[Eeglablist] Σχετ: eeglablist Digest, Vol 85, Issue 4

[stauros dimitriadis]

Dear Sharma,
The detection of reliable and related to task activity is an important issue in ICA analysis.
I will suggest you some techniques from my experience.
At first, it is important to apply ICA at each trial seperately.
Afterward, you can quantify the intertrial synchronization (e.g coherence) of each component seperately and then by applying a threshold (mean + 1 st.d. estimated by the intertrial coherence across ICAs) 
you can isolate componets related to p300. The intertrial coherence should be applied across 1st ICA from all the trials, 2nd ICA from all the trials etc.
I suggest to apply ICA seperately to each trial in order to study the variation in amplitude and latency across trials.
 Finally, you can adopt a causality synchronization measure between the selected componets to unfold causal interactions.

Moreover, there is a recently proposed technique called 
Identifying Reliable Independent Components via Split-Half Comparisons 
which attempt to detect reliable and stable components across different runs of ICA (due to random initialization of matrices).

Best reagards
Dimitriadis Stavros
PhD candidate in NeuroInformatics, Patras -Thessaloniki Greece


1)Electronics Laboratory, Department of Physics, University of Patras
2)Artificial Intelligence Information Analysis lab Department of Informatics
Aristotle University of Thessaloniki
http://users.auth.gr/~stdimitr/index.html


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Στάλθηκε: 12:00 μ.μ. Δευτέρα, 14 Νοεμβρίου 2011
Θεμα: eeglablist Digest, Vol 85, Issue 4

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Today's Topics:

   1. ICA in clinical populations (Sharma, Anuradha)
Dear All,

I have applied ICA to auditory oddball data in a group of healthy controls and schizophrenia patients to try and isolate the P3b (posterior) and P3f (frontal) components. The problem is that although I get two very clean components in most subjects in the control group, in the patients due to distorted topographies (which leads to different dipole localizations), time courses etc., it has been very hard uptil now to find the corresponding/comparable components in the patient group. Has anybody had a similar problem when using clinical populations, are there any reccommendable clustering strategies to find corresponding components in such populations?

Would be thankful for any tips...

Best,
Anuradha



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