.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/00_example-vol-src.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_00_example-vol-src.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_00_example-vol-src.py:


Volume Source Example
=====================
Estimate NCRFs for frequent and infrequent (oddball) tones.

For this tutorial, we use the auditory Brainstorm tutorial dataset :cite:`Brainstorm` that is available as a part of the Brainstorm software.

.. contents:: Contents
   :local:

.. note::
   Downloading the dataset requires answering an interactive prompt (see
   :func:`mne.datasets.brainstorm.bst_auditory.data_path`).

.. GENERATED FROM PYTHON SOURCE LINES 15-25

.. code-block:: Python

    # Authors: Proloy Das <proloy@umd.edu>
    #          Christian Brodbeck <brodbecc@mcmaster.ca>
    #
    # sphinx_gallery_thumbnail_number = 8

    import eelbrain
    import mne
    from ncrf import fit_ncrf
    import numpy as np
    import pandas as pd







.. GENERATED FROM PYTHON SOURCE LINES 26-29

Preprocessing
-------------
We broadly follow `this mne-python tutorial <https://mne.tools/stable/auto_tutorials/io/60_ctf_bst_auditory.html>`_.

.. GENERATED FROM PYTHON SOURCE LINES 29-58

.. code-block:: Python


    data_path = mne.datasets.brainstorm.bst_auditory.data_path()
    raw_fname = data_path / 'MEG' / 'bst_auditory' / 'S01_AEF_20131218_01.ds'
    raw = mne.io.read_raw_ctf(raw_fname, preload=False)

    eog_proj_fname = data_path / 'MEG' / 'bst_auditory' / 'bst_auditory-eog-proj.fif'
    eog_proj = mne.read_proj(eog_proj_fname)

    # We mark a set of bad channels that seem noisier than others. 
    raw.info['bads'] = ['MLO52-4408', 'MRT51-4408', 'MLO42-4408', 'MLO43-4408']

    # For noise reduction, a set of bad segments have been identified and stored in
    # csv files. The bad segments are later used to reject epochs that overlap with
    # them. We use pandas to read the data from the csv files.

    csv_fname = data_path / 'MEG' / 'bst_auditory' / 'events_bad_01.csv'
    annotations_df = pd.read_csv(csv_fname, header=None,
                        names=['onset', 'duration', 'id', 'label'])
    print('Events from run 1:')
    print(annotations_df)

    # Conversion from samples to times:
    onsets = annotations_df['onset'].values / raw.info['sfreq']
    durations = annotations_df['duration'].values / raw.info['sfreq']
    descriptions = annotations_df['label'].values

    annotations = mne.Annotations(onsets, durations, descriptions)
    raw.set_annotations(annotations)





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

 .. code-block:: none


    License
    -------
    This tutorial dataset (EEG and MRI data) remains a property of the MEG Lab,
    McConnell Brain Imaging Center, Montreal Neurological Institute,
    McGill University, Canada. Its use and transfer outside the Brainstorm
    tutorial, e.g. for research purposes, is prohibited without written consent
    from the MEG Lab.

    If you reference this dataset in your publications, please:

        1) acknowledge its authors: Elizabeth Bock, Esther Florin, Francois Tadel
           and Sylvain Baillet, and
        2) cite Brainstorm as indicated on the website:
           http://neuroimage.usc.edu/brainstorm

    For questions, please contact Francois Tadel (francois.tadel@mcgill.ca).
    Agree (y/[n])? Downloading file 'bst_auditory.tar.gz' from 'https://osf.io/download/5t9n8?version=1' to '/home/runner/mne_data'.

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    Untarring contents of '/home/runner/mne_data/bst_auditory.tar.gz' to '/home/runner/mne_data/MNE-brainstorm-data'
    Events from run 1:
         onset  duration  id label
    0     7625      2776   1   BAD
    1   142459       892   1   BAD
    2   216954       460   1   BAD
    3   345135      5816   1   BAD
    4   357687      1053   1   BAD
    5   409101      3736   1   BAD
    6   461110       179   1   BAD
    7   479866       426   1   BAD
    8   764914     11500   1   BAD
    9   798174      6589   1   BAD
    10  846880      5383   1   BAD
    11  858863      5136   1   BAD


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <script type="text/javascript">
        // must be `var` (not `const`) because this can get embedded multiple times on a page
    var toggleVisibility = (className) => {

        const elements = document.querySelectorAll(`.${className}`);

        elements.forEach(element => {
            if (element.classList.contains("mne-repr-section-header")) {
                return  // Don't collapse the section header row
            }
            element.classList.toggle("mne-repr-collapsed");
        });

        // trigger caret to rotate
        var sel = `.mne-repr-section-header.${className} > th.mne-repr-section-toggle > button`;
        const button = document.querySelector(sel);
        button.classList.toggle("collapsed");

        // adjust tooltip
        sel = `tr.mne-repr-section-header.${className}`;
        const secHeadRow = document.querySelector(sel);
        secHeadRow.classList.toggle("collapsed");
        secHeadRow.title = secHeadRow.title === "Hide section" ? "Show section" : "Hide section";
    }
    </script>

    <style type="text/css">
        /*
    Styles in this section apply both to the sphinx-built website docs and to notebooks
    rendered in an IDE or in Jupyter. In our web docs, styles here are complemented by
    doc/_static/styles.css and other CSS files (e.g. from the sphinx theme, sphinx-gallery,
    or bootstrap). In IDEs/Jupyter, those style files are unavailable, so only the rules in
    this file apply (plus whatever default styling the IDE applies).
    */
    .mne-repr-table {
        display: inline;  /* prevent using full container width */
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    .mne-repr-table tr.mne-repr-section-header > th {
        padding-top: 1rem;
        text-align: left;
        vertical-align: middle;
    }
    .mne-repr-section-toggle > button {
        all: unset;
        display: block;
        height: 1rem;
        width: 1rem;
    }
    .mne-repr-section-toggle > button > svg {
        height: 60%;
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    /* transition (rotation) effects on the collapser button */
    .mne-repr-section-toggle > button.collapsed > svg {
        transition: 0.1s ease-out;
        transform: rotate(-90deg);
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    .mne-repr-section-toggle > button:not(.collapsed) > svg {
        transition: 0.1s ease-out;
        transform: rotate(0deg);
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    /* hide collapsed table rows */
    .mne-repr-collapsed {
        display: none;
    }


    @layer {
        /*
        Selectors in a `@layer` will always be lower-precedence than selectors outside the
        layer. So even though e.g. `div.output_html` is present in the sphinx-rendered
        website docs, the styles here won't take effect there as long as some other rule
        somewhere in the page's CSS targets the same element.

        In IDEs or Jupyter notebooks, though, the CSS files from the sphinx theme,
        sphinx-gallery, and bootstrap are unavailable, so these styles will apply.

