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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span lang=NL style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>Hi Stephen,<o:p></o:p></span></p><p class=MsoNormal><span lang=NL style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>Approach 1 and 3 would be fine, 2 would be not a wise thing to do, as you get different noise estimates for the two periods (presumably). So beamform all your data that will go into the final analysis. Instead of computing some noise estimate you can also normalize the leadfields. According to Robert that should also work fairly fine, though I haven’t tested that myself. I would go for beamforming the baseline and the activation period (that’s because that’s what I already did earlier).<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>For getting the time courses, Eelke and me made an extended beamforming tutorial based on the existing tutorials, which featured virtual channel extraction. I just checked, and it can be found here:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><a href="http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors">http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors</a><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>So basically, yes, manually multiply the data with the filter. I guess that should get you started. <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>btw, not sure what you are doing wrong – maybe share the code you’re using? Or check the extended beamforming tutorial, there ft_redefinetrial is used: <a href="http://www.fieldtriptoolbox.org/tutorial/beamformingextended#time_windows_of_interest">http://www.fieldtriptoolbox.org/tutorial/beamformingextended#time_windows_of_interest</a><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>Hope this helps!<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>Best,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'>Jörn<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><div><p class=MsoNormal><b><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#626264;mso-fareast-language:NL'>--<o:p></o:p></span></b></p><p class=MsoNormal><b><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#255A9E;mso-fareast-language:NL'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#255A9E;mso-fareast-language:NL'>Jörn M. Horschig, PhD</span></b><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#626264;mso-fareast-language:NL'>, Software Engineer</span><b><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:black;mso-fareast-language:NL'><o:p></o:p></span></b></p><p class=MsoNormal><span lang=NL style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#626264;mso-fareast-language:NL'><a href="http://www.artinis.com/"><span lang=EN-US style='color:#626264'>Artinis Medical Systems</span></a></span><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#626264;mso-fareast-language:NL'> | +31 481 350 980 </span><b><span style='font-size:9.0pt;font-family:"Verdana",sans-serif;color:#255A9E;mso-fareast-language:NL'><o:p></o:p></span></b></p></div><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D'><o:p> </o:p></span></p><div style='border:none;border-left:solid blue 1.5pt;padding:0in 0in 0in 4.0pt'><div><div style='border:none;border-top:solid #E1E1E1 1.0pt;padding:3.0pt 0in 0in 0in'><p class=MsoNormal><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'>From:</span></b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'> fieldtrip-bounces@science.ru.nl [mailto:fieldtrip-bounces@science.ru.nl] <b>On Behalf Of </b>Stephen Whitmarsh<br><b>Sent:</b> Friday, July 17, 2015 12:04 AM<br><b>To:</b> fieldtrip, donders<br><b>Subject:</b> [FieldTrip] Advice on using LCMV beamformer<o:p></o:p></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><div><div><div><div><div><div><div><p class=MsoNormal style='margin-bottom:12.0pt'>Hi everyone,<o:p></o:p></p></div><p class=MsoNormal style='margin-bottom:12.0pt'>I hope there is someone out there who has some time to help me with using LCMV beamformer. I have no experience with it yet, although a little with DICS, and understand they are at least conceptually very similar. However, I am running into some questions. Before I go on, my data on sensor level looks good and it works out with dipolefitting as well (attached).