<!DOCTYPE html>

<html xmlns="http://www.w3.org/1999/xhtml">
  <head>
    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
    <title>mne.find_events &#8212; MNE 0.15 documentation</title>
    <link rel="stylesheet" href="../_static/bootstrap-sphinx.css" type="text/css" />
    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
    <link rel="stylesheet" href="../_static/gallery.css" type="text/css" />
    <script type="text/javascript">
      var DOCUMENTATION_OPTIONS = {
        URL_ROOT:    '../',
        VERSION:     '0.15',
        COLLAPSE_INDEX: false,
        FILE_SUFFIX: '.html',
        HAS_SOURCE:  true,
        SOURCELINK_SUFFIX: '.txt'
      };
    </script>
    <script type="text/javascript" src="../_static/jquery.js"></script>
    <script type="text/javascript" src="../_static/underscore.js"></script>
    <script type="text/javascript" src="../_static/doctools.js"></script>
    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
    <script type="text/javascript" src="../_static/js/jquery-1.11.0.min.js"></script>
    <script type="text/javascript" src="../_static/js/jquery-fix.js"></script>
    <script type="text/javascript" src="../_static/bootstrap-3.3.7/js/bootstrap.min.js"></script>
    <script type="text/javascript" src="../_static/bootstrap-sphinx.js"></script>
    <link rel="shortcut icon" href="../_static/favicon.ico"/>
    <link rel="index" title="Index" href="../genindex.html" />
    <link rel="search" title="Search" href="../search.html" />

    <script type="text/javascript" src="../_static/copybutton.js"></script>


    <script type="text/javascript">
    var _gaq = _gaq || [];
    _gaq.push(['_setAccount', 'UA-37225609-1']);
    _gaq.push(['_trackPageview']);

    (function() {
        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
    })();
    </script>


    <link rel="stylesheet" href="../_static/style.css " type="text/css" />
    <link rel="stylesheet" href="../_static/font-awesome.css" type="text/css" />
    <link rel="stylesheet" href="../_static/flag-icon.css" type="text/css" />



    <script type="text/javascript">
    !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);
    js.id=id;js.src="https://platform.twitter.com/widgets.js";
    fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs");
    </script>



    <script type="text/javascript">
    (function() {
    var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true;
    po.src = 'https://apis.google.com/js/plusone.js';
    var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
    })();
    </script>


  <link rel="canonical" href="https://mne.tools/stable/index.html" />
</head>
  <body>





  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
    <div class="container">
      <div class="navbar-header">
        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
          <span class="icon-bar"></span>
          <span class="icon-bar"></span>
          <span class="icon-bar"></span>
        </button>
        <a class="navbar-brand" href="../index.html"><span><img src="../_static/mne_logo_small.png"></span>
           </a>
        <span class="navbar-text navbar-version pull-left"><b>0.15</b></span>
      </div>

        <div class="collapse navbar-collapse nav-collapse">
          <ul class="nav navbar-nav">
            
                <li><a href="../getting_started.html">Install</a></li>
                <li><a href="../documentation.html">Documentation</a></li>
                <li><a href="../python_reference.html">API</a></li>
                <li><a href="../auto_examples/index.html">Examples</a></li>
                <li><a href="../contributing.html">Contribute</a></li>
            
            
              <li class="dropdown globaltoc-container">
  <a role="button"
     id="dLabelGlobalToc"
     data-toggle="dropdown"
     data-target="#"
     href="../index.html">Site <b class="caret"></b></a>
  <ul class="dropdown-menu globaltoc"
      role="menu"
      aria-labelledby="dLabelGlobalToc"></ul>
</li>
              
            
            
            
            
            
              <li class="hidden-sm"></li>
            
          </ul>

          
<div class="navbar-form navbar-right navbar-btn dropdown btn-group-sm" style="margin-left: 20px; margin-top: 5px; margin-bottom: 5px">
  <button type="button" class="btn btn-primary navbar-btn dropdown-toggle" id="dropdownMenu1" data-toggle="dropdown">
    v0.15
    <span class="caret"></span>
  </button>
  <ul class="dropdown-menu" aria-labelledby="dropdownMenu1">
    <li><a href="https://mne-tools.github.io/dev/index.html">Development</a></li>
    <li><a href="https://mne-tools.github.io/stable/index.html">v0.15 (stable)</a></li>
    <li><a href="https://mne-tools.github.io/0.14/index.html">v0.14</a></li>
    <li><a href="https://mne-tools.github.io/0.13/index.html">v0.13</a></li>
    <li><a href="https://mne-tools.github.io/0.12/index.html">v0.12</a></li>
    <li><a href="https://mne-tools.github.io/0.11/index.html">v0.11</a></li>
  </ul>
</div>


            
<form class="navbar-form navbar-right" action="../search.html" method="get">
 <div class="form-group">
  <input type="text" name="q" class="form-control" placeholder="Search" />
 </div>
  <input type="hidden" name="check_keywords" value="yes" />
  <input type="hidden" name="area" value="default" />
</form>
          

        </div>
    </div>
  </div>

<div class="container">
  <div class="row">
      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
        <div class="sphinxsidebarwrapper">
            <p class="logo"><a href="../index.html">
              <img class="logo" src="../_static/mne_logo_small.png" alt="Logo"/>
            </a></p><ul>
<li><a class="reference internal" href="#">mne.find_events</a><ul>
<li><a class="reference internal" href="#examples-using-mne-find-events">Examples using <code class="docutils literal"><span class="pre">mne.find_events</span></code></a></li>
</ul>
</li>
</ul>

<form action="../search.html" method="get">
 <div class="form-group">
  <input type="text" name="q" class="form-control" placeholder="Search" />
 </div>
  <input type="hidden" name="check_keywords" value="yes" />
  <input type="hidden" name="area" value="default" />
</form>
        </div>
      </div>
    <div class="col-md-12 content">
      
  <div class="section" id="mne-find-events">
<h1>mne.find_events<a class="headerlink" href="#mne-find-events" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="mne.find_events">
<code class="descclassname">mne.</code><code class="descname">find_events</code><span class="sig-paren">(</span><em>raw</em>, <em>stim_channel=None</em>, <em>output='onset'</em>, <em>consecutive='increasing'</em>, <em>min_duration=0</em>, <em>shortest_event=2</em>, <em>mask=None</em>, <em>uint_cast=False</em>, <em>mask_type=None</em>, <em>verbose=None</em><span class="sig-paren">)</span><a class="reference external" href="http://github.com/mne-tools/mne-python/blob/maint/0.15/mne/event.py"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mne.find_events" title="Permalink to this definition">¶</a></dt>
<dd><p>Find events from raw file.</p>
<p>See <a class="reference internal" href="../auto_tutorials/plot_epoching_and_averaging.html#tut-epoching-and-averaging"><span class="std std-ref">Epoching and averaging (ERP/ERF)</span></a> as well as <a class="reference internal" href="../auto_examples/io/plot_read_events.html#ex-read-events"><span class="std std-ref">Reading an event file</span></a>
for more information about events.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>raw</strong> : Raw object</p>
<blockquote>
<div><p>The raw data.</p>
</div></blockquote>
<p><strong>stim_channel</strong> : None | string | list of string</p>
<blockquote>
<div><p>Name of the stim channel or all the stim channels
affected by triggers. If None, the config variables
‘MNE_STIM_CHANNEL’, ‘MNE_STIM_CHANNEL_1’, ‘MNE_STIM_CHANNEL_2’,
etc. are read. If these are not found, it will fall back to
‘STI 014’ if present, then fall back to the first channel of type
‘stim’, if present. If multiple channels are provided
then the returned events are the union of all the events
extracted from individual stim channels.</p>
</div></blockquote>
<p><strong>output</strong> : ‘onset’ | ‘offset’ | ‘step’</p>
<blockquote>
<div><p>Whether to report when events start, when events end, or both.</p>
</div></blockquote>
<p><strong>consecutive</strong> : bool | ‘increasing’</p>
<blockquote>
<div><p>If True, consider instances where the value of the events
channel changes without first returning to zero as multiple
events. If False, report only instances where the value of the
events channel changes from/to zero. If ‘increasing’, report
adjacent events only when the second event code is greater than
the first.</p>
</div></blockquote>
<p><strong>min_duration</strong> : float</p>
<blockquote>
<div><p>The minimum duration of a change in the events channel required
to consider it as an event (in seconds).</p>
</div></blockquote>
<p><strong>shortest_event</strong> : int</p>
<blockquote>
<div><p>Minimum number of samples an event must last (default is 2). If the
duration is less than this an exception will be raised.</p>
</div></blockquote>
<p><strong>mask</strong> : int | None</p>
<blockquote>
<div><p>The value of the digital mask to apply to the stim channel values.
If None (default), no masking is performed.</p>
</div></blockquote>
<p><strong>uint_cast</strong> : bool</p>
<blockquote>
<div><p>If True (default False), do a cast to <code class="docutils literal"><span class="pre">uint16</span></code> on the channel
data. This can be used to fix a bug with STI101 and STI014 in
Neuromag acquisition setups that use channel STI016 (channel 16
turns data into e.g. -32768), similar to <code class="docutils literal"><span class="pre">mne_fix_stim14</span> <span class="pre">--32</span></code>
in MNE-C.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.12.</span></p>
</div>
</div></blockquote>
<p><strong>mask_type: ‘and’ | ‘not_and’</strong></p>
<blockquote>
<div><p>The type of operation between the mask and the trigger.
Choose ‘and’ for MNE-C masking behavior. The default (‘not_and’)
will change to ‘and’ in 0.16.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.13.</span></p>
</div>
</div></blockquote>
<p><strong>verbose</strong> : bool, str, int, or None</p>
<blockquote>
<div><p>If not None, override default verbose level (see <a class="reference internal" href="mne.verbose.html#mne.verbose" title="mne.verbose"><code class="xref py py-func docutils literal"><span class="pre">mne.verbose()</span></code></a>