        Notes:

        - the selector `.accordion-body` is for MNE Reports
        - the selector `.output_html` is for VSCode's notebook interface
        - the selector `.jp-RenderedHTML` is for Jupyter notebook
        - variables starting with `--theme-` are VSCode-specific.
        - variables starting with `--jp-` are Jupyter styles, *some of which* are also
          available in VSCode. Here we try the `--theme-` variable first, then fall back to
          the `--jp-` ones.
        */
        .mne-repr-table {
            --mne-toggle-color: var(--theme-foreground, var(--jp-ui-font-color1));
            --mne-button-bg-color: var(--theme-button-background, var(--jp-info-color0, var(--jp-content-link-color)));
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            --mne-button-radius: var(--jp-border-radius, 0.25rem);
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        /* chevron position/alignment; in VSCode it looks ok without adjusting */
        .accordion-body .mne-repr-section-toggle > button,
        .jp-RenderedHTML .mne-repr-section-toggle > button {
            padding: 0 0 45% 25% !important;
        }
        /* chevron color; MNE Report doesn't have light/dark mode */
        div.output_html .mne-repr-section-toggle > button > svg > path,
        .jp-RenderedHTML .mne-repr-section-toggle > button > svg > path {
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        .accordion-body .mne-ch-names-btn,
        div.output_html .mne-ch-names-btn,
        .jp-RenderedHTML .mne-ch-names-btn {
            -webkit-border-radius: var(--mne-button-radius);
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            border-radius: var(--mne-button-radius);
            border: none;
            background-image: none;
            background-color: var(--mne-button-bg-color);
            color: var(--mne-button-fg-color);
            font-size: inherit;
            min-width: 1.5rem;
            padding: 0.25rem;
            text-align: center;
            text-decoration: none;
        }
        .accordion-body .mne-ch-names-btn:hover,
        div.output_html .mne.ch-names-btn:hover,
        .jp-RenderedHTML .mne-ch-names-btn:hover {
            background-color: var(--mne-button-hover-bg-color);
            text-decoration: underline;
        }
        .accordion-body .mne-ch-names-btn:focus-visible,
        div.output_html .mne-ch-names-btn:focus-visible,
        .jp-RenderedHTML .mne-ch-names-btn:focus-visible {
            outline: 0.1875rem solid var(--mne-button-bg-color) !important;
            outline-offset: 0.1875rem !important;
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    </style>



    <table class="table mne-repr-table">
    







    <tr class="mne-repr-section-header general-ed3b1012-f9ea-4084-bd54-0d447d582771"
         title="Hide section" 
        onclick="toggleVisibility('general-ed3b1012-f9ea-4084-bd54-0d447d582771')">
        <th class="mne-repr-section-toggle">
            <button >
                <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path d="M233.4 406.6c12.5 12.5 32.8 12.5 45.3 0l192-192c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L256 338.7 86.6 169.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3l192 192z"/></svg>
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        </th>
        <th colspan="2">
            <strong>General</strong>
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    </tr>


    <tr class="repr-element general-ed3b1012-f9ea-4084-bd54-0d447d582771 ">
        <td class="mne-repr-section-toggle"></td>
        <td>Filename(s)</td>
        <td>
        
            S01_AEF_20131218_01.meg4
        
        
        </td>
    </tr>

    <tr class="repr-element general-ed3b1012-f9ea-4084-bd54-0d447d582771 ">
        <td class="mne-repr-section-toggle"></td>
        <td>MNE object type</td>
        <td>RawCTF</td>
    </tr>
    <tr class="repr-element general-ed3b1012-f9ea-4084-bd54-0d447d582771 ">
        <td class="mne-repr-section-toggle"></td>
        <td>Measurement date</td>
    
        <td>2013-12-18 at 09:43:00 UTC</td>
    
    </tr>
    <tr class="repr-element general-ed3b1012-f9ea-4084-bd54-0d447d582771 ">
        <td class="mne-repr-section-toggle"></td>
        <td>Participant</td>
    
    
        <td>S01</td>
    
    
    </tr>
    <tr class="repr-element general-ed3b1012-f9ea-4084-bd54-0d447d582771 ">
        <td class="mne-repr-section-toggle"></td>
        <td>Experimenter</td>
    
        <td>EAB</td>
    
    </tr>
    







    <tr class="mne-repr-section-header acquisition-3370218f-913e-400a-997d-6f464f195cdc"
         title="Hide section" 
        onclick="toggleVisibility('acquisition-3370218f-913e-400a-997d-6f464f195cdc')">
        <th class="mne-repr-section-toggle">
            <button >
                <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path d="M233.4 406.6c12.5 12.5 32.8 12.5 45.3 0l192-192c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L256 338.7 86.6 169.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3l192 192z"/></svg>
            </button>
        </th>
        <th colspan="2">
            <strong>Acquisition</strong>
        </th>
    </tr>


    <tr class="repr-element acquisition-3370218f-913e-400a-997d-6f464f195cdc ">
        <td class="mne-repr-section-toggle"></td>
        <td>Duration</td>
        <td>00:06:00 (HH:MM:SS)</td>
    </tr>








    <tr class="repr-element acquisition-3370218f-913e-400a-997d-6f464f195cdc ">
        <td class="mne-repr-section-toggle"></td>
        <td>Sampling frequency</td>
        <td>2400.00 Hz</td>
    </tr>


    <tr class="repr-element acquisition-3370218f-913e-400a-997d-6f464f195cdc ">
        <td class="mne-repr-section-toggle"></td>
        <td>Time points</td>
        <td>864,000</td>
    </tr>


    







    <tr class="mne-repr-section-header channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d"
         title="Hide section" 
        onclick="toggleVisibility('channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d')">
        <th class="mne-repr-section-toggle">
            <button >
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            </button>
        </th>
        <th colspan="2">
            <strong>Channels</strong>
        </th>
    </tr>


