[Eeglablist] Multiple sessions - same condition - per subject

[Chadwick Boulay]

Dear all,

I'm sorry to send two messages on the same day. I have a question 
related to the IC clustering discussion.

How do I perform a between-subjects statistical analysis on independent 
components from data that are obtained over multiple sessions within a 
subject? This was a BCI study and each session was a near-identical 
replication of the previous (i.e. same 'condition' in each session). 
This was a P300 study so I do not expect there to be much of a learning 
effect and I do not expect the P300 'source' to change across sessions.

I tried pretending all data came from the same session then running ICA 
on the concatenated data and I ended up with 5 ICs for eye blinks (5 
sessions) with each of those ICs representing mostly a subset of trials 
from a single session.

I can treat each subject*session as an independent subject so I have 62 
subjects (13ss * 5 sess - 3 noisy sessions) instead of 13. I think this 
is wrong because it gives me too much statistical power and some 
subjects are underrepresented.

I can choose a set of ICs from one session then recalculate activations 
and concatenate trials, but which session do I use?

Finally, I can cluster ICs within-subject in a manner similar to the 
2012 EMBC IEEE Conf Proc by Grandchamp et al 
(http://www.ncbi.nlm.nih.gov/pubmed/23367475). Then, I could manually 
reorder the ica weights so that each session's ICs are ordered according 
to their cluster-membership then recalculate activations. I could then 
concatenate activations across sessions into a super-session. Could I 
then use the average weights and dipole centroid for the super-component?
Can someone please share with me the modified clustering code to 
constrain the clusters to only include one component per session?

Thank you,

Chadwick Boulay
JSPS Postdoctoral Fellow
Keio University
Yokohama, Japan