and <a class="reference internal" href="../auto_tutorials/plot_configuration.html#tut-logging"><span class="std std-ref">Logging documentation</span></a> for more).</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>events</strong> : array, shape = (n_events, 3)</p>
<blockquote class="last">
<div><p>All events that were found. The first column contains the event time
in samples and the third column contains the event id. For output =
‘onset’ or ‘step’, the second column contains the value of the stim
channel immediately before the event/step. For output = ‘offset’,
the second column contains the value of the stim channel after the
event offset.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="mne.find_stim_steps.html#mne.find_stim_steps" title="mne.find_stim_steps"><code class="xref py py-obj docutils literal"><span class="pre">find_stim_steps</span></code></a></dt>
<dd>Find all the steps in the stim channel.</dd>
<dt><a class="reference internal" href="mne.read_events.html#mne.read_events" title="mne.read_events"><code class="xref py py-obj docutils literal"><span class="pre">read_events</span></code></a></dt>
<dd>Read events from disk.</dd>
<dt><a class="reference internal" href="mne.write_events.html#mne.write_events" title="mne.write_events"><code class="xref py py-obj docutils literal"><span class="pre">write_events</span></code></a></dt>
<dd>Write events to disk.</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">If you are working with downsampled data, events computed
before decimation are no longer valid. Please recompute
your events after decimation, but note this reduces the
precision of event timing.</p>
</div>
<p class="rubric">Examples</p>
<p>Consider data with a stim channel that looks like:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<p>By default, find_events returns all samples at which the value of the
stim channel increases:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">))</span> 
<span class="go">[[ 1  0 32]</span>
<span class="go"> [ 3 32 33]]</span>
</pre></div>
</div>
<p>If consecutive is False, find_events only returns the samples at which
the stim channel changes from zero to a non-zero value:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">consecutive</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> 
<span class="go">[[ 1  0 32]]</span>
</pre></div>
</div>
<p>If consecutive is True, find_events returns samples at which the
event changes, regardless of whether it first returns to zero:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">consecutive</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> 
<span class="go">[[ 1  0 32]</span>
<span class="go"> [ 3 32 33]</span>
<span class="go"> [ 4 33 32]]</span>
</pre></div>
</div>
<p>If output is ‘offset’, find_events returns the last sample of each event
instead of the first one:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">consecutive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
<span class="gp">... </span>                  <span class="n">output</span><span class="o">=</span><span class="s1">&#39;offset&#39;</span><span class="p">))</span>
<span class="go">[[ 2 33 32]</span>
<span class="go"> [ 3 32 33]</span>
<span class="go"> [ 4  0 32]]</span>
</pre></div>
</div>
<p>If output is ‘step’, find_events returns the samples at which an event
starts or ends:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">consecutive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
<span class="gp">... </span>                  <span class="n">output</span><span class="o">=</span><span class="s1">&#39;step&#39;</span><span class="p">))</span>
<span class="go">[[ 1  0 32]</span>
<span class="go"> [ 3 32 33]</span>
<span class="go"> [ 4 33 32]</span>
<span class="go"> [ 5 32  0]]</span>
</pre></div>
</div>
<p>To ignore spurious events, it is also possible to specify a minimum
event duration. Assuming our events channel has a sample rate of
1000 Hz:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">consecutive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
<span class="gp">... </span>                  <span class="n">min_duration</span><span class="o">=</span><span class="mf">0.002</span><span class="p">))</span>
<span class="go">[[ 1  0 32]]</span>
</pre></div>
</div>
<p>For the digital mask, if mask_type is set to ‘and’ it will take the
binary representation of the digital mask, e.g. 5 -&gt; ‘00000101’, and will
allow the values to pass where mask is one, e.g.:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span>     <span class="mi">7</span> <span class="s1">&#39;0000111&#39;</span> <span class="o">&lt;-</span> <span class="n">trigger</span> <span class="n">value</span>
    <span class="mi">37</span> <span class="s1">&#39;0100101&#39;</span> <span class="o">&lt;-</span> <span class="n">mask</span>
<span class="o">----------------</span>
     <span class="mi">5</span> <span class="s1">&#39;0000101&#39;</span>
</pre></div>
</div>
<p>For the digital mask, if mask_type is set to ‘not_and’ it will take the
binary representation of the digital mask, e.g. 5 -&gt; ‘00000101’, and will
block the values where mask is one, e.g.:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span>     <span class="mi">7</span> <span class="s1">&#39;0000111&#39;</span> <span class="o">&lt;-</span> <span class="n">trigger</span> <span class="n">value</span>
    <span class="mi">37</span> <span class="s1">&#39;0100101&#39;</span> <span class="o">&lt;-</span> <span class="n">mask</span>
<span class="o">----------------</span>
     <span class="mi">2</span> <span class="s1">&#39;0000010&#39;</span>
</pre></div>
</div>
</dd></dl>