    
    <tr class="repr-element channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d ">
        <td class="mne-repr-section-toggle"></td>
        <td>Magnetometers</td>
        <td>
            <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Good Magnetometers:\n\nMLC11-4408, MLC12-4408, MLC13-4408, MLC14-4408, MLC15-4408, MLC16-4408, MLC17-4408, MLC21-4408, MLC22-4408, MLC23-4408, MLC24-4408, MLC25-4408, MLC31-4408, MLC32-4408, MLC41-4408, MLC42-4408, MLC51-4408, MLC52-4408, MLC53-4408, MLC54-4408, MLC55-4408, MLC61-4408, MLC62-4408, MLC63-4408, MLF11-4408, MLF12-4408, MLF13-4408, MLF14-4408, MLF21-4408, MLF22-4408, MLF23-4408, MLF24-4408, MLF25-4408, MLF31-4408, MLF32-4408, MLF33-4408, MLF34-4408, MLF35-4408, MLF41-4408, MLF42-4408, MLF43-4408, MLF44-4408, MLF45-4408, MLF46-4408, MLF51-4408, MLF52-4408, MLF53-4408, MLF54-4408, MLF55-4408, MLF56-4408, MLF61-4408, MLF62-4408, MLF63-4408, MLF64-4408, MLF65-4408, MLF66-4408, MLF67-4408, MLO11-4408, MLO12-4408, MLO13-4408, MLO14-4408, MLO21-4408, MLO22-4408, MLO23-4408, MLO24-4408, MLO31-4408, MLO32-4408, MLO33-4408, MLO34-4408, MLO41-4408, MLO44-4408, MLO51-4408, MLO53-4408, MLP11-4408, MLP12-4408, MLP21-4408, MLP22-4408, MLP23-4408, MLP31-4408, MLP32-4408, MLP33-4408, MLP34-4408, MLP35-4408, MLP41-4408, MLP42-4408, MLP43-4408, MLP44-4408, MLP45-4408, MLP51-4408, MLP52-4408, MLP53-4408, MLP54-4408, MLP55-4408, MLP56-4408, MLP57-4408, MLT11-4408, MLT12-4408, MLT13-4408, MLT14-4408, MLT15-4408, MLT16-4408, MLT21-4408, MLT22-4408, MLT23-4408, MLT24-4408, MLT25-4408, MLT26-4408, MLT27-4408, MLT31-4408, MLT32-4408, MLT33-4408, MLT34-4408, MLT35-4408, MLT36-4408, MLT37-4408, MLT41-4408, MLT42-4408, MLT43-4408, MLT44-4408, MLT45-4408, MLT46-4408, MLT47-4408, MLT51-4408, MLT52-4408, MLT53-4408, MLT54-4408, MLT55-4408, MLT56-4408, MLT57-4408, MRC11-4408, MRC12-4408, MRC13-4408, MRC14-4408, MRC15-4408, MRC16-4408, MRC17-4408, MRC21-4408, MRC22-4408, MRC23-4408, MRC24-4408, MRC25-4408, MRC31-4408, MRC32-4408, MRC41-4408, MRC42-4408, MRC51-4408, MRC52-4408, MRC53-4408, MRC54-4408, MRC55-4408, MRC61-4408, MRC62-4408, MRC63-4408, MRF11-4408, MRF12-4408, MRF13-4408, MRF14-4408, MRF21-4408, MRF22-4408, MRF23-4408, MRF24-4408, MRF25-4408, MRF31-4408, MRF32-4408, MRF33-4408, MRF34-4408, MRF35-4408, MRF41-4408, MRF42-4408, MRF43-4408, MRF44-4408, MRF45-4408, MRF46-4408, MRF51-4408, MRF52-4408, MRF53-4408, MRF54-4408, MRF55-4408, MRF56-4408, MRF61-4408, MRF62-4408, MRF63-4408, MRF64-4408, MRF65-4408, MRF66-4408, MRF67-4408, MRO11-4408, MRO12-4408, MRO13-4408, MRO14-4408, MRO21-4408, MRO22-4408, MRO23-4408, MRO24-4408, MRO31-4408, MRO32-4408, MRO33-4408, MRO34-4408, MRO41-4408, MRO42-4408, MRO43-4408, MRO44-4408, MRO51-4408, MRO52-4408, MRO53-4408, MRP11-4408, MRP12-4408, MRP21-4408, MRP22-4408, MRP23-4408, MRP31-4408, MRP32-4408, MRP33-4408, MRP34-4408, MRP35-4408, MRP41-4408, MRP42-4408, MRP43-4408, MRP44-4408, MRP45-4408, MRP51-4408, MRP52-4408, MRP53-4408, MRP54-4408, MRP55-4408, MRP56-4408, MRP57-4408, MRT11-4408, MRT12-4408, MRT13-4408, MRT14-4408, MRT15-4408, MRT16-4408, MRT21-4408, MRT22-4408, MRT23-4408, MRT24-4408, MRT25-4408, MRT26-4408, MRT31-4408, MRT32-4408, MRT33-4408, MRT34-4408, MRT35-4408, MRT36-4408, MRT37-4408, MRT41-4408, MRT42-4408, MRT43-4408, MRT44-4408, MRT45-4408, MRT46-4408, MRT47-4408, MRT52-4408, MRT53-4408, MRT54-4408, MRT55-4408, MRT56-4408, MRT57-4408, MZC01-4408, MZC02-4408, MZC03-4408, MZC04-4408, MZF01-4408, MZF02-4408, MZF03-4408, MZO01-4408, MZO02-4408, MZO03-4408, MZP01-4408')" title="(Click to open in popup)&#13;&#13;MLC11-4408, MLC12-4408, MLC13-4408, MLC14-4408, MLC15-4408, MLC16-4408, MLC17-4408, MLC21-4408, MLC22-4408, MLC23-4408, MLC24-4408, MLC25-4408, MLC31-4408, MLC32-4408, MLC41-4408, MLC42-4408, MLC51-4408, MLC52-4408, MLC53-4408, MLC54-4408, MLC55-4408, MLC61-4408, MLC62-4408, MLC63-4408, MLF11-4408, MLF12-4408, MLF13-4408, MLF14-4408, MLF21-4408, MLF22-4408, MLF23-4408, MLF24-4408, MLF25-4408, MLF31-4408, MLF32-4408, MLF33-4408, MLF34-4408, MLF35-4408, MLF41-4408, MLF42-4408, MLF43-4408, MLF44-4408, MLF45-4408, MLF46-4408, MLF51-4408, MLF52-4408, MLF53-4408, MLF54-4408, MLF55-4408, MLF56-4408, MLF61-4408, MLF62-4408, MLF63-4408, MLF64-4408, MLF65-4408, MLF66-4408, MLF67-4408, MLO11-4408, MLO12-4408, MLO13-4408, MLO14-4408, MLO21-4408, MLO22-4408, MLO23-4408, MLO24-4408, MLO31-4408, MLO32-4408, MLO33-4408, MLO34-4408, MLO41-4408, MLO44-4408, MLO51-4408, MLO53-4408, MLP11-4408, MLP12-4408, MLP21-4408, MLP22-4408, MLP23-4408, MLP31-4408, MLP32-4408, MLP33-4408, MLP34-4408, MLP35-4408, MLP41-4408, MLP42-4408, MLP43-4408, MLP44-4408, MLP45-4408, MLP51-4408, MLP52-4408, MLP53-4408, MLP54-4408, MLP55-4408, MLP56-4408, MLP57-4408, MLT11-4408, MLT12-4408, MLT13-4408, MLT14-4408, MLT15-4408, MLT16-4408, MLT21-4408, MLT22-4408, MLT23-4408, MLT24-4408, MLT25-4408, MLT26-4408, MLT27-4408, MLT31-4408, MLT32-4408, MLT33-4408, MLT34-4408, MLT35-4408, MLT36-4408, MLT37-4408, MLT41-4408, MLT42-4408, MLT43-4408, MLT44-4408, MLT45-4408, MLT46-4408, MLT47-4408, MLT51-4408, MLT52-4408, MLT53-4408, MLT54-4408, MLT55-4408, MLT56-4408, MLT57-4408, MRC11-4408, MRC12-4408, MRC13-4408, MRC14-4408, MRC15-4408, MRC16-4408, MRC17-4408, MRC21-4408, MRC22-4408, MRC23-4408, MRC24-4408, MRC25-4408, MRC31-4408, MRC32-4408, MRC41-4408, MRC42-4408, MRC51-4408, MRC52-4408, MRC53-4408, MRC54-4408, MRC55-4408, MRC61-4408, MRC62-4408, MRC63-4408, MRF11-4408, MRF12-4408, MRF13-4408, MRF14-4408, MRF21-4408, MRF22-4408, MRF23-4408, MRF24-4408, MRF25-4408, MRF31-4408, MRF32-4408, MRF33-4408, MRF34-4408, MRF35-4408, MRF41-4408, MRF42-4408, MRF43-4408, MRF44-4408, MRF45-4408, MRF46-4408, MRF51-4408, MRF52-4408, MRF53-4408, MRF54-4408, MRF55-4408, MRF56-4408, MRF61-4408, MRF62-4408, MRF63-4408, MRF64-4408, MRF65-4408, MRF66-4408, MRF67-4408, MRO11-4408, MRO12-4408, MRO13-4408, MRO14-4408, MRO21-4408, MRO22-4408, MRO23-4408, MRO24-4408, MRO31-4408, MRO32-4408, MRO33-4408, MRO34-4408, MRO41-4408, MRO42-4408, MRO43-4408, MRO44-4408, MRO51-4408, MRO52-4408, MRO53-4408, MRP11-4408, MRP12-4408, MRP21-4408, MRP22-4408, MRP23-4408, MRP31-4408, MRP32-4408, MRP33-4408, MRP34-4408, MRP35-4408, MRP41-4408, MRP42-4408, MRP43-4408, MRP44-4408, MRP45-4408, MRP51-4408, MRP52-4408, MRP53-4408, MRP54-4408, MRP55-4408, MRP56-4408, MRP57-4408, MRT11-4408, MRT12-4408, MRT13-4408, MRT14-4408, MRT15-4408, MRT16-4408, MRT21-4408, MRT22-4408, MRT23-4408, MRT24-4408, MRT25-4408, MRT26-4408, MRT31-4408, MRT32-4408, MRT33-4408, MRT34-4408, MRT35-4408, MRT36-4408, MRT37-4408, MRT41-4408, MRT42-4408, MRT43-4408, MRT44-4408, MRT45-4408, MRT46-4408, MRT47-4408, MRT52-4408, MRT53-4408, MRT54-4408, MRT55-4408, MRT56-4408, MRT57-4408, MZC01-4408, MZC02-4408, MZC03-4408, MZC04-4408, MZF01-4408, MZF02-4408, MZF03-4408, MZO01-4408, MZO02-4408, MZO03-4408, MZP01-4408">
                270
            </button>

