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

[stauros dimitriadis]

Dear Sharma,
Combining two recently proposed papers, the first regarding your task [1] and the second an adopted optimal filter  as a preprocessing step of ICA [2,3], i highly recommend you  to filter  at first single trials from delta to gamma frequency bands and afterward to run ICA for each trial seperately. At a second level, you should estimate inter-trial coherence across ICAs of different trial (see previous e-mail) to reveal in a automatic way related to p300 ICA.Finally, inter-trial coherence should be estimated in a moving window fashion on the detected ICAp300 ( ICA related to p300) starting at an interval before the estimation of peak of p300 (estimated from grand average of ICAp300time courses).  
I have already adopted the preprocessing step of band-pass filtering before applying ICA in a recent study [3].


[1]Exploring the
dynamics of P300 amplitude in patients with schizophrenia.
Almeida PR, Vieira JB, Silveira C, Ferreira-Santos F, Chaves PL, Barbosa F, Marques-Teixeira J.
[2]Cong et al., 2011.Dimension reduction: Additional benefit of an optimal filter for indepedent component analysi to extract event-related potentials Journal of Neuroscience Methods
[3]Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S. What does delta band tell us about cognitive Processes: a mental calculation study.Neuroscience Letters Volume 483, Issue 1 October, 2010 p.11-15.



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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