<div class="section" id="examples-using-mne-find-events">
<h2>Examples using <code class="docutils literal"><span class="pre">mne.find_events</span></code><a class="headerlink" href="#examples-using-mne-find-events" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="The MEGSIM consists of experimental and simulated MEG data which can be useful for reproducing ..."><div class="figure" id="id1">
<img alt="../_images/sphx_glr_plot_megsim_data_thumb.png" src="../_images/sphx_glr_plot_megsim_data_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/datasets/plot_megsim_data.html#sphx-glr-auto-examples-datasets-plot-megsim-data-py"><span class="std std-ref">MEGSIM experimental and simulation datasets</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we compute the evoked from raw for the Brainstorm tutorial dataset. For comparison, see [1..."><div class="figure" id="id2">
<img alt="../_images/sphx_glr_plot_brainstorm_data_thumb.png" src="../_images/sphx_glr_plot_brainstorm_data_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/datasets/plot_brainstorm_data.html#sphx-glr-auto-examples-datasets-plot-brainstorm-data-py"><span class="std std-ref">Brainstorm tutorial datasets</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Runs a full pipeline using MNE-Python:"><div class="figure" id="id3">
<img alt="../_images/sphx_glr_plot_spm_faces_dataset_thumb.png" src="../_images/sphx_glr_plot_spm_faces_dataset_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/datasets/plot_spm_faces_dataset.html#sphx-glr-auto-examples-datasets-plot-spm-faces-dataset-py"><span class="std std-ref">From raw data to dSPM on SPM Faces dataset</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Representational Similarity Analysis is used to perform summary statistics on supervised classi..."><div class="figure" id="id4">
<img alt="../_images/sphx_glr_decoding_rsa_thumb.png" src="../_images/sphx_glr_decoding_rsa_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/decoding/decoding_rsa.html#sphx-glr-auto-examples-decoding-decoding-rsa-py"><span class="std std-ref">Representational Similarity Analysis</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Decoding of motor imagery applied to EEG data decomposed using CSP. Here the classifier is appl..."><div class="figure" id="id5">
<img alt="../_images/sphx_glr_plot_decoding_csp_eeg_thumb.png" src="../_images/sphx_glr_plot_decoding_csp_eeg_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/decoding/plot_decoding_csp_eeg.html#sphx-glr-auto-examples-decoding-plot-decoding-csp-eeg-py"><span class="std std-ref">Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" The time-frequency decomposition is estimated by iterating over raw data that has been band-pa..."><div class="figure" id="id6">
<img alt="../_images/sphx_glr_plot_decoding_csp_timefreq_thumb.png" src="../_images/sphx_glr_plot_decoding_csp_timefreq_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/decoding/plot_decoding_csp_timefreq.html#sphx-glr-auto-examples-decoding-plot-decoding-csp-timefreq-py"><span class="std std-ref">Decoding in time-frequency space data using the Common Spatial Pattern (CSP)</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the relationship between the noise covariance estimate and the MNE / ..."><div class="figure" id="id7">
<img alt="../_images/sphx_glr_plot_covariance_whitening_dspm_thumb.png" src="../_images/sphx_glr_plot_covariance_whitening_dspm_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/inverse/plot_covariance_whitening_dspm.html#sphx-glr-auto-examples-inverse-plot-covariance-whitening-dspm-py"><span class="std std-ref">Demonstrate impact of whitening on source estimates</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="ICA is fit to MEG raw data. We assume that the non-stationary EOG artifacts have already been r..."><div class="figure" id="id8">
<img alt="../_images/sphx_glr_plot_run_ica_thumb.png" src="../_images/sphx_glr_plot_run_ica_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/preprocessing/plot_run_ica.html#sphx-glr-auto-examples-preprocessing-plot-run-ica-py"><span class="std std-ref">Compute ICA components on epochs</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Compute the distribution of timing for EOG artifacts."><div class="figure" id="id9">