        
        
            and <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Bad Magnetometers:\n\nMLO42-4408, MLO43-4408, MLO52-4408, MRT51-4408')" title="(Click to open in popup)&#13;&#13;MLO42-4408, MLO43-4408, MLO52-4408, MRT51-4408">
                4 bad
            </button>
        
        </td>
    </tr>

    
    <tr class="repr-element channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d ">
        <td class="mne-repr-section-toggle"></td>
        <td>EEG</td>
        <td>
            <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Good EEG:\n\nCz, Pz, ECG, VEOG, HEOG')" title="(Click to open in popup)&#13;&#13;Cz, Pz, ECG, VEOG, HEOG">
                5
            </button>

        
        </td>
    </tr>

    
    <tr class="repr-element channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d ">
        <td class="mne-repr-section-toggle"></td>
        <td>misc</td>
        <td>
            <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Good misc:\n\nSCLK01-177, HADC001-4408, HADC002-4408, HADC003-4408, HDAC001-4408, HDAC002-4408, HDAC003-4408, UADC001-4408, HLC0011-4408, HLC0012-4408, HLC0013-4408, HLC0021-4408, HLC0022-4408, HLC0023-4408, HLC0031-4408, HLC0032-4408, HLC0033-4408, HLC0018-4408, HLC0028-4408, HLC0038-4408, HLC0014-4408, HLC0015-4408, HLC0016-4408, HLC0017-4408, HLC0024-4408, HLC0025-4408, HLC0026-4408, HLC0027-4408, HLC0034-4408, HLC0035-4408, HLC0036-4408, HLC0037-4408')" title="(Click to open in popup)&#13;&#13;SCLK01-177, HADC001-4408, HADC002-4408, HADC003-4408, HDAC001-4408, HDAC002-4408, HDAC003-4408, UADC001-4408, HLC0011-4408, HLC0012-4408, HLC0013-4408, HLC0021-4408, HLC0022-4408, HLC0023-4408, HLC0031-4408, HLC0032-4408, HLC0033-4408, HLC0018-4408, HLC0028-4408, HLC0038-4408, HLC0014-4408, HLC0015-4408, HLC0016-4408, HLC0017-4408, HLC0024-4408, HLC0025-4408, HLC0026-4408, HLC0027-4408, HLC0034-4408, HLC0035-4408, HLC0036-4408, HLC0037-4408">
                32
            </button>

        
        </td>
    </tr>

    
    <tr class="repr-element channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d ">
        <td class="mne-repr-section-toggle"></td>
        <td>Reference Magnetometers</td>
        <td>
            <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Good Reference Magnetometers:\n\nBG1-4408, BG2-4408, BG3-4408, BP1-4408, BP2-4408, BP3-4408, BR1-4408, BR2-4408, BR3-4408, G11-4408, G12-4408, G13-4408, G22-4408, G23-4408, P11-4408, P12-4408, P13-4408, P22-4408, P23-4408, Q11-4408, Q12-4408, Q22-4408, Q23-4408, R12-4408, R13-4408, R22-4408')" title="(Click to open in popup)&#13;&#13;BG1-4408, BG2-4408, BG3-4408, BP1-4408, BP2-4408, BP3-4408, BR1-4408, BR2-4408, BR3-4408, G11-4408, G12-4408, G13-4408, G22-4408, G23-4408, P11-4408, P12-4408, P13-4408, P22-4408, P23-4408, Q11-4408, Q12-4408, Q22-4408, Q23-4408, R12-4408, R13-4408, R22-4408">
                26
            </button>

        
        </td>
    </tr>

    
    <tr class="repr-element channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d ">
        <td class="mne-repr-section-toggle"></td>
        <td>Stimulus</td>
        <td>
            <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Good Stimulus:\n\nUDIO001, UPPT001, UTRG001')" title="(Click to open in popup)&#13;&#13;UDIO001, UPPT001, UTRG001">
                3
            </button>

        
        </td>
    </tr>


    <tr class="repr-element channels-5ffb2886-dd1f-4e9d-8e72-a359c71b981d ">
        <td class="mne-repr-section-toggle"></td>
        <td>Head & sensor digitization</td>
    
        <td>263 points</td>
    
    </tr>
    







    <tr class="mne-repr-section-header filters-c9b2ce2e-bd75-4fde-abf8-27b6b83e0ea6"
         title="Hide section" 
        onclick="toggleVisibility('filters-c9b2ce2e-bd75-4fde-abf8-27b6b83e0ea6')">
        <th class="mne-repr-section-toggle">
            <button >
                <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path d="M233.4 406.6c12.5 12.5 32.8 12.5 45.3 0l192-192c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L256 338.7 86.6 169.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3l192 192z"/></svg>
            </button>
        </th>
        <th colspan="2">
            <strong>Filters</strong>
        </th>
    </tr>


    <tr class="repr-element filters-c9b2ce2e-bd75-4fde-abf8-27b6b83e0ea6 ">
        <td class="mne-repr-section-toggle"></td>
        <td>Highpass</td>
        <td>0.00 Hz</td>
    </tr>


    <tr class="repr-element filters-c9b2ce2e-bd75-4fde-abf8-27b6b83e0ea6 ">
        <td class="mne-repr-section-toggle"></td>
        <td>Lowpass</td>
        <td>1200.00 Hz</td>
    </tr>


    </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-60

Event preprocessing: In this dataset, triggers can be adjusted by using a recording of the audio that was presented.

