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<div>
<div></div>
</div>
<div style="direction: ltr;">For most purposes, you can treat the coefficients from a linear model just like normal ERPs (Which would correspond to coefficients from an intercept-only model run on the subset of epochs). For an explanation, see eg Smith &amp; Kutas
 201x psychophysiology.&nbsp;</div>
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<div>&nbsp;</div>
<hr style="display:inline-block;width:98%" tabindex="-1">
<div id="divRplyFwdMsg" dir="dir=&quot;ltr&quot;"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Alexandre Gramfort &lt;alexandre.gramfort@polytechnique.org&gt;<br>
<b>Sent:</b> Tuesday, September 3, 2019 9:09 PM<br>
<b>To:</b> Denis A. Engemann; Jona Sassenhagen<br>
<b>Subject:</b> Fwd: [Mne_analysis] Group level ERF regression
<div>&nbsp;</div>
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<meta content="text/html; charset=utf-8">
<div dir="ltr">for you ! or Josť...
<div><br>
</div>
<div>A<br>
<br>
<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>
From: <strong class="gmail_sendername" dir="auto">Kambiz Tavabi</strong> <span dir="auto">
&lt;<a href="mailto:ktavabi@uw.edu">ktavabi@uw.edu</a>&gt;</span><br>
Date: Tue, Sep 3, 2019 at 7:50 PM<br>
Subject: [Mne_analysis] Group level ERF regression<br>
To: <a href="mailto:mne_analysis@nmr.mgh.harvard.edu">mne_analysis@nmr.mgh.harvard.edu</a> &lt;<a href="mailto:mne_analysis@nmr.mgh.harvard.edu">mne_analysis@nmr.mgh.harvard.edu</a>&gt;<br>
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<br>
<br>
<p><span style="padding:3px 10px; border-radius:5px; color:#ffffff; font-weight:bold; display:inline-block; background-color:#ff0000">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;External Email - Use Caution&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span></p>
<p></p>
<div>Hi all,</div>
<div>I'd like to carry out single-trial regression using linear_regression python function to evaluate effect of continuous variable on ERFs. The following gist yields the desired output for a single subject<span>:</span></div>
<code>
<pre style="background-color:rgba(0,0,0,0.05); padding:0.2em 1em"><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace">n_obs = len(epochs)</font></font></span></div><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace">intercept = np.ones((n_obs, 1))</font></font></span></div><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace"># Design matrix for continuous covariate</font></font></span></div><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace">regressor = np.linspace(8, 66, n_obs)</font></font></span></div><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace">dmat = np.concatenate((regressor[:, np.newaxis], intercept), axis=1)</font></font></span></div><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace"># regression between ERF &amp; covariate</font></font></span></div><div><span style="color:rgb(29,28,29)"><font style="font-size:12px"><font style="font-family:Monaco,Menlo,Consolas,&quot;Courier New&quot;,monospace">res = linear_regression(epochs, design_matrix=dmat, names=names)</font></font></span></div></pre>
</code>
<div>But it's not clear to me what to do with the resulting mne.Evoked objects containing beta values, to describe the regression results across a group of subjects. E.g. does it make sense to average the evoked (beta) objects across the group? and if so how
 do I derive the t-values? Thanks in advance.</div>
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<div>Kambiz Tavabi PhD</div>
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