<div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>Dear Martin,</div><div><br></div><div>this is a significant and known problem with IC clustering. Since the decomposition is never the same for different subjects (or even the same subject on a different recording), clusters will not contain a 1:1 mapping of ICs. Another big problem is that the algorithm does not find the same solution if clustering repeatedly, which is very bad for replication. I played around with this problem for a good while and created an algorithm for repeated clustering which provides the best solution for a particular region of interest, based on several quality measures. Please refer to this poster:<br></div><div><br></div><div><a href="https://www.researchgate.net/publication/326690351_The_BeMoBIL_Pipeline_-_Facilitating_Mobile_BrainBody_Imaging_MoBI_Data_Analysis_in_MATLAB">https://www.researchgate.net/publication/326690351_The_BeMoBIL_Pipeline_-_Facilitating_Mobile_BrainBody_Imaging_MoBI_Data_Analysis_in_MATLAB</a><br></div><div><br></div><div>You can find the code online on my github, but be aware that it is work in progress and the usability is not yet very good. <br></div><div><br></div><div><a href="https://github.com/MariusKlug/bemobil-pipeline">https://github.com/MariusKlug/bemobil-pipeline</a></div><div><br></div><div>The functions of interest for you are: bemobil_precluster() and
<span class="gmail-pl-c">bemobil_repeated_clustering_and_evaluation(), the help function should guide you through the process. You can define a ROI in the somatosensory cortex in talairach coordinates and try it out. I recommend no less than 100 repetitions. You will get the best 5 solutions plotted in the end, if they look very similar the algorithm converged and you can trust that somebody will likely be able to replicate your results using the same weight values with the same data. <br></span></div><div><span class="gmail-pl-c"><br></span></div><div><span class="gmail-pl-c">The algorithm was applied in this paper: <a href="https://www.biorxiv.org/content/early/2018/09/14/417972">https://www.biorxiv.org/content/early/2018/09/14/417972</a> <br></span></div><div><span class="gmail-pl-c">For creating the study I used the first 70 ICs of each subject (which is about the better half of all ICs) and relied on the negative weight for residual variance in my clustering. You find all relevant values in method section of the paper.<br></span></div><div><span class="gmail-pl-c"><br></span></div><div><span class="gmail-pl-c">I have also tried the measure projection toolbox, but was not able to replicate their own results with their example dataset and then dropped the project again in favor of the repeated clustering. If you can use measure projection effectively please drop me a line!</span></div><div><span class="gmail-pl-c"><br></span></div><div><span class="gmail-pl-c">I'm happy to help with questions and hope this helps you in your research!</span></div><div><span class="gmail-pl-c">Marius<br></span>
</div></div></div></div></div><br><div class="gmail_quote"><div dir="ltr">Am Sa., 20. Okt. 2018 um 23:11 Uhr schrieb Martin Simoneau <<a href="mailto:Martin.Simoneau@kin.ulaval.ca">Martin.Simoneau@kin.ulaval.ca</a>>:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div lang="FR-CA" link="#0563C1" vlink="#954F72">
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<p class="MsoNormal"><span lang="EN-CA">Dear EEGlab experts,<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA">I have clustered the components of all the participants. Let says that I am targeting components located in the somatosensory cortex. Lucky enough, one cluster mean is in the somatosensory cortex. However, not all participants
have a component in this cluster. What do I do with the participants that do not have a component in this cluster? If there is any paper (documentation…) related to this issue, please let me know as I will be happy to read it.<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA">When performing K-mean clustering, is there a minimum or a maximum number of clusters that one should select? Again, if there is any paper (documentation…) related to this topic, please let me know as could not find any.
<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA">Finally, does the measure projection toolbox solved this problem of having a cluster with component from some participant not included?<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA">Thanks for your help,<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA">Martin <u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
<p class="MsoNormal"><span lang="EN-CA"><u></u> <u></u></span></p>
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