<o:p></o:p></p></div><p class=MsoNormal>I would like to beamform an early (unilaterally stimulated) sensory evoked field. There is no conditional difference, I would just like to extract the timecourse of some maximum voxels. In other words:<o:p></o:p></p></div><p class=MsoNormal>1) get a contralateral cluster or a number of voxels with maximum amplitude.<o:p></o:p></p></div><p class=MsoNormal style='margin-bottom:12.0pt'>2) extract the timecourses of those clusters, then average them.<o:p></o:p></p></div><p class=MsoNormal>For step 1, it seem to have several options, either:<o:p></o:p></p></div><p class=MsoNormal>1a) calculate the filter on the whole timecourse (a common filter as would be used in DICS)<o:p></o:p></p><div><div><p class=MsoNormal>1b) use that common filter to calculate the amplitude of the early component<o:p></o:p></p></div><div><p class=MsoNormal>1c) do the same for the baseline period<o:p></o:p></p></div><div><p class=MsoNormal style='margin-bottom:12.0pt'>1d) subtract the baseline activation from the active period (or take a ratio)<o:p></o:p></p></div><div><p class=MsoNormal>Alternatively, I could:<o:p></o:p></p></div><div><p class=MsoNormal>2a) beamform the active period<o:p></o:p></p></div><div><p class=MsoNormal>2b) beamform the baseline period<o:p></o:p></p></div><div><p class=MsoNormal style='margin-bottom:12.0pt'>2c) subtract the baseline activation from the active period (or take a ratio)<o:p></o:p></p></div><div><p class=MsoNormal>Or it seems, I might even:<o:p></o:p></p></div><div><p class=MsoNormal>3a) beamform the active period<o:p></o:p></p></div><div><p class=MsoNormal style='margin-bottom:12.0pt'>3b) divide the active period by the projected noise<o:p></o:p></p></div><div><p class=MsoNormal>Do I get this correctly? Does anyone have an argument for one of these option?<br><br>Anyway, first practical problem is that when I plot either the whole, the baseline or the activation period I get the same results (attached). A difference therefore doesn't work, obviously. So this raises the question how the selection of latency works in ft_sourceanalysis? I trust cfg.latency works, but it doesn't seem to make a difference for me. At the same time, using selectdata to select a time period doesn't make a difference either. I'm obviously doing something wrong, but what?<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><p class=MsoNormal style='margin-bottom:12.0pt'>Anyway, when I get this figured out, I still need to extract the timecourses. How would I proceed doing this? I imagine the fastest way would not be to loop ft_sourceanalysis for each timepoint (using a common filter I would think). Should I 'manually' multiply the data with the filter?<o:p></o:p></p></div><div><p class=MsoNormal style='margin-bottom:12.0pt'>It would be great to have some feedback on the general idea, and perhaps someone can identify what I am doing wrong (script below). I would be happy to finish the LCMV beamformer tutorial on the FT website once I get it figured out, as I can imagine more people would have these questions.