<img alt="../_images/sphx_glr_plot_eog_artifact_histogram_thumb.png" src="../_images/sphx_glr_plot_eog_artifact_histogram_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/preprocessing/plot_eog_artifact_histogram.html#sphx-glr-auto-examples-preprocessing-plot-eog-artifact-histogram-py"><span class="std std-ref">Show EOG artifact timing</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrate movement compensation on simulated data. The simulated data contains bilateral acti..."><div class="figure" id="id10">
<img alt="../_images/sphx_glr_plot_movement_compensation_thumb.png" src="../_images/sphx_glr_plot_movement_compensation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/preprocessing/plot_movement_compensation.html#sphx-glr-auto-examples-preprocessing-plot-movement-compensation-py"><span class="std std-ref">Maxwell filter data with movement compensation</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing experiments where timing is critical, a signal with a high sampling rate is des..."><div class="figure" id="id11">
<img alt="../_images/sphx_glr_plot_resample_thumb.png" src="../_images/sphx_glr_plot_resample_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/preprocessing/plot_resample.html#sphx-glr-auto-examples-preprocessing-plot-resample-py"><span class="std std-ref">Resampling data</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example generates raw data by repeating a desired source activation multiple times."><div class="figure" id="id12">
<img alt="../_images/sphx_glr_plot_simulate_raw_data_thumb.png" src="../_images/sphx_glr_plot_simulate_raw_data_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/simulation/plot_simulate_raw_data.html#sphx-glr-auto-examples-simulation-plot-simulate-raw-data-py"><span class="std std-ref">Generate simulated raw data</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This demonstrates how rER[P/F]s - regressing the continuous data - is a generalisation of tradi..."><div class="figure" id="id13">
<img alt="../_images/sphx_glr_plot_linear_regression_raw_thumb.png" src="../_images/sphx_glr_plot_linear_regression_raw_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/stats/plot_linear_regression_raw.html#sphx-glr-auto-examples-stats-plot-linear-regression-raw-py"><span class="std std-ref">Regression on continuous data (rER[P/F])</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Returns an STC file containing the PSD (in dB) of each of the sources."><div class="figure" id="id14">
<img alt="../_images/sphx_glr_plot_source_power_spectrum_thumb.png" src="../_images/sphx_glr_plot_source_power_spectrum_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/time_frequency/plot_source_power_spectrum.html#sphx-glr-auto-examples-time-frequency-plot-source-power-spectrum-py"><span class="std std-ref">Compute power spectrum densities of the sources with dSPM</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Returns STC files ie source estimates of induced power for different bands in the source space...."><div class="figure" id="id15">
<img alt="../_images/sphx_glr_plot_source_space_time_frequency_thumb.png" src="../_images/sphx_glr_plot_source_space_time_frequency_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/time_frequency/plot_source_space_time_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-space-time-frequency-py"><span class="std std-ref">Compute induced power in the source space with dSPM</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The objective is to show you how to explore spectrally localized effects. For this purpose we a..."><div class="figure" id="id16">
<img alt="../_images/sphx_glr_plot_time_frequency_global_field_power_thumb.png" src="../_images/sphx_glr_plot_time_frequency_global_field_power_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/time_frequency/plot_time_frequency_global_field_power.html#sphx-glr-auto-examples-time-frequency-plot-time-frequency-global-field-power-py"><span class="std std-ref">Explore event-related dynamics for specific frequency bands</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Compute time-frequency maps of power and phase lock in the source space. The inverse method is ..."><div class="figure" id="id17">
<img alt="../_images/sphx_glr_plot_source_label_time_frequency_thumb.png" src="../_images/sphx_glr_plot_source_label_time_frequency_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/time_frequency/plot_source_label_time_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-label-time-frequency-py"><span class="std std-ref">Compute power and phase lock in label of the source space</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example calculates and displays ERDS maps of event-related EEG data. ERDS (sometimes also ..."><div class="figure" id="id18">
