If True, the relative bandpower is returned instead of the absolute
            bandpower.
        **kwargs : dict
            Additional keyword arguments are provided to the power spectral density
            estimation function.
            * 'periodogram': scipy.signal.periodogram
            * 'welch'``: scipy.signal.welch
            * 'multitaper': mne.time_frequency.psd_array_multitaper

            The only provided arguments are the data array and the sampling
            frequency.

        Returns
        -------
        bandpower : array of shape (n_channels,)
            The bandpower of each channel.
        """
        # compute the power spectral density
        assert (
            data.ndim == 2
        ), "The provided data must be a 2D array of shape (n_channels, n_samples)."
        if method == "periodogram":
            freqs, psd = periodogram(data, fs, **kwargs)
        elif method == "welch":
            freqs, psd = welch(data, fs, **kwargs)
        elif method == "multitaper":
            psd, freqs = psd_array_multitaper(data, fs, verbose="ERROR", **kwargs)
        else:
            raise RuntimeError(f"The provided method '{method}' is not supported.")
        # compute the bandpower
        assert len(band) == 2, "The 'band' argument must be a 2-length tuple."
        assert (
            band[0] <= band[1]
        ), "The 'band' argument must be defined as (low, high) (in Hz)."
        freq_res = freqs[1] - freqs[0]
        idx_band = np.logical_and(freqs >= band[0], freqs <= band[1])
        bandpower = simpson(psd[:, idx_band], dx=freq_res)
        bandpower = bandpower / simpson(psd, dx=freq_res) if relative else bandpower
        return bandpower









.. GENERATED FROM PYTHON SOURCE LINES 114-128

Real-time estimation on a rolling window
----------------------------------------

Next, we can estimate the alpha band power on a rolling window of 4 seconds by running
an infinite loop that reads the data from the stream and computes the bandpower on the
last 4 seconds of data.

.. note::

    A chunk size of 200 samples is used to ensure stability in our documentation
    build, but in practice, a real-time application will likely publish new samples
    in smaller chunks and thus at a higher frequency. Due to the large chunk size,
    the acquisition delay of the connected stream is also increased to reduce the
    load on the CPU.

.. GENERATED FROM PYTHON SOURCE LINES 128-148

.. code-block:: Python


    source_id = uuid.uuid4().hex
    with PlayerLSL(raw, chunk_size=200, name="bandpower-example", source_id=source_id):
        stream = StreamLSL(bufsize=4, name="bandpower-example", source_id=source_id)
        stream.connect(acquisition_delay=0.1, processing_flags="all")
        stream.pick("eeg").filter(1, 30)
        stream.get_data()  # reset the number of new samples after the filter is applied

        datapoints, times = [], []
        while stream.n_new_samples < stream.n_buffer:
            time.sleep(0.1)  # wait for the buffer to be entirely filled
        while len(datapoints) != 30:
            if stream.n_new_samples == 0:
                continue  # wait for new samples
            data, ts = stream.get_data()
            bp = bandpower(data, stream.info["sfreq"], "periodogram", band=(8, 13))
            datapoints.append(bp)
            times.append(ts[-1])
        stream.disconnect()








.. GENERATED FROM PYTHON SOURCE LINES 149-155

Plot in function of time
------------------------

We can now plot the rolling-window bandpower in function of time, using the timestamps
of the last sample for each window on the X-axis. For simplicity, let's average all
channels together.

.. GENERATED FROM PYTHON SOURCE LINES 155-162

.. code-block:: Python


    f, ax = plt.subplots(1, 1, layout="constrained")
    ax.plot(times - times[0], [np.average(dp) * 100 for dp in datapoints])
    ax.set_xlabel("Time (s)")
    ax.set_ylabel("Relative α band power (%)")
    plt.show()




.. image-sg:: /generated/examples/images/sphx_glr_20_bandpower_001.png
   :alt: 20 bandpower
   :srcset: /generated/examples/images/sphx_glr_20_bandpower_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 163-168

Delay between 2 samples
-----------------------

Let's also have a look at the delay between 2 data points, i.e. the overlap between
the windows.

.. GENERATED FROM PYTHON SOURCE LINES 168-175

.. code-block:: Python


    f, ax = plt.subplots(1, 1, layout="constrained")
    timedeltas = np.diff(times - times[0]) * 1000
    ax.hist(timedeltas, bins=15)
    ax.set_xlabel("Delay between 2 samples (ms)")
    plt.show()




.. image-sg:: /generated/examples/images/sphx_glr_20_bandpower_002.png
   :alt: 20 bandpower
   :srcset: /generated/examples/images/sphx_glr_20_bandpower_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 176-189

.. note::

    Due to the low resources available on our CIs to build the documentation, some of
    those datapoints might have been computed with 2 acquisition window of delay
    instead of 1, yielding a delay between 2 samples of 2 acquisition windows instead
    of 1. In practice, with a large chunk size of 200 samples, we should get a delay
    between 2 computed time points to 200 samples, i.e. around 195.31 ms.