<o:p></o:p></p></div><div><p class=MsoNormal>All the best and thanks,<o:p></o:p></p></div><div><p class=MsoNormal>Stephen<br><span style='font-size:7.5pt;font-family:"Courier New"'><br><br>% timelock average + covariance<br><br>cfg = [];<br>cfg.covariance = 'yes';<br>cfg.covariancewindow = 'all';<br>cfg.trials = find(clean_data.trialinfo(:,2) == 1);<br>ERF_single{isubject} = ft_timelockanalysis(cfg,clean_data);<br>cfg.trials = find(clean_data.trialinfo(:,2) == 2);<br>ERF_double{isubject} = ft_timelockanalysis(cfg,clean_data);<br><br>% combine gradiometers (single stim)<br>cfg = [];<br>ERF_single_cmb{isubject} = ft_combineplanar(cfg,ERF_single{isubject});<br>ERF_double_cmb{isubject} = ft_combineplanar(cfg,ERF_double{isubject});<br><br>% load headmodel MEG<br>temp = load(['/home/stephen/analysis/metacognition/sourcemodel/pp_' num2str(isubject) '_headmodel_singleshell.mat']);<br>headmodel_meg{isubject} = temp.headmodel_meg;<br><br>% headmodel_meg = ft_convert_units(headmodel_meg, 'cm');<br>gridtemp = load(['/home/stephen/analysis/metacognition/sourcemodel/pp_' num2str(isubject) '_grid.mat']);<br><br>hdr = ft_read_header(flist{1});<br>cfg = [];<br>cfg.grad = hdr.grad;<br>cfg.vol = headmodel_meg{isubject};<br>cfg.grid = gridtemp.grid;<br>cfg.channel = {'MEGGRAD'};<br>cfg.normalize = 'yes';<br>leadfield = ft_prepare_leadfield(cfg);<br><br>% get the filter<br>cfg = [];<br>cfg.method = 'lcmv';<br>cfg.grid = leadfield;<br>cfg.vol = headmodel_meg{isubject};<br>cfg.keepfilter = 'yes';<br>cfg.senstype = 'meg';<br>cfg.channel = 'meggrad';<br>cfg.lcmv.fixedori = 'yes'; % use only right gradiometers<br>cfg.reducerank = 2;<br>source_lcmv_single{isubject} = ft_sourceanalysis(cfg, ERF_single{isubject});<br>source_lcmv_double{isubject} = ft_sourceanalysis(cfg, ERF_double{isubject});<br><br>% project post stim latency with previous common filter<br><br>cfg = [];<br>cfg.latency = latency;<br>ERF_single_latency{isubject} = ft_selectdata(cfg,ERF_single{isubject});<br><br>cfg = [];<br>cfg.method = 'lcmv';<br>cfg.grid = leadfield;<br>cfg.vol = headmodel_meg{isubject};<br>cfg.grid.filter = source_lcmv_single{isubject}.avg.filter;<br>cfg.channel = 'meggrad';<br>cfg.senstype = 'meg';<br>% cfg.latency = latency;<br>cfg.lcmv.fixedori = 'yes'; <br>cfg.reducerank = 2;<br>source_lcmv_single_latency{isubject} = ft_sourceanalysis(cfg, ERF_single_latency{isubject});<br><br>% baseline<br><br>cfg = [];<br>cfg.latency = [-0.2 0];<br>ERF_single_baseline{isubject} = ft_selectdata(cfg,ERF_single{isubject});<br><br>cfg = [];<br>cfg.method = 'lcmv';<br>cfg.grid = leadfield;<br>cfg.vol = headmodel_meg{isubject};<br>cfg.grid.filter = source_lcmv_single{isubject}.avg.filter;<br>cfg.channel = 'meggrad';<br>cfg.senstype = 'meg';<br>% cfg.latency = latency;<br>cfg.lcmv.fixedori = 'yes'; <br>cfg.reducerank = 2;<br>source_lcmv_single_baseline{isubject} = ft_sourceanalysis(cfg, ERF_single_baseline{isubject});<br><br>% get difference between baseline and component<br>source_lcmv_single_diff{isubject} = source_lcmv_single_latency{isubject};<br>source_lcmv_single_diff{isubject}.avg.pow = source_lcmv_single_latency{isubject}.avg.pow - source_lcmv_single_baseline{isubject}.avg.pow;<br><br>template_mri = ft_read_mri(['/opt/fieldtrip/external/spm8/templates/T1.nii']);<br><br>cfg = [];<br>cfg.voxelcoord = 'no';<br>cfg.parameter = 'avg.pow';<br>cfg.interpmethod = 'nearest';<br>source_lcmv_single_int{isubject} = ft_sourceinterpolate(cfg, source_lcmv_single{isubject}, template_mri);<br>source_lcmv_single_latency_int{isubject} = ft_sourceinterpolate(cfg, source_lcmv_single_latency{isubject}, template_mri);<br>source_lcmv_single_diff_int{isubject} = ft_sourceinterpolate(cfg, source_lcmv_single_diff{isubject}, template_mri);<br><br>cfg = [];<br>cfg.method = 'slice';<br>cfg.funparameter = 'avg.pow';<br>cfg.maskparameter = cfg.funparameter;<br>cfg.funcolorlim = [3e-22 5e-22];<br>% ft_sourceplot(cfg,source_lcmv_single_latency_int{isubject});<br>% ft_sourceplot(cfg,source_lcmv_single_int{isubject});<br>ft_sourceplot(cfg,source_lcmv_single_latency_int{isubject});<br><br>cfg = [];<br>cfg.method = 'slice';<br>cfg.funparameter = 'pow';<br>cfg.funcolorlim = [-1e-23 1e-23];<br>ft_sourceplot(cfg,source_lcmv_single_diff_int{isubject});</span><span style='font-size:7.5pt'><br> </span><o:p></o:p></p></div><div><p class=MsoNormal style='margin-bottom:12.0pt'><o:p> </o:p></p></div><div><div><p class=MsoNormal><o:p> </o:p></p></div></div></div></div></div></body></html>