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<div dir="ltr"><font face="Tahoma" color="#000000" size="2">Hi Michael, Elena,</font></div>
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<div dir="ltr"><font face="tahoma" size="2">If it becomes a sticking point, you could also run MaxFilter with the -nosss option. I don't have any experience with how this would affect the beamformer output, but it would at least resolve the rank issue.</font></div>
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<div dir="ltr"><font face="tahoma" size="2">Tim.</font></div>
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<div class="PlainText">---------------------------------------------------------<br>
Timothy Bardouille, PhD, Research Officer<br>
Laboratory for Clinical MEG <br>
NRC Institute for Biodiagnostics (Atlantic)<br>
Office: Halifax Infirmary<br>
3900 - 1796 Summer Street<br>
Halifax, Nova Scotia B3H 3A7<br>
Phone: 902-473-1865</div>
<div class="PlainText"><font face="times new roman">Lab: 902-470-3936 </font><br>
Fax: 902-473-1851<br>
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<font face="Tahoma" size="2"><b>From:</b> fieldtrip-bounces@donders.ru.nl [fieldtrip-bounces@donders.ru.nl] On Behalf Of Michael Wibral [michael.wibral@web.de]<br>
<b>Sent:</b> May 30, 2011 12:58 PM<br>
<b>To:</b> Email discussion list for the FieldTrip project<br>
<b>Subject:</b> Re: [FieldTrip] SAM beamforming on Neuromag data<br>
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Hi Elena,<br>
<br>
as far as I can see from the neuromeg discussion list and the maxfilter papers the properties of the components removed by the maxfilter do not require a leadfield update.<br>
If you run into rank-deficiency issue with the covariance matrix a tiny amount of regularization should fix this.<br>
(Note: If someone who reads this is of a different opinion, please let me know!)<br>
<br>
<br>
Michael<br>
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<b>Von:</b> "Elena Orekhova" <Elena.Orekhova@neuro.gu.se><br>
<b>Gesendet:</b> May 30, 2011 4:30:34 PM<br>
<b>An:</b> "Email discussion list for the FieldTrip project" <fieldtrip@donders.ru.nl><br>
<b>Betreff:</b> Re: [FieldTrip] SAM beamforming on Neuromag data<br>
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Thank you for this.<br>
<br>
I have more concerns. I applied MaxFilter to the data. Since MaxFilter reduces the rank of the covariance matrix by removing noisy components, it may influence the beamformer results.<br>
Is it safe to do beamforming with MaxFiltered data?<br>
<br>
Elena<br>
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<div id="divRpF893554" style="DIRECTION: ltr"><font face="Tahoma" color="#000000" size="2"><b>From:</b> fieldtrip-bounces@donders.ru.nl [fieldtrip-bounces@donders.ru.nl] on behalf of Michael Wibral [michael.wibral@web.de]<br>
<b>Sent:</b> Monday, May 30, 2011 2:08 PM<br>
<b>To:</b> Email discussion list for the FieldTrip project<br>
<b>Subject:</b> Re: [FieldTrip] SAM beamformeing on Neuromag data</font><br>
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<div>Hi Elena,<br>
<br>
as far as I know, the leadfield computation should be aware of the different UNITS (not only scales) of gradiometers and magnetometers. There was a problem with the sign of the leadfields but that should have been fixed.<br>
<br>
There is one more fundamental problem however, that you should be aware of (doesn't invalidate your source analysis but bears potential for fine-tuning), which is the projection of noise:<br>
In beamforming the unit gain constraint guarantees that you get your source signal back with unit gain. Added on top however is neurophysiological crosstalk (minimized) and sensor noise of the sensors with the largest weights in your Beamformer (not reducible).
So different sensor types willhave different (inverse) leadfield strengths, theerfore also fiofefrent source noise levels. the relative benefits of each sensor type changes from location to location, so a location (and data) dependend weighting would in principle
be best.<br>
I am not sure if and how this is implemented if FT (Bayesian weighting would be optimal here..)<br>
<br>
What you could do as a first step is to beam separately and compare the results.<br>
<br>
Michael<br>
<br>
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<b>Von:</b> "Elena Orekhova" <Elena.Orekhova@neuro.gu.se><br>
<b>Gesendet:</b> May 30, 2011 1:08:29 PM<br>
<b>An:</b> "fieldtrip@donders.ru.nl" <fieldtrip@donders.ru.nl><br>
<b>Betreff:</b> [FieldTrip] SAM beamformeing on Neuromag data<br>
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<p class="MsoNormal">Dear All,</p>
<p class="MsoNormal">I try to run beamformer analysis on the auditory MEG data (Neuromag) and have basic questions.</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">1.Magnetometers and gradiometers Neuromag sensors have different scales. Does the Fieldtrip take care of this difference or should I normalize the data?<span>
</span><span> </span>It yes, how to <span> </span>normalize?</p>
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<pre><span style="FONT-SIZE: 12pt; FONT-FAMILY: Cambria">2. I would like to do SAM analysis<span> </span>of evoked field and look at the time courses at ROIs <span> </span>(virtual channels).<span> </span>The only tutorial example I have found was for the lcmv-beamformer
(cfg.method = 'lcmv'; http://fieldtrip.fcdonders.nl/example/lcmv-beamformer). I am not sure
which parameters should I specify in <span style="COLOR: black">ft_sourceanalysis if </span>cfg.method = 'sam'.<span> </span></span>
</pre>
<pre><span style="FONT-SIZE: 12pt; FONT-FAMILY: Cambria"> </span></pre>
<pre><span style="FONT-SIZE: 12pt; FONT-FAMILY: Cambria">I would be most grateful for any example script of this type analysis!</span></pre>
<pre><span style="FONT-SIZE: 12pt; FONT-FAMILY: Cambria"> </span></pre>
<pre><span style="FONT-SIZE: 12pt; FONT-FAMILY: Cambria">Regards, </span></pre>
<pre><span style="FONT-SIZE: 12pt; FONT-FAMILY: Cambria">Elena</span></pre>
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