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<p>Hello All,<br>
</p>
<p><br>
</p>
<p>So once I have calculated the preprocessing data, timelock data, headmodel, lead field and source.<br>
</p>
<p><br>
</p>
<p>I have the following code to create virtual EEG channels of data (similar to the tutorial):<br>
</p>
<div><span> </span><span> </span><span> </span>% find sensor names and indices</div>
<div> chans = ft_channelselection( 'EEG', data.label);</div>
<div> chans = match_str( data.label, chans);</div>
<div> </div>
<div> % compute virtual channels</div>
<div> virtual_chans = [];</div>
<div> virtual_chans.label = data.label;</div>
<div> virtual_chans.time = data.time;</div>
<div> virtual_chans.trial = source.avg.filter{ 1} * timelock.trial{ 1}(chans,:);<br>
</div>
<div><br>
</div>
<div>However as I have 642 source.avg.filter, which filter do I chose? Or do I calculate EEG channels for each voxel I landed with (I only had one trial).<br>
</div>
<div><br>
</div>
<div>Btw, the source was calculated with the following code:<br>
</div>
<div><br>
</div>
<div><strong></strong>
<div><span> </span><span> </span><span> </span>cfg = [];</div>
<div><span style="font-size: 12pt;"> cfg.method = 'lcmv';</span><br>
</div>
<div> cfg.grid.inside = grid.inside;</div>
<div> cfg.grid.outside = [];</div>
<div> cfg.vol = vol;</div>
<div><span style="font-size: 12pt;"> cfg.keepfilter = 'yes';</span><br>
</div>
<div><span style="font-size: 12pt;"> source = ft_sourceanalysis( cfg, timelock);</span><br>
</div>
<div> <br>
</div>
Thankyou for all the help you have provided me thus far.<br>
</div>
<div><br>
</div>
<div>tyler<br>
</div>
<div> <br>
</div>
<p><br>
</p>
<div> <br>
</div>
<p><br>
</p>
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<p><br>
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<div style="font-size:13px">*************************
<div style="font-family:Tahoma"><br>
</div>
<div><font face="Arial"><i>Tyler Grummett ( BBSc, BSc(Hons I))</i></font></div>
<div><font face="Arial"><i>PhD Candidate</i></font></div>
<div><font face="Arial"><i>Brain Signals Laboratory</i></font></div>
<div><font face="Arial"><i>Flinders University</i></font></div>
<div><font face="Arial"><i>Rm 5A301</i></font></div>
<div><font face="Arial"><i>Ext 66124</i></font></div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" color="#000000" style="font-size:11pt"><b>From:</b> fieldtrip-bounces@science.ru.nl <fieldtrip-bounces@science.ru.nl> on behalf of jan-mathijs schoffelen <jan.schoffelen@donders.ru.nl><br>
<b>Sent:</b> Friday, 4 April 2014 7:02 PM<br>
<b>To:</b> FieldTrip discussion list<br>
<b>Subject:</b> Re: [FieldTrip] spatial and temporal ICA after beamformer</font>
<div> </div>
</div>
<div>Hi all,
<div><br>
<div>Allow me to chime in here. As I understand it Tyler wants to do ICA on the output of an LCMV beamformer. This is in general allowed, and can be done if you know how to handle the data. In Fieldtrip there is no pre-cooked way of doing it. What you need
is to fool ft_componentanalysis into believing that your source reconstructed data is of the type 'raw'. To get started, you can have a look at <a href="http://fieldtrip.fcdonders.nl/tutorial/shared/virtual_sensors">http://fieldtrip.fcdonders.nl/tutorial/shared/virtual_sensors</a>.
In this particular tutorial, only a few 'virtual channels' are created. You probably want many of those, defined on a 3D grid. The only constraint there is probably the amount of RAM on your computer. Note that you want to do (and will do) temporal ICA in
this case. Also, note that you probably should pre-PCA your data (this will be a parameter in the specs for the options for the ICA algorithm you are going to use). If you want to apply the ICA algorithm on amplitude envelope timeseries of band-limited activity,
rumour has it that the fastica method works quite well.</div>
<div><br>
</div>
<div>Cheers,</div>
<div>Jan-Mathijs</div>
<div><br>
<div>
<div>On Apr 4, 2014, at 10:17 AM, Jörn M. Horschig wrote:</div>
<br class="Apple-interchange-newline">
<blockquote type="cite">
<div>Hi Tyler,<br>
<br>
sorry, I realized too late that you want to use LCMV on ICA components - mostly because your approach resulted in many different erros ;). Afaik, it is not possible to use beamforming on ICA components. On Wednesday we talked about this in our weekly FieldTrip
meeting and identified some occasions where FieldTrip is not handling IC-data well. This can lead to unexpected results and missing warnings or errors. However, you can use dipole modelling (like EEGlab does) or MNE if you want to. These should work with ICs.<br>
<br>
Any reason why you want to use LCMV on ICs? In theory, both steps constitute a spatial filter, so you can try to be smart and first to LCMV on all your channel level data, and then apply the LCMV spatial filter on the individual backprojected ICs. Somewhat
a complicated procedure, but I think it would work?<br>
<br>
Best,<br>
Jörn<br>
<br>
<br>
<br>
On 4/4/2014 7:23 AM, Tyler Grummett wrote:<br>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">Hello fieldtrip,<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">I was wondering whether it was possible in the fieldtrip toolbox to run a spatial and temporal ICA on data after it has been run through ft_sourceanalysis (We can a LCMV beamformer on EEG data that had been timelocked).<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">Currently it is saying that I need raw data for it to be processed.<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">Tyler<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">*************************<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">/Tyler Grummett ( BBSc, BSc(Hons I))/<br>
</blockquote>
<blockquote type="cite">/PhD Candidate/<br>
</blockquote>
<blockquote type="cite">/Brain Signals Laboratory/<br>
</blockquote>
<blockquote type="cite">/Flinders University/<br>
</blockquote>
<blockquote type="cite">/Rm 5A301/<br>
</blockquote>
<blockquote type="cite">/Ext 66124/<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">_______________________________________________<br>
</blockquote>
<blockquote type="cite">fieldtrip mailing list<br>
</blockquote>
<blockquote type="cite"><a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br>
</blockquote>
<blockquote type="cite"><a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br>
</blockquote>
<br>
<br>
-- <br>
Jörn M. Horschig<br>
PhD Student<br>
Donders Institute for Brain, Cognition and Behaviour<br>
Centre for Cognitive Neuroimaging<br>
Radboud University Nijmegen<br>
Neuronal Oscillations Group<br>
FieldTrip Development Team<br>
<br>
P.O. Box 9101<br>
NL-6500 HB Nijmegen<br>
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Tel: +31-(0)24-36-68493<br>
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<div>Jan-Mathijs Schoffelen, MD PhD </div>
<div><br>
</div>
<div>Donders Institute for Brain, Cognition and Behaviour, <br>
Centre for Cognitive Neuroimaging,<br>
Radboud University Nijmegen, The Netherlands</div>
<div><br>
</div>
<div>Max Planck Institute for Psycholinguistics,</div>
<div>Nijmegen, The Netherlands</div>
<div><br>
</div>
<div><a href="mailto:J.Schoffelen@donders.ru.nl">J.Schoffelen@donders.ru.nl</a></div>
<div>Telephone: +31-24-3614793</div>
<div><br>
</div>
<div><a href="http://www.hettaligebrein.nl">http://www.hettaligebrein.nl</a></div>
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