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

[Makoto Miyakoshi]

Dear Tim,

Why don't you comment on the following question: If independent components
are truly independent, how do causality analyses work?

Dear Joe,

Your inputs are too difficult for me to understand. In short, are you
saying causality analysis works on independent components because they are
not completely independent?

Makoto


2014-02-18 15:46 GMT-08:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:

> Dear Bethel,
>
> > say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find
> that A is maximally  temporaly independent from B.
>
> ICA would find a correlation between sunrise and ice-cream-sale.
>
> Makoto
>
> 2014-02-10 4:57 GMT-08:00 Bethel Osuagwu <b.osuagwu.1 at research.gla.ac.uk>:
>
> 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]
>>
>>
>>
>>
>>
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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