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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Hi Max,
<o:p></o:p></span></p>
<p class="MsoNormal"><a name="_MailEndCompose"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">It is great.
<o:p></o:p></span></a></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">I have a question about the toolbox and its application on continuous EEG processing , especially during resting. Examples in your published paper are based on
ERP experiments however if we don’t have any sign on ERP components in the temporal activity I wonder how your algorithm/toolbox can differentiate btw neural and non-neural for something like Fig 3 G. For example, In your paper it is obvious that for the
above figure, you have got a early positive and late negative ERP components. <o:p>
</o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Best,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Iman
<o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">-------------------------------------------------------------<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Iman M.Rezazadeh, Ph.D<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Semel Intitute, UCLA , Los Angeles<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">& Center for Mind and Brain, UC DAVIS, Davis<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> eeglablist-bounces@sccn.ucsd.edu [mailto:eeglablist-bounces@sccn.ucsd.edu]
<b>On Behalf Of </b>Maximilien Chaumon<br>
<b>Sent:</b> Tuesday, July 19, 2016 4:15 AM<br>
<b>To:</b> eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> [Eeglablist] SASICA 1.3.1 now available<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Dear all,<br>
<br>
A new version of SASICA, the EEGLAB extension for ICA component selection is available in the extension repository of EEGLAB (File > Manage Extensions > data processing extensions), or directly from
<a href="https://github.com/dnacombo/SASICA/archive/master.zip" target="_blank">github</a>.<o:p></o:p></p>
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<p class="MsoNormal">The associated paper can be downloaded <a href="https://github.com/dnacombo/SASICA/blob/master/Chaumon_et_al_JNM_2015.pdf" target="_blank">
here</a>.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">New features:<o:p></o:p></p>
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<p class="MsoNormal">Bug fixes and improved usability.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Note: on a new install, first entering SASICA('resetprefs') in the command window is recommended.<br>
<br>
Enjoy!<br>
Max<o:p></o:p></p>
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