.. GENERATED FROM PYTHON SOURCE LINES 60-88

.. code-block:: Python


    # Events are the presentation times of the audio stimuli: UPPT001
    event_fname = data_path / 'MEG' / 'bst_auditory' / 'S01_AEF_20131218_01-eve.fif'
    events = mne.find_events(raw, stim_channel='UPPT001')

    # The event timing is adjusted by comparing the trigger times on detected sound onsets on channel UADC001-4408
    sound_data = raw[raw.ch_names.index('UADC001-4408')][0][0]
    onsets = np.where(np.abs(sound_data) > 2. * np.std(sound_data))[0]
    min_diff = int(0.5 * raw.info['sfreq'])
    diffs = np.concatenate([[min_diff + 1], np.diff(onsets)])
    onsets = onsets[diffs > min_diff]
    assert len(onsets) == len(events)
    diffs = 1000. * (events[:, 0] - onsets) / raw.info['sfreq']
    print(f'Trigger delay removed (μ ± σ): {diffs.mean():.1f} ± {diffs.std():.1f} ms')

    # Since event onsets are stored in samples
    event_sfreq = raw.info['sfreq']

    # DataFrame for sound events
    sound_events = pd.DataFrame({
        'onset': onsets,
        'time': onsets / event_sfreq,
        'duration': np.ones(len(onsets)),
        'id': events[:, 2],
        'label': pd.Categorical.from_codes(events[:, 2], ['', 'frequent', 'infrequent']),
    })
    sound_events





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

 .. code-block:: none

    Trigger delay removed (μ ± σ): -13.9 ± 0.3 ms


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>onset</th>
          <th>time</th>
          <th>duration</th>
          <th>id</th>
          <th>label</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>13628</td>
          <td>5.678333</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>1</th>
          <td>16961</td>
          <td>7.067083</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>2</th>
          <td>20925</td>
          <td>8.718750</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>3</th>
          <td>24203</td>
          <td>10.084583</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>4</th>
          <td>28159</td>
          <td>11.732917</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>235</th>
          <td>737274</td>
          <td>307.197500</td>
          <td>1.0</td>
          <td>2</td>
          <td>infrequent</td>
        </tr>
        <tr>
          <th>236</th>
          <td>740140</td>
          <td>308.391667</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>237</th>
          <td>744178</td>
          <td>310.074167</td>
          <td>1.0</td>
          <td>2</td>
          <td>infrequent</td>
        </tr>
        <tr>
          <th>238</th>
          <td>746839</td>
          <td>311.182917</td>
          <td>1.0</td>
          <td>1</td>
          <td>frequent</td>
        </tr>
        <tr>
          <th>239</th>
          <td>749740</td>
          <td>312.391667</td>
          <td>1.0</td>
          <td>2</td>
          <td>infrequent</td>
        </tr>
      </tbody>
    </table>
    <p>240 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 89-92

Visualize the event related fields (ERFs) for the two types of stimuli
(frequent and infrequent beeps). This is just to get a quick impression of
the data, and to check if the event timing is correct.

.. GENERATED FROM PYTHON SOURCE LINES 92-104

.. code-block:: Python


    events[:, 0] = onsets  # Adjust event onsets
    event_id = dict(frequent=1, infrequent=2)
    reject = dict(mag=4e-12)
    epochs = mne.Epochs(raw.copy().load_data().filter(1, 25.0), events, event_id, reject=reject, picks='mag', reject_by_annotation=True, preload=True)
    epochs.add_proj(eog_proj)  # Add EOG projectors to epochs
    epochs.apply_proj()  # Apply projectors to epochs
    evoked_ndvar = [eelbrain.load.mne.epochs_ndvar(epochs[name], data='mag', name=name).mean('case')
                    for name in ('frequent', 'infrequent')]
    evoked_ndvar.append((evoked_ndvar[1] - evoked_ndvar[0]).copy('contrast'))
    p = eelbrain.plot.TopoButterfly(evoked_ndvar, t=.190, ylabel=False, vmax=300e-15)




.. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_001.png
   :alt: 00 example vol src
   :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 105-106

Preprocess MEG Data: low pass filtering, power line attenuation, downsampling, etc.

.. GENERATED FROM PYTHON SOURCE LINES 106-126

.. code-block:: Python


    raw.add_proj(eog_proj).apply_proj()  # Add EOG projectors to raw data
    # Notch filter for power line artifact
    raw.load_data()
    raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')

    # Band pass filtering 1-20 Hz
    raw.filter(1.0, 20.0, fir_design='firwin')

    # Crop relative to sound events
    tmin = sound_events['time'].min() - 0.2
    tmax = sound_events['time'].max() + 1.0
    raw.crop(tmin, tmax)

    # Resample to 100 Hz
    raw.resample(100, npad="auto")

    # `raw` remembers the original time axis, consistent with events
    raw.first_time





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

 .. code-block:: none


    5.48



.. GENERATED FROM PYTHON SOURCE LINES 127-128

Convert MEG data to :class:`eelbrain.NDVar` for NCRF estimation.

.. GENERATED FROM PYTHON SOURCE LINES 128-133

.. code-block:: Python


    meg = eelbrain.load.mne.raw_ndvar(raw)
    # Time axis is preserved (t_start, t_step, n_samples)
    meg.time





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

 .. code-block:: none


    UTS(5.48, 0.01, 30791)



.. GENERATED FROM PYTHON SOURCE LINES 134-149

Continuous stimulus variable construction
-----------------------------------------
After loading and preprocessing the raw data, we will construct predictor
variables for this particular experiment.
Here, we construct predictor variables by placing an impulse at every
stimulus tone onset. 
Note that the predictor variable and MEG response should have the same time
axis. 

In this example, we use two different predictor variables:
For the common response to any tone, we put impulses at the presentation
times of both the audio stimuli (i.e., frequent and infrequent beeps).
To distinguish infrequent from frequent beeps (i.e., contrast), we assign 1
and -1 impulses to infrequent and frequent beeps, respectively (contrast
coding).

.. GENERATED FROM PYTHON SOURCE LINES 149-165

.. code-block:: Python


    # Create an all-zero NDVar with time axis matching the MEG data 
    stim1 = eelbrain.NDVar.zeros(meg.time, 'common')
    # Set values at time points for any sound to 1.
    stim1[sound_events['time'].values] = 1.  # [Common]

    # To contrast infrequent from frequent beeps, we assign 1 and -1 impulses,
    # respectively
    stim2 = stim1.copy('contrast')
    frequent_index = sound_events['label'] == 'frequent'
    stim2[sound_events['time'].values[frequent_index]] = -1  # [Contrast]

    # Visualize the predictors
    p = eelbrain.plot.UTS([stim1, stim2], color='black', stem=True, frame='none',
                          w=10, h=2.5, legend=False)




.. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_002.png
   :alt: common, contrast
   :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 166-169

Noise covariance estimation
---------------------------
Here we estimate the noise covariance from empty room data.

.. GENERATED FROM PYTHON SOURCE LINES 169-186

.. code-block:: Python


    noise_path = data_path / 'MEG' / 'bst_auditory' / 'S01_Noise_20131218_01.ds'
    raw_empty_room = mne.io.read_raw_ctf(noise_path, preload=True)

    # Apply the same pre-processing steps to empty room data
    raw_empty_room.add_proj(eog_proj)

    raw_empty_room.notch_filter(np.arange(60, 181, 60), fir_design='firwin')

    raw_empty_room.filter(1.0, 20.0, fir_design='firwin')

    raw_empty_room.resample(100, npad="auto")

    # Compute the noise covariance matrix
    noise_cov = mne.compute_raw_covariance(raw_empty_room, tmin=0, tmax=None,
                                           method='shrunk', rank=None)








.. GENERATED FROM PYTHON SOURCE LINES 187-190

Forward model (aka lead-field matrix)
-------------------------------------
Create a volume source space based on a 10 mm voxel grid.

