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<div class="">Dear Andria and Nicole,</div>
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<div class="">Combining sensor types (gradiometers and magnetometers, or MEG and EEG) is indeed something that is not documented, and there is not yet a unanimous agreement in the community about how to do this best.</div>
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<div class="">But, I can suggest one simple solution that was discussed at a recent beamformer workshop hosted by the Aston MEG Centre in February. (I believe John Mosher made the initial suggestion.)</div>
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<div class="">As you probably know, the covariance matrix is an essential ingredient to constructing beamformer solutions. Different sensor types will have different scales and different noise properties, which then complicates the covariance between channels
of different types.</div>
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<div class="">So, one way around this is to zero out the ‘cross’ terms of the covariance matrix, i.e., set to 0 the elements of the covariance matrix that result from the product of a magnetometer channel and a gradiometer channel. This can be accomplished
with something like the following code:</div>
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timelock = ft_timelockanalysis(cfg, data);</div>
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[~,megmagidx]=intersect(data.label,ft_channelselection(<span style="color: #b245f3" class="">'MEGMAG'</span>,data.label));</div>
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[~,meggradidx]=intersect(data.label,ft_channelselection(<span style="color: #b245f3" class="">'MEGGRAD'</span>,data.label));</div>
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timelock.cov(meggradidx,megmagidx)=0;</div>
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timelock.cov(megmagidx,meggradidx)=0;</div>
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<div class="">In my experience, this allows source reconstruction using all channels, with results at least as good as analyzing the channels separately. This is not to say that is the best possible solution, but it is a simple one that should work well!</div>
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<div class="">Best wishes,</div>
<div class="">Sarang</div>
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<blockquote type="cite" class="">
<div class="">On 10 May 2017, at 09:33, Klink-3, N.E.C. van <<a href="mailto:N.vanKlink-2@umcutrecht.nl" class="">N.vanKlink-2@umcutrecht.nl</a>> wrote:</div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class="">Hi Andria,<o:p class=""></o:p></span></div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class=""><o:p class=""> </o:p></span></div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class="">We have been struggling with the same problem, also using a 306-channel Neuromag system. We ended up correcting for the scale difference between
magnetometer and gradiometer signals (i.e. noise 5e-15T vs 5e-13T/m). So we multiplied the signals of the magnetometers and the leadfield values of the magnetometers by a factor 100. This gave us the best result.<o:p class=""></o:p></span></div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class=""><o:p class=""> </o:p></span></div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class="">Kind regards,<o:p class=""></o:p></span></div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class="">Nicole<o:p class=""></o:p></span></div>
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<span lang="EN-US" style="font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(31, 73, 125);" class=""><o:p class=""> </o:p></span></div>
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<span lang="EN-US" class=""><o:p class=""> </o:p></span></div>
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<b class=""><span style="font-size: 10pt; font-family: Tahoma, sans-serif;" class="">Van:</span></b><span style="font-size: 10pt; font-family: Tahoma, sans-serif;" class=""><span class="Apple-converted-space"> </span><a href="mailto:fieldtrip-bounces@science.ru.nl" style="color: purple; text-decoration: underline;" class="">fieldtrip-bounces@science.ru.nl</a><span class="Apple-converted-space"> </span>[<a href="mailto:fieldtrip-bounces@science.ru.nl" style="color: purple; text-decoration: underline;" class="">mailto:fieldtrip-bounces@science.ru.nl</a>]<span class="Apple-converted-space"> </span><b class="">Namens<span class="Apple-converted-space"> </span></b>Andria
Pelentritou<br class="">
<b class="">Verzonden:</b><span class="Apple-converted-space"> </span>10 mei 17 1:52<br class="">
<b class="">Aan:</b><span class="Apple-converted-space"> </span><a href="mailto:fieldtrip@science.ru.nl" style="color: purple; text-decoration: underline;" class="">fieldtrip@science.ru.nl</a><br class="">
<b class="">Onderwerp:</b><span class="Apple-converted-space"> </span>[FieldTrip] combining gradiometer/magnetometer sensors - neuromag data<o:p class=""></o:p></span></div>
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<o:p class=""> </o:p></div>
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Greetings from Melbourne!<o:p class=""></o:p></p>
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We've been using fieldtrip (v-20160801) for a while now and we are having issues when it comes to combining magnetometer and gradiometer MEG sensors types during (LCMV) beamforming for the Elekta Neuromag Triux 306-channel system. Beamforming the magnetometers
or gradiometers separately on auditory evoked data works well, but we cannot successfully combine the two sensor types. We've played with the cfg.coilaccuracy variable as advised in one of the tutorials with no luck as there seem to be no obvious effects on
the output. The documentation provides very little information on the topic.<o:p class=""></o:p></p>
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What strategies would you recommend when trying to combine the Neuromag 306-channel system magnetometer/gradiometer data?<o:p class=""></o:p></p>
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Many thanks in advance for your help and many more thanks for releasing such a valuable toolbox!<br class="">
Warm regards<br class="">
Andria<o:p class=""></o:p></p>
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