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

[Chadwick Boulay]

Dear Makoto,

I've been trying to do IC clustering but I guess I just don't get it.

On 3/1/2013 8:42 AM, Makoto Miyakoshi wrote:
>
> If it were inappropriate then most of our publications would be bad :-)
>
> ICA is data driven and generates subspaces where ICs are 
> intradependent. It also reflects individual differences. This is like 
> cost for this nice method. However, what actually matter is 
> convention, isn't it?
>
>
In general, reporting a statistically significant difference in a group 
of subjects implies that the source of error is between-subject and each 
subject is represented exactly once (for a given condition). If, in a 
peer-reviewed article, I see a statement such as, "P300 amplitude was 
greater after attended stimuli than unattended stimuli (p<0.05)" then I 
assume that the source of error used in the statistical test is the 
between subject error, unless of course the authors explicitly state 
this is a single-subject, in which case the source of error is the 
between-trial error. This is convention, isn't it?

Since IC clusters may, at the extreme, represent a single subject then 
the above statement could really mean a single-subject difference even 
though many subjects were measured. To me, this breaks convention and is 
unintuitive.

Thus, I have been manually editing my clusters so each cluster has 
exactly 1 IC from each subject, even if sometimes that means excluding 
an IC that fits my model if it comes from a subject with multiple 
clusters that fit the model or including an IC that does not fit the 
model if a given subject has no clusters that fit the model. I see no 
other way to report my results in a manner that follows convention. 
Editing clusters this way is biased but I'm not sure what else I can do.

Do you have any other suggestions?
Should I merge ICs from a subject when they are over-represented in a 
cluster? Should I exclude subjects that are not represented in a cluster 
and explicitly state that along with the result? i.e., "Left M1 cluster 
mu-rhythms were significantly more desynchronized during task A compared 
to task B among the 6 (of 10) subjects with an IC that localized to left 
M1 and had similar ERSP profiles."

I read the MPT paper and tried using it but I ended up with an 
interesting domain that contained only 1 dipole (from a set of 13 
subjects x 64 ICs). I can't report that. I still don't understand this 
method fully so I do not admit defeat yet.

Thank you,
Chadwick Boulay
JSPS Postdoctoral Fellow
Keio University, Yokohama, Japan