<div dir="ltr">Dear Shingo,<div><br></div><div>Ok here is a solution for you based on my guess.</div><div><br></div><div><span style="font-size:12.6666669845581px">> Is it impossible to plot topographies of two experimental conditions for a component cluster as we can do in channel pot (for a time-window)?</span><br></div><div><br></div><div>I guess you mean 'to plot <b><i>difference</i></b> topographies of two experimental conditions for a component cluster'. The instruction below is based on this assumption.</div><div><br></div><div>Case 1: The conditions are all within-subject and with the continuous use of an EEG cap.</div><div>Unless each within-subject condition is recorded on different sessions (i.e. apply the EEG cap for one session, remove it, and applied it newly for the next session), independent components should be shared across the conditions. If this is the case, then difference topography is all zero. Therefore, <b>you can't get difference topographies</b>.</div><div><br></div><div>Case 2: The conditions are between-subject (or within-subject but different sessions with different EEG cap application). If this is the case, subtraction of IC scalp maps makes sense. </div><div><br></div><div>How to do it:</div><div><br></div><div>1. Load your STUDY with group differences (i.e. Case 2 mentioned above)</div><div><br></div><div>2. Use my plugin std_envtopo and show whatever envtopo. This is to generate topoall_group under STUDY.cluster(1,X) It looks like this</div><div><br></div><div><div>topoall_group: {{1x14 cell} {1x17 cell}}<br></div></div><div><br></div><div>3. To visualize Group A and Group B in Cluster 5, see my code and result example below</div><div><a href="https://drive.google.com/open?id=0BzTEovh4TOLiflAwREdrZmxsUTBfVFdjR3AyS0pOV3JONEEtTHpwdndwLU9mVXg3cHlITHc&authuser=0">https://drive.google.com/open?id=0BzTEovh4TOLiflAwREdrZmxsUTBfVFdjR3AyS0pOV3JONEEtTHpwdndwLU9mVXg3cHlITHc&authuser=0</a><br></div><div><br></div><div>Let me know if you have any trouble in accessing my Google drive.</div><div><br></div><div>Makoto</div><div><br></div><div><br></div><div><br></div><div><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, May 5, 2015 at 1:23 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr">Dear Shingo,<div><br></div><div>I don't think it's possible.</div><div>scalp topo generation is relatively straightforward using pop_topoplot() function. I can help you to do this if you are interested in.</div><div><br></div><div>Makoto</div></div><div class="gmail_extra"><div><div class="h5"><br><div class="gmail_quote">On Thu, Apr 30, 2015 at 3:24 PM, Shingo Tokimoto <span dir="ltr"><<a href="mailto:tokimoto@mejiro.ac.jp" target="_blank">tokimoto@mejiro.ac.jp</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">Dear EEGLAB experts,<br>
<br>
Is it impossible to plot topographies of two experimental conditions for a component cluster as we can do in channel pot (for a time-window)? Thank you in advance.<br>
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Shingo Tokimoto, Ph.D.<br>
in Linguistics and Psychology<br>
Department of Foreign Languages<br>
Mejiro University<br>
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,<br>
161-8539, Japan<br>
<a href="mailto:tokimoto@mejiro.ac.jp" target="_blank">tokimoto@mejiro.ac.jp</a><br>
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</blockquote></div><br><br clear="all"><div><br></div></div></div><span class=""><font color="#888888">-- <br><div><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div></div>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature"><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div></div>
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