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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Hi Makoto,
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">My question is : does doing PCA before ICA helps for increasing K or in other words the quality of ICA output? Could you please describe it in a simple language
</span><span style="font-size:11.0pt;font-family:Wingdings;color:#1F497D">J</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><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">Thanks<o:p></o:p></span></a></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">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> Makoto Miyakoshi [mailto:mmiyakoshi@ucsd.edu]
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<b>Sent:</b> Wednesday, April 20, 2016 7:01 PM<br>
<b>To:</b> Iman Mohammad-Rezazadeh <irezazadeh@UCDAVIS.EDU><br>
<b>Cc:</b> EEGLAB List <eeglablist@sccn.ucsd.edu>; Arnaud Delorme <arno@ucsd.edu>; Loo, Sandra <SLoo@mednet.ucla.edu>; Jeste, Shafali M.D. <SJeste@mednet.ucla.edu>; ADickinson@mednet.ucla.edu; Scott Makeig <smakeig@ucsd.edu><br>
<b>Subject:</b> Re: ICA after PCA<o:p></o:p></span></p>
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<p class="MsoNormal">Dear Iman,<o:p></o:p></p>
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<p class="MsoNormal">> I have found few papers and discussions about doing PCA and then ICA for increasing the K-factor and dimensionality reduction.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">K-factor you mean is the term you see in the equation?<o:p></o:p></p>
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<p class="MsoNormal">minimumDataPointsToRunICA = ((number of channels)^2) * K<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">> However, I cannot completely understand what is the meaning of the ICA outputs? <o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">ICA output rotates PCA-dimension-reduced data. You can reconstruct full-channel (but now rank-deficient) data using PCA output.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Actually there is another rotation, sphering, as a preprocess for ICA. So you rotate data three times to find unmixing matrix using PCA rank-reduction option. That's why you feel dizzy :-)<o:p></o:p></p>
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<p class="MsoNormal">Makoto<o:p></o:p></p>
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<p class="MsoNormal">On Wed, Apr 13, 2016 at 11:59 PM, Iman Mohammad-Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Hi EEGLABers,<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">I have found few papers and discussions about doing PCA and then ICA for increasing the K-factor and dimensionality reduction. The (un)mixing matrix would be m x m which m is the
number of PCA. Each row (column) is the weights for ICA sources.<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">However, I cannot completely understand what is the meaning of the ICA outputs? How are the IC maps (topo maps) constructed since we need the location of PCA components (similar
to the channels locations) to plot the spatial filters/IC maps. <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">In other words, how can we plot the IC maps given the fact that we don’t have the spatial information about PCA components?<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Best<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Iman
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">============================================<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="font-size:10.0pt;font-family:"Arial Rounded MT Bold",sans-serif;color:#7030A0">Iman M.Rezazadeh, Ph.D</span></b><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.0pt;font-family:"Arial",sans-serif">UCLA David Geffen School of Medicine</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.0pt;font-family:"Arial",sans-serif">Semel Institute for Neuroscience and Human Behavior</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.0pt;font-family:"Arial",sans-serif">760 Westwood Plaza, Ste 47-448</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.0pt;font-family:"Arial",sans-serif">Los Angeles, CA 90095</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><a href="http://www.linkedin.com/pub/iman-m-rezazadeh/10/859/840/" target="_blank"><span style="color:#0563C1">http://www.linkedin.com/pub/iman-m-rezazadeh/10/859/840/</span></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">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<o:p></o:p></p>
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