.. GENERATED FROM PYTHON SOURCE LINES 190-223

.. code-block:: Python


    # The paths to FreeSurfer reconstructions
    subjects_dir = data_path / 'subjects'
    subject = 'bst_auditory'

    # The transformation file obtained by coregistration
    trans = data_path / 'MEG' / 'bst_auditory' / 'bst_auditory-trans.fif'

    # Uncomment for visualization
    # mne.viz.plot_alignment(raw.info, trans, subject=subject, dig=True,
    #                        meg=['helmet', 'sensors'], subjects_dir=subjects_dir,
    #                        surfaces='head')

    # Cache/reload the source space for faster execution
    srcfile = subjects_dir / 'bst_auditory' / 'bem' / 'bst_auditory-vol-10-src.fif'
    if srcfile.is_file():
        src = mne.read_source_spaces(srcfile)
    else:
        surface = subjects_dir / subject / "bem" / "inner_skull.surf"
        src = mne.setup_volume_source_space(subject,
                                            subjects_dir=subjects_dir,
                                            surface=surface,
                                            pos=10.0,
                                            add_interpolator=True)
        mne.write_source_spaces(srcfile, src, overwrite=True)  # needed for smoothing

    # Visualize the source space
    fig = mne.viz.plot_bem(subject,
                           subjects_dir,
                           'coronal',
                           brain_surfaces='white',
                           src=src)




.. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_003.png
   :alt: 00 example vol src
   :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 224-225

Compute the forward solution:

.. GENERATED FROM PYTHON SOURCE LINES 225-243

.. code-block:: Python


    fwdfile = subjects_dir / 'bst_auditory' / 'bem' / 'bst_auditory-vol-10-fwd.fif'
    if fwdfile.is_file():
        fwd = mne.read_forward_solution(fwdfile)
    else:
        conductivity = (0.3,)  # for single layer
        # conductivity = (0.3, 0.006, 0.3)  # for three layers
        model = mne.make_bem_model(subject=subject, ico=4,
                                   conductivity=conductivity,
                                   subjects_dir=subjects_dir)
        bem = mne.make_bem_solution(model)

        fwd = mne.make_forward_solution(raw.info, trans=trans, src=src, bem=bem,
                                        meg=True, eeg=False, mindist=5.0, n_jobs=2)
        mne.write_forward_solution(fwdfile, fwd)

    fwd





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

 .. code-block:: none

    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    1.1s finished
    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.5s finished
    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.5s finished
    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.2s finished
    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.1s finished


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        const elements = document.querySelectorAll(`.${className}`);

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        // trigger caret to rotate
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        secHeadRow.classList.toggle("collapsed");
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    </script>

    <style type="text/css">
        /*
    Styles in this section apply both to the sphinx-built website docs and to notebooks
    rendered in an IDE or in Jupyter. In our web docs, styles here are complemented by
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        Selectors in a `@layer` will always be lower-precedence than selectors outside the
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        somewhere in the page's CSS targets the same element.

        In IDEs or Jupyter notebooks, though, the CSS files from the sphinx theme,
        sphinx-gallery, and bootstrap are unavailable, so these styles will apply.

        Notes:

        - the selector `.accordion-body` is for MNE Reports
        - the selector `.output_html` is for VSCode's notebook interface
        - the selector `.jp-RenderedHTML` is for Jupyter notebook
        - variables starting with `--theme-` are VSCode-specific.
        - variables starting with `--jp-` are Jupyter styles, *some of which* are also
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          the `--jp-` ones.
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        .mne-repr-table {
            --mne-toggle-color: var(--theme-foreground, var(--jp-ui-font-color1));
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        .accordion-body .mne-ch-names-btn,
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            background-color: var(--mne-button-bg-color);
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            font-size: inherit;
            min-width: 1.5rem;
            padding: 0.25rem;
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            text-decoration: none;
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        .accordion-body .mne-ch-names-btn:hover,
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        .accordion-body .mne-ch-names-btn:focus-visible,
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MRT25-4408, MRT26-4408, MRT31-4408, MRT32-4408, MRT33-4408, MRT34-4408, MRT35-4408, MRT36-4408, MRT37-4408, MRT41-4408, MRT42-4408, MRT43-4408, MRT44-4408, MRT45-4408, MRT46-4408, MRT47-4408, MRT52-4408, MRT53-4408, MRT54-4408, MRT55-4408, MRT56-4408, MRT57-4408, MZC01-4408, MZC02-4408, MZC03-4408, MZC04-4408, MZF01-4408, MZF02-4408, MZF03-4408, MZO01-4408, MZO02-4408, MZO03-4408, MZP01-4408')" title="(Click to open in popup)&#13;&#13;MLC11-4408, MLC12-4408, MLC13-4408, MLC14-4408, MLC15-4408, MLC16-4408, MLC17-4408, MLC21-4408, MLC22-4408, MLC23-4408, MLC24-4408, MLC25-4408, MLC31-4408, MLC32-4408, MLC41-4408, MLC42-4408, MLC51-4408, MLC52-4408, MLC53-4408, MLC54-4408, MLC55-4408, MLC61-4408, MLC62-4408, MLC63-4408, MLF11-4408, MLF12-4408, MLF13-4408, MLF14-4408, MLF21-4408, MLF22-4408, MLF23-4408, MLF24-4408, MLF25-4408, MLF31-4408, MLF32-4408, MLF33-4408, MLF34-4408, MLF35-4408, MLF41-4408, MLF42-4408, MLF43-4408, MLF44-4408, MLF45-4408, MLF46-4408, MLF51-4408, MLF52-4408, MLF53-4408, MLF54-4408, MLF55-4408, MLF56-4408, MLF61-4408, MLF62-4408, MLF63-4408, MLF64-4408, MLF65-4408, MLF66-4408, MLF67-4408, MLO11-4408, MLO12-4408, MLO13-4408, MLO14-4408, MLO21-4408, MLO22-4408, MLO23-4408, MLO24-4408, MLO31-4408, MLO32-4408, MLO33-4408, MLO34-4408, MLO41-4408, MLO44-4408, MLO51-4408, MLO53-4408, MLP11-4408, MLP12-4408, MLP21-4408, MLP22-4408, MLP23-4408, MLP31-4408, MLP32-4408, MLP33-4408, MLP34-4408, MLP35-4408, MLP41-4408, MLP42-4408, MLP43-4408, MLP44-4408, MLP45-4408, MLP51-4408, MLP52-4408, MLP53-4408, MLP54-4408, MLP55-4408, MLP56-4408, MLP57-4408, MLT11-4408, MLT12-4408, MLT13-4408, MLT14-4408, MLT15-4408, MLT16-4408, MLT21-4408, MLT22-4408, MLT23-4408, MLT24-4408, MLT25-4408, MLT26-4408, MLT27-4408, MLT31-4408, MLT32-4408, MLT33-4408, MLT34-4408, MLT35-4408, MLT36-4408, MLT37-4408, MLT41-4408, MLT42-4408, MLT43-4408, MLT44-4408, MLT45-4408, MLT46-4408, MLT47-4408, MLT51-4408, MLT52-4408, MLT53-4408, MLT54-4408, MLT55-4408, MLT56-4408, MLT57-4408, MRC11-4408, MRC12-4408, MRC13-4408, MRC14-4408, MRC15-4408, MRC16-4408, MRC17-4408, MRC21-4408, MRC22-4408, MRC23-4408, MRC24-4408, MRC25-4408, MRC31-4408, MRC32-4408, MRC41-4408, MRC42-4408, MRC51-4408, MRC52-4408, MRC53-4408, MRC54-4408, MRC55-4408, MRC61-4408, MRC62-4408, MRC63-4408, MRF11-4408, MRF12-4408, MRF13-4408, MRF14-4408, MRF21-4408, MRF22-4408, MRF23-4408, MRF24-4408, MRF25-4408, MRF31-4408, MRF32-4408, MRF33-4408, MRF34-4408, MRF35-4408, MRF41-4408, MRF42-4408, MRF43-4408, MRF44-4408, MRF45-4408, MRF46-4408, MRF51-4408, MRF52-4408, MRF53-4408, MRF54-4408, MRF55-4408, MRF56-4408, MRF61-4408, MRF62-4408, MRF63-4408, MRF64-4408, MRF65-4408, MRF66-4408, MRF67-4408, MRO11-4408, MRO12-4408, MRO13-4408, MRO14-4408, MRO21-4408, MRO22-4408, MRO23-4408, MRO24-4408, MRO31-4408, MRO32-4408, MRO33-4408, MRO34-4408, MRO41-4408, MRO42-4408, MRO43-4408, MRO44-4408, MRO51-4408, MRO52-4408, MRO53-4408, MRP11-4408, MRP12-4408, MRP21-4408, MRP22-4408, MRP23-4408, MRP31-4408, MRP32-4408, MRP33-4408, MRP34-4408, MRP35-4408, MRP41-4408, MRP42-4408, MRP43-4408, MRP44-4408, MRP45-4408, MRP51-4408, MRP52-4408, MRP53-4408, MRP54-4408, MRP55-4408, MRP56-4408, MRP57-4408, MRT11-4408, MRT12-4408, MRT13-4408, MRT14-4408, MRT15-4408, MRT16-4408, MRT21-4408, MRT22-4408, MRT23-4408, MRT24-4408, MRT25-4408, MRT26-4408, MRT31-4408, MRT32-4408, MRT33-4408, MRT34-4408, MRT35-4408, MRT36-4408, MRT37-4408, MRT41-4408, MRT42-4408, MRT43-4408, MRT44-4408, MRT45-4408, MRT46-4408, MRT47-4408, MRT52-4408, MRT53-4408, MRT54-4408, MRT55-4408, MRT56-4408, MRT57-4408, MZC01-4408, MZC02-4408, MZC03-4408, MZC04-4408, MZF01-4408, MZF02-4408, MZF03-4408, MZO01-4408, MZO02-4408, MZO03-4408, MZP01-4408">
                270
            </button>

