[Eeglablist] Silhouette value for IC clustering

Mon Dec 4 10:25:32 PST 2017

Without confirming (I have now > 15 plugins...), yes I think so. Also, when
you plot STUDY scalp topography, it shows the number of unique subjects as
well. If you don't have between-group condition, it would be the easiest to
know the number.

Makoto

On Sat, Dec 2, 2017 at 7:28 AM, 時本真吾 <tokimoto at mejiro.ac.jp> wrote:

> Miyakoshi-san,
>
> Thank you for the reply again. I understand your intention. Is the plugin
> you mentioned for the report of how many unique subjects are included by
> each cluster ‘std_envtopo?'
>
> ******************************************
> Shingo Tokimoto, Ph.D.
> in Linguistics and Psychology
> Department of Foreign Languages
> Mejiro University
> 4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
> 161-8539, Japan
> tokimoto at mejiro.ac.jp
> ******************************************
>
> > 2017/11/30 9:36、Makoto Miyakoshi <mmiyakoshi at ucsd.edu>のメール:
> >
> > Dear Shingo,
> >
> > This is not a published method, but it's such a simple idea that it does
> not require publication to be justified.
> > You are replacing one open parameter ('How many clusters do we want to
> generate? We determined to be 12') with another open parameter ('How many
> unique subjects do we want to include in a cluster? We determined 80%').
> The latter has more intuitive value than the former one.
> >
> > In my grroupSIFT() toolbox (still in alpha version), I use this 'minimum
> number of unique subject' criterion to threshold connectivity edges. Even
> if you find an interesting looking result, if it is from only < 30 % of
> subjects it's difficult to be justified.
> >
> > Which reminds me that in one of Scott's previous works (Onton and
> Makeig, 2009), they captured only 35% of total variance--not due to
> cleaning, but for dimension reduction. Probably this is the record of the
> smallest variance analyzed in the history of EEG study.
> >
> > Makoto
> >
> > On Wed, Nov 29, 2017 at 3:14 PM, 時本真吾 <tokimoto at mejiro.ac.jp> wrote:
> > Dear Makoto,
> >
> > Thank you for the quick and kind reply. This is really practical and
> easy to understand. Is this method published anywhere? I am referring to
> Takahashi & Kitazawa (2017, JNS) for Silhouette value as a criterion of IC
> clustering.
> >
> > ******************************************
> > Shingo Tokimoto, Ph.D.
> > in Linguistics and Psychology
> > Department of Foreign Languages
> > Mejiro University
> > 4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
> > 161-8539, Japan
> > tokimoto at mejiro.ac.jp
> > ******************************************
> >
> > > 2017/11/29 3:04、Makoto Miyakoshi <mmiyakoshi at ucsd.edu>のメール:
> > >
> > > Dear Shingo,
> > >
> > > Another practical criterion to determine the number of clusters is to
> specify the minimum number of unique subjects per cluster. For example, if
> you determine to include at least 70% of unique subjects, after clustering
> you count how many unique subjects are included, and if it did not reach
> 70%, you lower the number of clusters and try it again until it meets the
> 70%-criterion. In most of my plugins for STUDY anlaysis, it is reported how
> many unique subjects are included by each cluster.
> > >
> > > Makoto
> > >
> > > On Sat, Nov 25, 2017 at 2:12 AM, 時本真吾 <tokimoto at mejiro.ac.jp> wrote:
> > > Dear Dr. Jens Bernhardsson,
> > >
> > > I am deeply grateful for the excellent code. I am very sorry for the
> late response. This is because I took some time to understand the meaning
> of plotted results. I am trying to cluster 323 ICs from 31 participants by
> dipoles. I repeated the code 10  times, and the number of clusters for
> which the average Silhouette value was greatest was 15 or 16. Can I
> understand that the best number of clusters here is 15 or 16? Thank you
> very much again.
> > >
> > > ******************************************
> > > Shingo Tokimoto, Ph.D.
> > > in Linguistics and Psychology
> > > Department of Foreign Languages
> > > Mejiro University
> > > 4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
> > > 161-8539, Japan
> > > tokimoto at mejiro.ac.jp
> > > ******************************************
> > >
> > > > 2017/11/17 19:19、Bernhardsson Jens <Jens.Bernhardsson at miun.se>のメール:
> > > >
> > > > Hi,
> > > >
> > > > I believe that the Silhouette value found in A10: MI-clust is based
> on kmeans cluster of multidimensional scaled data performed on the mutual
> information matrix and the kmeans found in STUDY is based on a pca derived
> (pre clustering) array. You can run the code below after you pre cluster in
> STUDY to get the mean and individual Silhouette coefficients for different
> cluster solutions.
> > > >
> > > > avgSil = [];
> > > > kmod = [];
> > > > siSil = [];
> > > >
> > > > for num_of_cluster = 2:25
> > > >    kmod = kmeans(STUDY.etc.preclust.preclustdata,num_of_cluster,
> 'replicates',10,'emptyaction','drop','distance','sqEuclidean');
> > > >    siSil(:,num_of_cluster-1) = silhouette(STUDY.etc.preclust.
> preclustdata,kmod,'sqEuclidean');
> > > >    avgSil = [avgSil; num_of_cluster mean(siSil(:,num_of_cluster-1)
> )];
> > > > end
> > > >
> > > > figure;
> > > > plot( avgSil(:,1),avgSil(:,2),'r*-.');
> > > > % ylim([0 1])
> > > > set(gca,'XTick',1:num_of_cluster, 'XGrid', 'on');
> > > >
> > > >
> > > > Regards,
> > > > Jens
> > > >
> > > > -----Ursprungligt meddelande-----
> > > > Från: eeglablist [mailto:eeglablist-bounces at sccn.ucsd.edu] För ????
> > > > Skickat: den 17 september 2017 15:43
> > > > Till: eeglablist at sccn.ucsd.edu
> > > > Ämne: [Eeglablist] Silhouette value for IC clustering
> > > >
> > > > Dear EEGLAB users,
> > > >
> > > > I understand that the determination of the number of clusters in IC
> clustering is one of the difficult problems in source localizations by ICs.
> Silhouette value is one of the criteria for the best number of clusters in
> IC clustering, and I have found the description of silhouette value in
> EEGLAB Wiki (A10: MI-clust) for a single participant. Can we get silhouette
> values for group data in STUDY formats? Thank you in advance.
> > > >
> > > > ******************************************
> > > > Shingo Tokimoto, Ph.D.
> > > > in Linguistics and Psychology
> > > > Department of Foreign Languages
> > > > Mejiro University
> > > > 4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
> > > > 161-8539, Japan
> > > > tokimoto at mejiro.ac.jp
> > > > ******************************************
> > > >
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> > >
> > >
> > > --
> > > Makoto Miyakoshi
> > > Swartz Center for Computational Neuroscience
> > > Institute for Neural Computation, University of California San Diego
> >
> >
> >
> >
> > --
> > Makoto Miyakoshi
> > Swartz Center for Computational Neuroscience
> > Institute for Neural Computation, University of California San Diego
>
>

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