[Eeglablist] Two step source connectivity analysis (as implemented in SIFT)

[Shou, Guofa]

Hi, All,
   Yes, I recently also have this kind of confusion even without the source localization, but only the connectivity. For example, whether it is valid to calculation coherence, some phase lag index or CC of powers between two ICs. I know in the 
Original paper of EEGLAB in JNM, they also mentioned about the inter-IC phase coherence. It seems we still can do, but whether it's against the assumption of independence between ICs.
   I look forward to hear some comments.
Shou

Guofa Shou
Computational Neuroengineering and Neuroimaging Lab
School of Electrical and Computer Engineering
University of Oklahoma
405-496-8661



-----Original Message-----
From: eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Bethel Osuagwu
Sent: Monday, February 10, 2014 6:58 AM
To: IMALI THANUJA HETTIARACHCHI; mullen.tim at gmail.com
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)

Hi
I am not an expert but I just want to give my own opinion! 

I do not think that temporal independence of two variables (A and B) violets causality between them as implemented in SIFT. In fact if  say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find that A is maximally  temporaly independent from B. However we know there is causal flow from A to B.

This is what I think, but I wait to be corrected so that I can learn!

Thanks
Bethel
________________________________________
From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu] On Behalf Of IMALI THANUJA HETTIARACHCHI [ith at deakin.edu.au]
Sent: 07 February 2014 01:27
To: mullen.tim at gmail.com
Cc: eeglablist at sccn.ucsd.edu
Subject: [Eeglablist] Two step source connectivity analysis (as implemented     in SIFT)

Hi Tim and the list,

I am just in need of a clarification regarding the ICA source reconstruction and the subsequent MVAR -based effective connectivity analysis using the components, which is the basis of the SIFT toolbox. I was trying to use this approach in my work but was questioned on the validity using ICA and subsequent MVAR analysis by my colleagues.

"When using independent component analysis (ICA), we assume the mutual independence of underlying sources, however when we try to estimate connectivity between EEG sources, we implicitly assume that the sources may be  influenced by each other. This contradicts the fundamental assumption of mutual independence between sources in ICA [Cheung et al., 2010, Chiang et al., 2012, Haufe et al., 2009 ]. "

So due to this reason different approaches such as MVARICA, CICAAR(convolution ICA+MVAR),  SCSA and state space-based methods have been proposed as ICA+MVAR based source connectivity analysis techniques.


*         So, how would you support the valid use of SIFT ( ICA+MVAR as a two-step procedure) for the source connectivity analysis?


*         If I argue that I do not assume independent sources but rely on the fact that ICA will decompose the EEG signals and output 'maximally independent' sources and then, I subsequently model for the dependency, will you agree with me? How valid would my argument be?

It would be really great to see different thoughts and opinions.

Kind regards

Imali


Dr. Imali Thanuja Hettiarachchi
Researcher
Centre for Intelligent Systems research
Deakin University, Geelong 3217, Australia.

Mobile : +61430321972
Email: ith at deakin.edu.au<mailto:ith at deakin.edu.au>
Web :www.deakin.edu.au/cisr<http://www.deakin.edu.au/cisr>

[cid:image001.jpg at 01CF23FF.F8259940]






_______________________________________________
Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu