[FieldTrip] Fwd: Beamformer confusion

[Herring, J.D. (Jim)]

Hi Tyler,

Although I cannot comment on whether or not something went wrong, as far as 
I know you shouldn't use single-sphere models in EEG as they do not take 
into account the difference in conductivity of the various tissue types. At 
least use a triple sphere model or preferably a BEM of FEM model.

Best,

Jim

-----Original Message-----
From: fieldtrip-bounces at science.ru.nl 
[mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Eelke Spaak
Sent: vrijdag 27 juni 2014 7:42
To: Email discussion list for the FieldTrip project
Subject: [FieldTrip] Fwd: Beamformer confusion

PS: Forwarding your figure to the list.


---------- Forwarded message ----------
From: Eelke Spaak <eelke.spaak at donders.ru.nl>
Date: 27 June 2014 07:40
Subject: Re: [FieldTrip] Beamformer confusion
To: Email discussion list for the FieldTrip project 
<fieldtrip at donders.ru.nl>


Hi Tyler,

First, please send your messages to the list, so that others may benefit 
(now or in the future through the archives).

Then, regarding your plot, indeed I think there might be something wrong 
(though I am not sure). You are looking for a proper alignment between the 
sensors (I have colored them red to be able to better see), the source model 
(grid points for beamforming) and the volume conductor model (translucent 
mesh; sphere in your case). I have never used a single sphere model myself, 
so I cannot really comment on whether the sphere aligns properly. However, I 
would suspect not, since there are grid points located outside the sphere. 
Also, the sensors might not be properly located, some are intersecting the 
body of grid points.

But I do seem to recall that sensors are automatically projected to the 
volume conductor surface in the case of EEG. Maybe someone who has EEG 
source modelling experience can comment on this (and your figure)?
I usually only work with MEG, so I don't have the same intuition for your 
kind of plot as with an analogous plot for MEG.

The following tutorial might be very helpful for you, by the way:
http://fieldtrip.fcdonders.nl/tutorial/headmodel_eeg

Best,
Eelke

On 27 June 2014 03:06, Tyler Grummett <tyler.grummett at flinders.edu.au> 
wrote:
> Hello Eelke,
>
> Thank you for replying to my email.
>
> I have attached an image with the requested sourcemodel positions and 
> sensor positions.
>
> I dont really know how to interpret the figures to be honest.
>
> I have attached a .fig figure so that you can have a play with it.
>
> Tyler
>
> *************************
>
> Tyler Grummett ( BBSc, BSc(Hons I))
> PhD Candidate
> Brain Signals Laboratory
> Flinders University
> Rm 5A301
> Ext 66124
>
> ________________________________________
> From: fieldtrip-bounces at science.ru.nl
> <fieldtrip-bounces at science.ru.nl> on behalf of Eelke Spaak
> <eelke.spaak at donders.ru.nl>
> Sent: Thursday, 26 June 2014 11:17 PM
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] Beamformer confusion
>
> Hi Tyler,
>
> You write that you end up with the same result "whatever data you
> process", so I suspect the raw EEG/MEG data is not to blame.
>
> Rather, I suspect something is wrong with your leadfield or one of its
> ingredients: the volume conductor model, source model, or
> electrode/gradiometer definitions. It's probably a good idea to check
> whether these three ingredients all line up. Could you plot them in
> one set of axes, as described at the end of this section:
> http://fieldtrip.fcdonders.nl/tutorial/beamformingextended#computing_t
> he_sourcemodel
> ?
>
> Best,
> Eelke
>
> On 26 June 2014 10:00, Tyler Grummett <tyler.grummett at flinders.edu.au> 
> wrote:
>> Hello Fieldtrip,
>>
>>
>> I have been using the LCMV beamformer in fieldtrip for some time now,
>> so I have experience with it.
>>
>>
>> I am facing an issue where whatever data I process I get an image
>> identical to attachment 'sourceplot.png'. We have tried visual SSR,
>> memory tasks, tactile tasks etc.
>>
>> Also, whatever I do with the data, the same issue occurs (filtering etc).
>>
>>
>> Attachment 'original_data.png' shows the data before going into the
>> beamformer, and 'virtual_data.png' shows the data after it has run
>> through beamformer.
>>
>>
>> We have tried filtering in every possible combination ie lowpass =
>> 40/30/20, highpass = 1/10/20, we even did a highpass of 100 and it
>>
>> was still identical.
>>
>>
>> We dont think this is a fault of the beamformer, but we cant work out
>> how to get rid of the issue. It is overpowering the other data.
>>
>>
>> We have also had a look at some spectra of the brain region with the
>> high power and a brain region that doesnt have the high power and it
>> appears
>>
>> as though there is higher power over all frequencies.
>>
>>
>> Before asking:
>>
>> -There isnt any muscle, the data was recorded from a paralysed person.
>>
>> -We have tried it on CAR'd data and data that hasnt been CAR'd
>>
>>
>> We are all out of ideas.
>>
>>
>> Kind regards,
>>
>>
>> Tyler
>>
>>
>> *************************
>>
>> Tyler Grummett ( BBSc, BSc(Hons I))
>> PhD Candidate
>> Brain Signals Laboratory
>> Flinders University
>> Rm 5A301
>> Ext 66124
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> _______________________________________________
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