        
        
            and <button class="mne-ch-names-btn sd-sphinx-override sd-btn sd-btn-info sd-text-wrap sd-shadow-sm" onclick="alert('Bad Magnetometers:\n\nMLO42-4408, MLO43-4408, MLO52-4408, MRT51-4408')" title="(Click to open in popup)&#13;&#13;MLO42-4408, MLO43-4408, MLO52-4408, MRT51-4408">
                4 bad
            </button>
        
        </td>
    </tr>
    

        <tr class="repr-element forward-e8e38cdd-3763-4a36-97fe-cb84c47fa1a3 ">
            <td class="mne-repr-section-toggle"></td>
            <td>Source space</td>
            <td>Volume with 1653 grid points</td>
        </tr>
        <tr class="repr-element forward-e8e38cdd-3763-4a36-97fe-cb84c47fa1a3 ">
            <td class="mne-repr-section-toggle"></td>
            <td>Source orientation</td>
            <td>Free</td>
        </tr>
    </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 244-245

Extract the leadfield matrix as :class:`eelbrain.NDVar`

.. GENERATED FROM PYTHON SOURCE LINES 245-249

.. code-block:: Python

    lf = eelbrain.load.mne.forward_operator(fwd, src='vol-10',
                                            subjects_dir=subjects_dir,
                                            parc='aparc+aseg')








.. GENERATED FROM PYTHON SOURCE LINES 250-266

NCRF estimation
---------------
Now we have all the required data to estimate NCRFs.

.. note::
   This example uses simplified settings to speed up estimation:

   1) For this example, we use a fixed regularization parameter (``mu``).
   For a real experiment, the optimal ``mu`` can be determined by
   cross-validation (set ``mu='auto'``, which is the default).
   The optimal ``mu`` will then be stored in ``model.mu``
   (this is how the ``mu`` used here was determined).

   2) The example forces the estimation to stop after fewer iterations than
   is recommended (``n_iter``). For stable models, we recommend to use the
   default setting (``n_iter=10``).

.. GENERATED FROM PYTHON SOURCE LINES 266-280

.. code-block:: Python


    # To speed up the example, we cache the NCRF
    ncrf_file = data_path / 'MEG' / 'bst_auditory' / 'oddball_ncrf_vol.pickle'
    if ncrf_file.exists():
        model = eelbrain.load.unpickle(ncrf_file)
    else:
        model = fit_ncrf(
            meg, [stim1, stim2], lf, noise_cov, tstart=0, tstop=0.5,
            mu=4.958456130470556e-06, n_iter=5,
            # mu='auto', n_iter=5,
        )
        eelbrain.save.pickle(model, ncrf_file)
    model





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

 .. code-block:: none


    <[free orientation] cTRFs estimator on <VolumeSourceSpace [1653], 'bst_auditory', 'vol-10', parc=aparc+aseg>>



.. GENERATED FROM PYTHON SOURCE LINES 281-285

The learned kernel/filter (the NCRF) can be accessed as an attribute of the
``model``.
NCRFs are stored as :class:`eelbrain.NDVar`. Here, the two NCRFs correspond
to the two different predictor variables:

.. GENERATED FROM PYTHON SOURCE LINES 285-288

.. code-block:: Python


    model.h





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

 .. code-block:: none


    [<NDVar 'common': 1653 source, 3 space, 51 time>, <NDVar 'contrast': 1653 source, 3 space, 51 time>]



.. GENERATED FROM PYTHON SOURCE LINES 289-298

Visualization
-------------
A butterfly plot shows weights in all sources over time.
This is good for forming a quick impression of important time lags,
or peaks in the response:

.. note::
   Since the estimates are sparse over cortical locations, smoothing the NCRFs
   over sources makes the visualization more intuitive.

.. GENERATED FROM PYTHON SOURCE LINES 298-303

.. code-block:: Python


    hs_orig = [h.smooth('source', 0.01, 'gaussian') for h in model.h]
    p = eelbrain.plot.Butterfly([h.norm('space') for h in hs_orig],
                                axtitle=['Common', 'Contrast'], rows=1)




.. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_004.png
   :alt: Common, Contrast
   :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 304-312

We can visualize anatomical locations of the peaks with 2D projections of
NCRFs using glass-brain plots.
The function :class:`eelbrain.plot.GlassBrain`: visualizes a single time
point in that fashion.
For brain activations to align with a schematic brain overlay,
the plotted image should be in MNI coordinate space.
Hence, we will first morph the NCRFs to the `fsaverage` brain,
which is in MNI space.

