Combining different MEG sensortypes

[Robert Oostenveld]

Hi Tolga and Michael,

This reminds me of a bug that was in the code "once upon a time". That bug caused a sign difference on the gradiometer and magnetometers in the leadfield. Now hearing this problem, I am not 100% sure whether that bug has been resolved. 

If that bug still lingers in the code somehow, it might explain the results of each seperate being better than the combined reconstruction (because the combined channels would result in inconsistent/flipped source orientations). Do you perhaps have a neuromag phantom dataset, i.e. real data with the correct field distribution of a simple source? If so, then you can fit the source (using ft_dipolefitting) with "mag only" and with "grad only" and compare the dipole orientation. Or fit with mag only, compute the leadfield on mag and grad, and compare the computed leadfield with the true data.

best
Robert


On 24 Nov 2010, at 12:07, Michael Wibral wrote:

> Hi Tolga,
> 
> very interesting to hear of your results and a bit disspointing to hear that "grad only" or "mag only" performed better for your case than the combination. Let me know if you make any progress on this.
> The Brookes paper I was referring to is on noise reduction in recostructed source timecourse from EEG acquired with concurrent fMRI (Fig. 4 is what I was referring to):
> 
> Source localisation in concurrent EEG/fMRI: Applications at 7T
> Matthew J. Brookes, Jiri Vrba b Karen J. Mullinger , Gerða Björk Geirsdóttir , Winston X. Yan ,
> Claire M. Stevenson , Richard Bowtell , Peter G. Morris 
> 
> Michael
> 
> -----Ursprüngliche Nachricht-----
> Von: "Tolga Özkurt" <tolgacan1 at YAHOO.COM>
> Gesendet: Nov 23, 2010 3:58:10 PM
> An: FIELDTRIP at NIC.SURFNET.NL
> Betreff: Re: [FIELDTRIP] Combining different MEG sensortypes
> 
>> This had also been a question in my mind for a while.
>> Hey Michael,
>> 
>> 
>> This had also been a question in my mind for a while.
>> 
>> As you say so, magnetometers and gradiometers have different noise levels and 
>> obviously different units; I believe the magnetoemeters are wegihted by some 
>> value like "100" in Maxwell Filtering (for SSS decomposition) to avoid the 
>> singularity in the gain matrix. However, when I tried the same weigthing for 
>> beamforming algorithm in the Fieldtrip toolbox for a real data experiment, I 
>> could not get a good performance when I compared the localization results to the 
>> results obtained with "only gradiometers" or "only magnetometers". That is, 
>> weighting 100 was not optimal; although the results was much better than the 
>> lozalization result "with no weighting" at all. This means some optimal 
>> weighting required.
>> 
>> I suppose it makes sense to use the fabric noise levels of the sensors while 
>> weighting them. There is also a way suggested by by Henson et. al. (2009) that 
>> uses a Bayesian scheme to obtain optimal noise estimates, although I did not 
>> attempt to work into that approach yet. 
>> 
>> 
>> By the way, could you tell me the date and title of the "the recent paper by 
>> Matthew Brookes" you mentioned? It sounds like an interesting one.
>> 
>> Regards,
>> 
>> Tolga  
>> 
>> 
>> 
>> 
>> 
>> 
>> ----- Original Message ----
>> From: Michael Wibral <michael.wibral at WEB.DE>
>> To: FIELDTRIP at NIC.SURFNET.NL
>> Sent: Mon, November 22, 2010 7:54:44 PM
>> Subject: [FIELDTRIP] Combining different MEG sensortypes
>> 
>> Dear Fieldtrip users (with a Neuromag system),
>> 
>> I have a question on how to combine the Information from the planar gradiometers 
>> and the magnetometers of a 306 channel Neurmag system best for beamformer weight 
>> computation and source time course reconstruction. Do you compute a complete 
>> leadfield mixing both types of gradiometers (i.e. you do an unweighted 
>> analysis)? Do you somehow weight the sensors for their different noise levels? 
>> Do you compute two sets of timecourses (one from grads, one from megnetometers)?
>> A related question: Do you update the leadfileds for projections that 
>> MAxfiltering does (like it should be done when using ICA)?
>> 
>> I am asking because it has been shown that the more sensors are available the 
>> better the time course reconstruction (a recent paper by Matthew Brookes). Hence 
>> it would be a pity to have to throw some of the information away.
>> 
>> Michael
>> 
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>> and to discuss  new ideas for MEG and EEG analysis.
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>> and http://www.ru.nl/neuroimaging/fieldtrip.
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>> 
>> 
>> 
>> 
>> ---------------------------------------------------------------------------
>> You are receiving this message because you are subscribed to
>> the  FieldTrip list. 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/neuroimaging/fieldtrip.
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> 
> ---------------------------------------------------------------------------
> You are receiving this message because you are subscribed to
> the  FieldTrip list. 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/neuroimaging/fieldtrip.
> ---------------------------------------------------------------------------<Michael Wibral.vcf>

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and http://www.ru.nl/neuroimaging/fieldtrip.
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