Compare power spectral density estimation methods
-------------------------------------------------

Let's compare both the bandpower estimation and the computation time of the different
methods to estimate the power spectral density.

.. GENERATED FROM PYTHON SOURCE LINES 189-223

.. code-block:: Python


    methods = ("periodogram", "welch", "multitaper")
    with PlayerLSL(raw, chunk_size=200, name="bandpower-example", source_id=source_id):
        stream = StreamLSL(bufsize=4, name="bandpower-example", source_id=source_id)
        stream.connect(acquisition_delay=0.1, processing_flags="all")
        stream.pick("eeg").filter(1, 30)
        stream.get_data()  # reset the number of new samples after the filter is applied

        datapoints, times = {method: [] for method in methods}, []
        while stream.n_new_samples < stream.n_buffer:
            time.sleep(0.1)  # wait for the buffer to be entirely filled
        while len(datapoints[methods[0]]) != 30:
            if stream.n_new_samples == 0:
                continue  # wait for new samples
            data, ts = stream.get_data()
            for method in methods:
                bp = bandpower(data, stream.info["sfreq"], method, band=(8, 13))
                datapoints[method].append(bp)
            times.append(ts[-1])
        stream.disconnect()

    f, ax = plt.subplots(1, 1, layout="constrained")
    for k, method in enumerate(methods):
        ax.plot(
            times - times[0],
            [np.average(dp) * 100 for dp in datapoints[method]],
            label=method,
            color=colormaps["viridis"].colors[k * 60 + 20],
        )
    ax.set_xlabel("Time (s)")
    ax.set_ylabel("Relative α band power (%)")
    ax.legend()
    plt.show()




.. image-sg:: /generated/examples/images/sphx_glr_20_bandpower_003.png
   :alt: 20 bandpower
   :srcset: /generated/examples/images/sphx_glr_20_bandpower_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 224-226

For the computation time and the overall loop execution speed, we need to run each
method on a separate loop.

.. GENERATED FROM PYTHON SOURCE LINES 226-257

.. code-block:: Python


    methods = ("periodogram", "welch", "multitaper")
    with PlayerLSL(raw, chunk_size=200, name="bandpower-example", source_id=source_id):
        stream = StreamLSL(bufsize=4, name="bandpower-example", source_id=source_id)
        stream.connect(acquisition_delay=0.1, processing_flags="all")
        stream.pick("eeg").filter(1, 30)
        stream.get_data()  # reset the number of new samples after the filter is applied

        times = {method: [] for method in methods}
        while stream.n_new_samples < stream.n_buffer:
            time.sleep(0.1)  # wait for the buffer to be entirely filled
        for k, method in enumerate(methods):
            while len(times[methods[k]]) != 30:
                if stream.n_new_samples == 0:
                    continue  # wait for new samples
                data, ts = stream.get_data()
                bp = bandpower(data, stream.info["sfreq"], method, band=(8, 13))
                times[method].append(ts[-1])
        stream.disconnect()

    timedeltas = {
        method: np.diff(times[method] - times[method][0]) * 1000 for method in methods
    }
    timedeltas_average = {method: np.average(timedeltas[method]) for method in methods}

    for method in methods:
        print(
            f"Average delay between 2 samples for {method}: "
            f"{timedeltas_average[method]:.2f} ms"
        )





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

 .. code-block:: none

    Average delay between 2 samples for periodogram: 195.31 ms
    Average delay between 2 samples for welch: 195.31 ms
    Average delay between 2 samples for multitaper: 195.31 ms




.. GENERATED FROM PYTHON SOURCE LINES 258-282

.. note::

    For this example, the average delay between 2 estimation of the bandpower is
    similar between all 3 methods because we are waiting for new samples which come in
    chunks of 200 samples, i.e. every 195.31 ms at the sampling frequency of 1024 Hz.
    The figure obtained for a chunk size of 1 sample and an acquisition delay of 1 ms
    is shown below.

    .. code-block:: python

        f, ax = plt.subplots(1, 1, layout="constrained")
        for k, method in enumerate(methods):
            ax.hist(
                timedeltas[method],
                bins=15,
                label=method,
                color=colormaps["viridis"].colors[k * 60 + 20],
            )
        ax.set_xlabel("Delay between 2 samples (ms)")
        ax.legend()
        plt.show()

.. image:: ../../_static/tutorials/bp-performance.png
    :align: center


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

   **Total running time of the script:** (0 minutes 49.017 seconds)

**Estimated memory usage:**  204 MB


.. _sphx_glr_download_generated_examples_20_bandpower.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 20_bandpower.ipynb <20_bandpower.ipynb>`

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

      :download:`Download Python source code: 20_bandpower.py <20_bandpower.py>`

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

      :download:`Download zipped: 20_bandpower.zip <20_bandpower.zip>`


.. only:: html

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

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