.. GENERATED FROM PYTHON SOURCE LINES 312-334

.. code-block:: Python


    mne.datasets.fetch_fsaverage(subjects_dir)
    fname_src_fsaverage = subjects_dir / "fsaverage" / "bem" / "fsaverage-vol-5-src.fif"
    src_fs = mne.read_source_spaces(fname_src_fsaverage)
    morph = mne.compute_source_morph(
        fwd["src"],
        subject_from=subject,
        subjects_dir=subjects_dir,
        niter_affine=[10, 10, 5],
        niter_sdr=[10, 10, 5],  # just for speed
        src_to=src_fs,
        verbose=True,
    )
    def morph_to_fsaverage(h):
        data = h.get_data(('source', 'space', 'time'))
        time = h.get_dim('time')
        stc = mne.VolVectorSourceEstimate(data, [fwd['src'][0]['vertno']],
                                          time.tmin, time.tstep, subject)
        stc_fsaverage = morph.apply(stc)
        return eelbrain.load.mne.stc_ndvar(stc_fsaverage, 'fsaverage', 'vol-5')
    hs = [morph_to_fsaverage(h) for h in hs_orig]





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

 .. code-block:: none

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    [2026-06-13 00:48:29][dipy] INFO: ➞ Running center_of_mass step from affine registration...
    [2026-06-13 00:48:29][dipy] INFO: ➞ Running translation step from affine registration...
    [2026-06-13 00:48:30][dipy] INFO: ➞ Running rigid step from affine registration...
    [2026-06-13 00:48:30][dipy] INFO: ➞ Running affine step from affine registration...
    [2026-06-13 00:48:31][dipy] INFO: Creating scale space from the moving image. Levels: 3. Sigma factor: 0.200000.
    [2026-06-13 00:48:31][dipy] INFO: Creating scale space from the static image. Levels: 3. Sigma factor: 0.200000.
    [2026-06-13 00:48:31][dipy] INFO: Optimizing level 2
    [2026-06-13 00:48:31][dipy] INFO: Optimizing level 1
    [2026-06-13 00:48:31][dipy] INFO: Optimizing level 0

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.. GENERATED FROM PYTHON SOURCE LINES 335-339

Now, the following code plots the anatomical localization.
First, we locate the sources that are involved in the prominent early
peaks in the Common stimulus code: P50: 50ms, N100: 95ms, P200: 170ms.
They correspond to auditory response in both conditions.

.. GENERATED FROM PYTHON SOURCE LINES 339-352

.. code-block:: Python


    times = (0.05, 0.095, 0.170,)
    vmax = 5e-11
    # vmax = hs[0].norm('space').max()  # alternative: vmax based on data
    bf = eelbrain.plot.Butterfly(hs[0].norm('space'), axtitle='common', h=2,
                                 vmax=vmax, ylabel='Amplitude')
    for time in times:
        bf.add_vline(time, color='k', linestyle='--')
    bs = [eelbrain.plot.GlassBrain(
            hs[0].sub(time=time), vmax=vmax, display_mode='lr', 
            title=f"common-[{time*1000:.0f} ms]",
          ) for time in times]




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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_005.png
         :alt: 00 example vol src
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_006.png
         :alt: common-[50 ms]
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_006.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_007.png
         :alt: common-[95 ms]
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_007.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_008.png
         :alt: common-[170 ms]
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_008.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 353-359

Next, we do the same with NCRFs to the `Contrast` predictor. Note that the
peaks in the contrast condition are later than those in the common condition,
and emphasizes known temporal dynamics of mismatch negativity (MMN) response.
We locate the sources that are involved in the prominent MMN peak around
190ms. Note that the dircetion of activation is opposite to that of early
peaks in the common condition.

.. GENERATED FROM PYTHON SOURCE LINES 359-371

.. code-block:: Python


    times = (0.190,)
    bf = eelbrain.plot.Butterfly(hs[1].norm('space'), axtitle='contrast',
                                 h=2, vmax=vmax, ylabel='Amplitude')
    for time in times:
        bf.add_vline(time, color='k', linestyle='--')

    bs = [eelbrain.plot.GlassBrain(
            hs[1].sub(time=time), vmax=vmax, display_mode='lr', 
            title=f"contrast-[{time*1000:.0f} ms]",
          ) for time in times]




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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_009.png
         :alt: 00 example vol src
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_009.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_010.png
         :alt: contrast-[190 ms]
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_010.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 372-378

Finally, we can reconstruct the response to frequent and infrequent stimuli
as :math:`[Common - Contrast]` amd :math:`[Common + Contrast]` respectively.
Here, we locate the sources that are involved in the prominent peaks around
450ms, which are stronger for infrequent stimuli, to the left motor cortex.
This aligns with the expected neural response to the motor task: the subject
presses a button when detecting a deviant with the right index finger.

.. GENERATED FROM PYTHON SOURCE LINES 378-405

.. code-block:: Python

    vmax = 7e-11
    times = (0.45,)

    # Frequent
    h = hs[0] - hs[1]
    bf = eelbrain.plot.Butterfly(h.norm('space'), h=2, vmax=vmax,
                                 ylabel='Amplitude', frame=None, title='frequent')
    for time in times:
        bf.add_vline(time, color='k', linestyle='--')

    bs = [eelbrain.plot.GlassBrain(
            h.sub(time=time), vmax=vmax, display_mode='lr',
            title=f"frequent-[{time*1000:.0f} ms]",
          ) for time in times]

    # Infrequent
    h = hs[0] + hs[1]
    bf = eelbrain.plot.Butterfly(h.norm('space'), title='infrequent',
                                 h=2, vmax=vmax, frame=None, ylabel='Amplitude')
    for time in times:
        bf.add_vline(time, color='k', linestyle='--')

    bs = [eelbrain.plot.GlassBrain(
            h.sub(time=time), vmax=vmax, display_mode='lr',
            title=f"Infrequent-[{time*1000:.0f} ms]",
          ) for time in times]




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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_011.png
         :alt: frequent
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_011.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_012.png
         :alt: frequent-[450 ms]
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_012.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_013.png
         :alt: infrequent
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_013.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_00_example-vol-src_014.png
         :alt: Infrequent-[450 ms]
         :srcset: /auto_examples/images/sphx_glr_00_example-vol-src_014.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 406-408

In an interactive iPython session, we can also use interactive time-linked
plots with `eelbrain.plot.GlassBrain.butterfly`:

.. GENERATED FROM PYTHON SOURCE LINES 408-410

.. code-block:: Python


    # brain, butterfly = eelbrain.plot.GlassBrain.butterfly(h)








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

   **Total running time of the script:** (3 minutes 54.384 seconds)


.. _sphx_glr_download_auto_examples_00_example-vol-src.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 00_example-vol-src.ipynb <00_example-vol-src.ipynb>`

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

      :download:`Download Python source code: 00_example-vol-src.py <00_example-vol-src.py>`

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

      :download:`Download zipped: 00_example-vol-src.zip <00_example-vol-src.zip>`


.. only:: html

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

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