Brain model

Michael Wibral wibral at BIC.UNI-FRANKFURT.DE
Wed Nov 26 10:47:02 CET 2008


Dear Hu Li, dear all,

as far as I know the covariance matrix of an ICA component should be of rank 1, i.e. it is extremely rank deficient, meaning that LCMV will not work (and also not make sense). 
This is because an ICA component has ONE single timecourse. When projecting it back to the sensor level (i.e. by using the mixing weights aka map) all sensors will have that one timecourse. It is also important to consider that after performing ICA there is no need for one of the main capabilities of LCMV, i.e. to suppress unwanted signals from other sources, as these should be part of other ICA components. So the best option for localizing ICA components should be to use DIPFIT if the map is relatively clearly dipolar, or some distributed linear inverse (like LAURA, s/sw/c-LORETA or the like) when it is not, to localize the component taking the values from the component map.

'Hope this helps,
Michael


> -----Urspr√ľngliche Nachricht-----
> Von: "li hu" <huli at HKUSUA.HKU.HK>
> Gesendet: 25.11.08 14:04:43
> An: FIELDTRIP at NIC.SURFNET.NL
> Betreff: Re: [FIELDTRIP] Brain model


> 
> Hi, All,
> 
> Is LCMV method supported for component data? Is it possible to use 
> the LCMV method for analysising ICA component data?
> 
> Best regards,
> 
> HU Li
> 
> 
> ----------------------------------
> The aim of this list is to facilitate the discussion between users of 
> the FieldTrip toolbox, to share experiences and to discuss new ideas 
> for MEG and EEG analysis.
>  http://listserv.surfnet.nl/archives/fieldtrip.html
> 
>  http://www.ru.nl/fcdonders/fieldtrip/



----------------------------------
The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/fcdonders/fieldtrip.
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