<div dir="ltr">Dear Salim,<div><br></div><div>I don't know any paper for that, but the approach is reasonable. Be confident.</div><div><br></div><div>Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">On Sat, Mar 21, 2015 at 4:51 AM, Salim Al-wasity <span dir="ltr"><<a href="mailto:salim_alwasity@yahoo.com" target="_blank">salim_alwasity@yahoo.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div style="color:#000;background-color:#fff;font-family:arial,helvetica,sans-serif;font-size:16px"><div dir="ltr"><span>Dear Mr. Makoto</span></div><div dir="ltr"><span>Thanks for your reply.</span></div><div dir="ltr"><span>Would you recommend a reference paper for the advantages of manual pre-selection.</span></div><div dir="ltr"><span>I read the Nima's paper of measure projection analysis, and he used the same dataset form both clustering approaches.</span></div><div dir="ltr"><span>In my study I am comparing the results of these two approaches to discover which one the best for clustering.</span></div><div dir="ltr"><span><br></span></div><div dir="ltr"><span>Sincerely</span></div><span><font color="#888888"><div dir="ltr"><span>Salim</span></div></font></span><div><div><div></div><div> </div><div><div><span style="FONT-FAMILY:times new roman"><div style="font-weight:bold;color:rgb(192,0,0)"><br></div></span></div></div> <br><div><br><br></div><div style="display:block"> <div style="font-family:arial,helvetica,sans-serif;font-size:16px"> <div style="font-family:HelveticaNeue,Helvetica Neue,Helvetica,Arial,Lucida Grande,Sans-Serif;font-size:16px"> <div dir="ltr"> <font face="Arial"> On Thursday, 19 March 2015, 17:12, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>> wrote:<br> </font> </div> <br><br> <div><div><div><div dir="ltr">Dear Salim,<div><br clear="none"></div><div>Sorry for slow response.</div><div><br clear="none"></div><div><span style="color:rgb(0,0,0);font-family:arial,helvetica,sans-serif">> for PCA-clustering I removed the noisy ICs and then I preselected ICs which are located in the Sensorimotor cortex, after that I created a STUDY and used a PCA for clustering and ensured each cluster has at most 2 components from the same subject to avoid biasing.</span><br clear="none"></div><div><br clear="none"></div><div>Interesting. As long as you choose them by anatomical locations, I believe you are fine.</div><div><br clear="none"></div><div><span style="color:rgb(0,0,0);font-family:arial,helvetica,sans-serif">> and Whether these results are comparable to those of MPT?</span><br clear="none"></div><div><br clear="none"></div><div>First you have to check how much overlap between MPT-selected ICs and your selections.</div><div><br clear="none"></div><div>MPT is, to my understanding, a 'similarity filter'. Therefore, I expect that MPT-selected ICs could be more consistent in the selected measure than k-mean clustering (well, you can still tweak the parameters in k-mean clustering so that the result is maximally similar to that of MPT).</div><div><br clear="none"></div><div>Sorry it could be confusing that there are two solutions for the group-level analysis.</div><div>By the way, before running MPT I recommend you clean the ICs in the following way (did I tell this to you before? If so excuse me)</div><div><a rel="nofollow" shape="rect" href="http://sccn.ucsd.edu/wiki/Backproject_clustered_ICs" target="_blank">http://sccn.ucsd.edu/wiki/Backproject_clustered_ICs</a><br clear="none"></div><div><br clear="none"></div><div>Makoto</div><div><br clear="none"></div><div><br clear="none"></div><div><br clear="none"><div>On Thu, Feb 26, 2015 at 2:29 AM, Salim Al-wasity <span dir="ltr"><<a rel="nofollow" shape="rect" href="mailto:salim_alwasity@yahoo.com" target="_blank">salim_alwasity@yahoo.com</a>></span> wrote:<br clear="none"><blockquote 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><div><div style="color:rgb(0,0,0);font-family:arial,helvetica,sans-serif;font-size:16px;background-color:rgb(255,255,255)"><div dir="ltr">Dears</div><div dir="ltr">As I understood from the following discussion (<a rel="nofollow" shape="rect" href="http://sccn.ucsd.edu/pipermail/eeglablist/2013/006353.html" target="_blank">http://sccn.ucsd.edu/pipermail/eeglablist/2013/006353.html</a>), its not recommended to pre-select the components for a PCA-clustering approach?</div><div dir="ltr"><br clear="none"></div><div dir="ltr">I am comparing between the ERSP results of using MPT approach and PCA-clustering approach. In the first STUDY, I used the MPT to cluster the ICs (after removing the noisy ones). and for PCA-clustering I removed the noisy ICs and then I preselected ICs which are located in the Sensorimotor cortex, after that I created a STUDY and used a PCA for clustering and ensured each cluster has at most 2 components from the same subject to avoid biasing. </div><div dir="ltr">Is this technique of pre-selecting ICs based on ROI (Region Of Interest) prior to PCA-clustering is a good way to avoid biasing? and Whether these results are comparable to those of MPT?</div><div dir="ltr"><br clear="none"></div><div dir="ltr">Sincerely</div><span><font color="#888888"></font></span><div dir="ltr">Salim</div><div dir="ltr"> </div></div></div></div><br clear="none">_______________________________________________<br clear="none">
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For digest mode, send an email with the subject "set digest mime" to <a rel="nofollow" shape="rect" href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><br clear="none"></blockquote></div><br clear="none"><br clear="all"><div><br clear="none"></div>-- <br clear="none"><div><div dir="ltr">Makoto Miyakoshi<br clear="none">Swartz Center for Computational Neuroscience<br clear="none">Institute for Neural Computation, University of California San Diego<br clear="none"></div></div>
</div></div></div></div><br><br></div> </div> </div> </div></div></div></div></div></blockquote></div><br><br clear="all"><div><br></div>-- <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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