[Eeglablist] What to do with more than one IC per subject in a cluster

[Salim Al-wasity]

Dear Mr. MakotoThanks for your reply.Would you recommend a reference paper for the advantages of manual pre-selection.I read the Nima's paper of measure projection analysis, and he used the same dataset form both clustering approaches.In my study I am comparing the results of these two approaches to discover which one the best for clustering.
SincerelySalim 
 


     On Thursday, 19 March 2015, 17:12, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
   

 Dear Salim,
Sorry for slow response.
> 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.

Interesting. As long as you choose them by anatomical locations, I believe you are fine.
> and Whether these results are comparable to those of MPT?

First you have to check how much overlap between MPT-selected ICs and your selections.
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).
Sorry it could be confusing that there are two solutions for the group-level analysis.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)http://sccn.ucsd.edu/wiki/Backproject_clustered_ICs

Makoto


On Thu, Feb 26, 2015 at 2:29 AM, Salim Al-wasity <salim_alwasity at yahoo.com> wrote:

DearsAs I understood from the following discussion (http://sccn.ucsd.edu/pipermail/eeglablist/2013/006353.html), its not recommended to pre-select the components for a PCA-clustering approach?
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. 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?
SincerelySalim 
_______________________________________________
Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu




-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego


  
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20150321/7f0d6990/attachment.html>