[Eeglablist] What to do with more than one IC per subject in a cluster
mmiyakoshi at ucsd.edu
Thu Mar 19 10:11:46 PDT 2015
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
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
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)
On Thu, Feb 26, 2015 at 2:29 AM, Salim Al-wasity <salim_alwasity at yahoo.com>
> As 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?
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Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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