<html><head></head><body bgcolor='#FFFFFF' style='font-size:12px;background-color:#FFFFFF;font-family:Verdana, Arial, sans-serif;'>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/><br/><blockquote style="border-left: 2px solid blue; margin-left: 5px; padding-left: 5px; padding-top: 5px;"><hr/><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/><br/><div><div style="direction: ltr; font-family: Tahoma; color: rgb(0, 0, 0); font-size: 10pt;">@font-face { font-family: "\FF2D \FF33 \660E \671D "; }@font-face { font-family: "\FF2D \FF33 \660E \671D "; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: Cambria; }pre { margin: 0cm 0cm 0.0001pt; font-size: 10pt; font-family: Courier; }span.HTMLPreformattedChar { font-family: Courier; }.MsoChpDefault { font-family: Cambria; }div.WordSection1 { page: WordSection1; }@font-face { font-family: "\FF2D \FF33 \660E \671D "; }@font-face { font-family: "Cambria Math"; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: Cambria; }pre { margin: 0cm 0cm 0.0001pt; font-size: 10pt; font-family: Courier; }span.HTMLPreformattedChar { font-family: Courier; }.MsoChpDefault { font-family: Cambria; }div.WordSection1 { page: WordSection1; }<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 style=""> </span><span style=""> </span>It yes, how to <span style=""> </span>normalize?</p><p class="MsoNormal"> </p><pre><span style="font-size: 12pt; font-family: Cambria;">2. I would like to do SAM analysis<span style=""> </span>of evoked field and look at the time courses at ROIs <span style=""> </span>(virtual channels).<span style=""> </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 style=""> </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><p class="MsoNormal"> </p></div></div></blockquote></body></html>