ICA based artifact correction and phase-locking

Christian Hesse c.hesse at FCDONDERS.RU.NL
Fri Feb 23 14:11:41 CET 2007


Hi Markus,

> Because I expected some changes (i.e., I assumed to get rid of some  
> artifactual muscle activity),  now I can not judge whether these  
> results are 'good' or 'bad'. Therefore, I would be interested in  
> the 'slightly longer answer'.

When applying ICA to "real" data (i.e. where we do not really know  
everything about how the observed data are really generated) it is  
actually impossible to tell "objectively" whether removing components  
and then reconstructing the signal leads to "good" or "bad" results -  
it is basically a judgement call. Alternatively, you could use  
indirect arguments, e.g. related to improved ability to discriminate  
between two different conditions or something like that. The only way  
you can tell whether you successfully removed components from data is  
using synthetic data where you have a "ground truth".

(Philosophical / Methodological Aside: the same arguments apply to  
ANY mathematical or statistical method which involves numerical  
estimation of some model (always based on some assumptions) from  
data. Believe it or not, this would include nummerical integration  
techniques. The best you can ever do with numerical techniques is to  
understand their applicability criteria recognize their behaviour  
when these are criteria are not met. In the case of "real" data you  
must judge whether the data to satisfy the applicability criteria  
(big judgement), then brace yourself and jump ... that's as good as  
it ever gets, I am afraid :-)


> Furthermore, currently I do the ICA decomposition on all available  
> segments (minus those with really excessive artifacts, which I took  
> out before running ICA).

Why do you remove these epochs? Do the signals saturate or is there  
excessive electrode(-cap) or subject movement in these?


> This means, my trials are a mixture of clearly contaminated as well  
> as clearly uncontaminated trials. Because my main goal is to  
> identify the eye and muscle artifacts, would it improve the  
> detection of components reflecting artifacts if I do the ICA  
> decomposition only on pre-identified artifactual trials? (My idea  
> is that then the components reflecting artifacts would catch less  
> 'true' brain activity. But perhaps I have a missconception here ...)

The accuracy of the ICA decomposition relies fundamentally on the  
accuracy of the mixing matrix estimate A from which you form the de- 
mixing matrix (or vice versa). So if only some columns of A (e.g.  
artifact source sensor projections) are accurately estimated while  
the sensor projection estimates of brain activity are inaccurate  
(individually or as a subspace), then the de-mixing the data using W  
= inv(A) will not be fully successful in removing artifact activity  
from components reflecting the brain activity (subspace). In order to  
accurately estimate the sensor projections of ALL sources, the data  
needs to comprise example activity from ALL sources, i.e. BOTH  
artifact activity and brain activity.

So since you need to estimate your brain activity (subspace) as  
accurately as your artifact sources, you should use as much of the  
data, i.e. contaminated and clean segments, as possible.

How many EEG channels do you have, by the way? I need to know in  
order to continue with the 'slightly longer answer'

Regards,
Christian



>
> Thank you very much for your help.
>
> Best regards,
> Markus
>
> Christian Hesse schrieb:
>> Hi Markus,
>>
>> the short answer is: if (and only if, big if by the way) ICA  
>> correctly separates artifacts from brain activity, and you  
>> correctly identify the artifact components, removal of the  
>> artifact components from your data does not affect the time- 
>> frequency properties (including phase) of the other components  
>> (i.e. the rest of the data) if you project back to the sensor  
>> level. This is because ICA assumes a linear (instantaneous) mixing  
>> of statistically independent signals.
>>
>> I would just try it out for the  time being - if you get funny  
>> results (or things too good to be true), please get back in touch  
>> as there is also a slightly longer answer.
>>
>> Hope this helps,
>> Christian
>>
>>
>> On 23 Feb 2007, at 12:24, Markus Werkle-Bergner wrote:
>>
>>> Dear all,
>>>
>>> in my studies, I'm investigating early preceptual binding (visual)
>>> across the lifespan (i.e., I have data form children, younger and  
>>> older
>>> adults) with EEG measures. My main interest concerns changes in
>>> gamma-power and measures of phase-synchronization in the gamma  
>>> frequency range(e.g., phase-locking index, n:m (theta:gamma)  
>>> phase synchronization).
>>>
>>> Currently  I use a 'semi-automatic' procedure for artifact  
>>> rejection, i.e., I use thresholding in the time-domain (min/max  
>>> in segment -/+ 100┬ÁV)to 'suggest' contaminated epochs. After that  
>>> I visually inspect the data again for eye-blink and muscle  
>>> activity, and completely reject the contaminated epochs.
>>>
>>> The problem with this procedure is that, especially in the older  
>>> adults group, for many subjects only too few trials remain in the  
>>> final sample.
>>>
>>> Therefore, I thought I could use ICA for artifact correction  
>>> (instead of complete rejection). After identification of the  
>>> components that reflect muscle activity (and also other  
>>> artifacts), I thought to recombine the remaining ICs and perform  
>>> my analyses (power, PLI, n:m synchronization) on the recombined  
>>> (cleaned data).
>>>
>>> Now my question(s): Is there any experience whether removing  
>>> certain ICs
>>> may change the phase spectrum, i.e. may this approach induce some
>>> systematic bias? If there is a systematic bias, are different  
>>> frequency
>>> bands affected differentialy? Could anyone give me some  
>>> references on
>>> these issues?
>>>
>>> Any comments are very much appreciated.
>>>
>>> Best regards,
>>> Markus
>>>
>>> -- 
>>> **************************************************************
>>> Markus Werkle-Bergner, Dipl. Psych.
>>> Predoctoral Research Fellow
>>>
>>> Center for Lifespan Psychology
>>> Max Planck Institute for Human Development
>>> Lentzeallee 94, Room 211, D-14195 Berlin, Germany.
>>> Phone: +49(0)30-82406-447       Fax: +49(0)30-8249939
>>> **************************************************************
>>>
>>
>> --------------------------------------------------------------------- 
>> -
>> Christian Hesse, PhD, MIEEE
>>
>> F.C. Donders Centre for Cognitive Neuroimaging P.O. Box 9101  
>> NL-6500 HB Nijmegen The Netherlands
>>
>> Tel.: +31 (0)24 36 68293
>> Fax: +31 (0)24 36 10989
>>
>> Email: c.hesse at fcdonders.ru.nl <mailto:c.hesse at fcdonders.ru.nl>
>> Web: www.fcdonders.ru.nl <http://www.fcdonders.ru.nl>
>> --------------------------------------------------------------------- 
>> -
>>
>>
>>
>>
>
>
> -- 
> **************************************************************
> Markus Werkle-Bergner, Dipl. Psych.
> Predoctoral Research Fellow
>
> Center for Lifespan Psychology
> Max Planck Institute for Human Development
> Lentzeallee 94, Room 211, D-14195 Berlin, Germany.
> Phone: +49(0)30-82406-447       Fax: +49(0)30-8249939
> **************************************************************
>

----------------------------------------------------------------------
Christian Hesse, PhD, MIEEE

F.C. Donders Centre for Cognitive Neuroimaging
P.O. Box 9101
NL-6500 HB Nijmegen
The Netherlands

Tel.: +31 (0)24 36 68293
Fax: +31 (0)24 36 10989

Email: c.hesse at fcdonders.ru.nl
Web: www.fcdonders.ru.nl
----------------------------------------------------------------------




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