<img alt="../_images/sphx_glr_plot_time_frequency_erds_thumb.png" src="../_images/sphx_glr_plot_time_frequency_erds_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/time_frequency/plot_time_frequency_erds.html#sphx-glr-auto-examples-time-frequency-plot-time-frequency-erds-py"><span class="std std-ref">Compute and visualize ERDS maps</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This will produce what is sometimes called an event related potential / field (ERP/ERF) image."><div class="figure" id="id19">
<img alt="../_images/sphx_glr_plot_roi_erpimage_by_rt_thumb.png" src="../_images/sphx_glr_plot_roi_erpimage_by_rt_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_examples/visualization/plot_roi_erpimage_by_rt.html#sphx-glr-auto-examples-visualization-plot-roi-erpimage-by-rt-py"><span class="std std-ref">Plot single trial activity, grouped by ROI and sorted by RT</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to clean MEG data with Maxwell filtering."><div class="figure" id="id20">
<img alt="../_images/sphx_glr_plot_artifacts_correction_maxwell_filtering_thumb.png" src="../_images/sphx_glr_plot_artifacts_correction_maxwell_filtering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_artifacts_correction_maxwell_filtering.html#sphx-glr-auto-tutorials-plot-artifacts-correction-maxwell-filtering-py"><span class="std std-ref">Artifact correction with Maxwell filter</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=""><div class="figure" id="id21">
<img alt="../_images/sphx_glr_plot_artifacts_correction_ssp_thumb.png" src="../_images/sphx_glr_plot_artifacts_correction_ssp_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_artifacts_correction_ssp.html#sphx-glr-auto-tutorials-plot-artifacts-correction-ssp-py"><span class="std std-ref">Artifact Correction with SSP</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Many methods in MNE, including e.g. source estimation and some classification algorithms, requi..."><div class="figure" id="id22">
<img alt="../_images/sphx_glr_plot_compute_covariance_thumb.png" src="../_images/sphx_glr_plot_compute_covariance_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_compute_covariance.html#sphx-glr-auto-tutorials-plot-compute-covariance-py"><span class="std std-ref">Computing covariance matrix</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=""><div class="figure" id="id23">
<img alt="../_images/sphx_glr_plot_epoching_and_averaging_thumb.png" src="../_images/sphx_glr_plot_epoching_and_averaging_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_epoching_and_averaging.html#sphx-glr-auto-tutorials-plot-epoching-and-averaging-py"><span class="std std-ref">Epoching and averaging (ERP/ERF)</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=""><div class="figure" id="id24">
<img alt="../_images/sphx_glr_plot_artifacts_correction_rejection_thumb.png" src="../_images/sphx_glr_plot_artifacts_correction_rejection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_artifacts_correction_rejection.html#sphx-glr-auto-tutorials-plot-artifacts-correction-rejection-py"><span class="std std-ref">Rejecting bad data (channels and segments)</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=":class:`Epochs &lt;mne.Epochs&gt;` objects are a way of representing continuous data as a collection ..."><div class="figure" id="id25">
<img alt="../_images/sphx_glr_plot_object_epochs_thumb.png" src="../_images/sphx_glr_plot_object_epochs_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_object_epochs.html#sphx-glr-auto-tutorials-plot-object-epochs-py"><span class="std std-ref">The Epochs data structure: epoched data</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The objective is to show you how to explore the spectral content of your data (frequency and ti..."><div class="figure" id="id26">
<img alt="../_images/sphx_glr_plot_sensors_time_frequency_thumb.png" src="../_images/sphx_glr_plot_sensors_time_frequency_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_sensors_time_frequency.html#sphx-glr-auto-tutorials-plot-sensors-time-frequency-py"><span class="std std-ref">Frequency and time-frequency sensors analysis</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This script shows how to estimate significant clusters in time-frequency power estimates. It us..."><div class="figure" id="id27">
<img alt="../_images/sphx_glr_plot_stats_cluster_1samp_test_time_frequency_thumb.png" src="../_images/sphx_glr_plot_stats_cluster_1samp_test_time_frequency_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_stats_cluster_1samp_test_time_frequency.html#sphx-glr-auto-tutorials-plot-stats-cluster-1samp-test-time-frequency-py"><span class="std std-ref">Non-parametric 1 sample cluster statistic on single trial power</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we compute the evoked from raw for the Brainstorm Elekta phantom tutorial dataset. For com..."><div class="figure" id="id28">
<img alt="../_images/sphx_glr_plot_brainstorm_phantom_elekta_thumb.png" src="../_images/sphx_glr_plot_brainstorm_phantom_elekta_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_brainstorm_phantom_elekta.html#sphx-glr-auto-tutorials-plot-brainstorm-phantom-elekta-py"><span class="std std-ref">Brainstorm Elekta phantom tutorial dataset</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The aim of this tutorial is to teach you how to compute and apply a linear inverse method such ..."><div class="figure" id="id29">
<img alt="../_images/sphx_glr_plot_mne_dspm_source_localization_thumb.png" src="../_images/sphx_glr_plot_mne_dspm_source_localization_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_mne_dspm_source_localization.html#sphx-glr-auto-tutorials-plot-mne-dspm-source-localization-py"><span class="std std-ref">Source localization with MNE/dSPM/sLORETA</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The aim of this tutorial is to demonstrate how to put a known signal at a desired location(s) i..."><div class="figure" id="id30">
<img alt="../_images/sphx_glr_plot_point_spread_thumb.png" src="../_images/sphx_glr_plot_point_spread_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_point_spread.html#sphx-glr-auto-tutorials-plot-point-spread-py"><span class="std std-ref">Corrupt known signal with point spread</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Decoding, a.k.a MVPA or supervised machine learning applied to MEG data in sensor space. Here t..."><div class="figure" id="id31">
<img alt="../_images/sphx_glr_plot_sensors_decoding_thumb.png" src="../_images/sphx_glr_plot_sensors_decoding_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_sensors_decoding.html#sphx-glr-auto-tutorials-plot-sensors-decoding-py"><span class="std std-ref">Decoding sensor space data (MVPA)</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="MNE-Python reimplements most of MNE-C's (the original MNE command line utils) functionality and..."><div class="figure" id="id32">
<img alt="../_images/sphx_glr_plot_introduction_thumb.png" src="../_images/sphx_glr_plot_introduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_introduction.html#sphx-glr-auto-tutorials-plot-introduction-py"><span class="std std-ref">Basic MEG and EEG data processing</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we compute the evoked from raw for the auditory Brainstorm tutorial dataset. For compariso..."><div class="figure" id="id33">
<img alt="../_images/sphx_glr_plot_brainstorm_auditory_thumb.png" src="../_images/sphx_glr_plot_brainstorm_auditory_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../auto_tutorials/plot_brainstorm_auditory.html#sphx-glr-auto-tutorials-plot-brainstorm-auditory-py"><span class="std std-ref">Brainstorm auditory tutorial dataset</span></a></span></p>
</div>
</div><div style='clear:both'></div></div>
</div>


    </div>
    
  </div>
</div>
<footer class="footer">
  <div class="container"><img src="../_static/institutions.png" alt="Institutions"></div>
  <div class="container">
    <ul class="list-inline">
      <li><a href="https://github.com/mne-tools/mne-python">GitHub</a></li>
      <li>·</li>
      <li><a href="https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis">Mailing list</a></li>
      <li>·</li>
      <li><a href="https://gitter.im/mne-tools/mne-python">Gitter</a></li>
      <li>·</li>
      <li><a href="whats_new.html">What's new</a></li>
      <li>·</li>
      <li><a href="faq.html#cite">Cite MNE</a></li>
      <li class="pull-right"><a href="#">Back to top</a></li>
    </ul>
    <p>&copy; Copyright 2012-2017, MNE Developers. Last updated on 2017-10-31.</p>
  </div>
</footer>
<script src="https://mne.tools/versionwarning.js"></script>
  </body>
</html>