<div dir="ltr"><div>Thank you very much, Eelke.</div><div><br></div><div>Best,</div><div>Helena<br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Eelke Spaak <<a href="mailto:e.spaak@donders.ru.nl">e.spaak@donders.ru.nl</a>> escreveu no dia terça, 9/06/2020 à(s) 12:59:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Dear Helena,<br>
<br>
Yes, you can safely ignore the warning.<br>
<br>
Best,<br>
Eelke<br>
<br>
On Tue, 9 Jun 2020 at 12:14, Helena Pereira<br>
<<a href="mailto:hri.pereira@campus.fct.unl.pt" target="_blank">hri.pereira@campus.fct.unl.pt</a>> wrote:<br>
><br>
> Dear Eelk,<br>
><br>
> Perhaps everything is ok, and I can ignore the warning. The structure of my data is as follows (and contains the components that you mentioned: e.g. .topolabel), or it should also appear in the component analysis?<br>
><br>
> ica_cleaned =<br>
> struct with fields:<br>
> fsample: 512<br>
> time: {1×100 cell}<br>
> trial: {1×100 cell}<br>
> topo: [69×69 double]<br>
> unmixing: [69×69 double]<br>
> label: {69×1 cell}<br>
> topolabel: {69×1 cell}<br>
> sampleinfo: [100×2 double]<br>
> trialinfo: [100×1 double]<br>
> cfg: [1×1 struct]<br>
><br>
> Thank you for your time and help.<br>
><br>
> Best,<br>
> Helena Pereira<br>
><br>
><br>
> Helena Pereira <<a href="mailto:hri.pereira@campus.fct.unl.pt" target="_blank">hri.pereira@campus.fct.unl.pt</a>> escreveu no dia terça, 9/06/2020 à(s) 09:38:<br>
>><br>
>> Dear Eelk,<br>
>><br>
>> Yes, 'ica_cleaned' is produced directly from ft_componentanalysis:<br>
>><br>
>> cfg = [];<br>
>> cfg.dataset=subject;<br>
>> cfg.trialfun = 'ft_trialfun_general'; % this is the default<br>
>> cfg.trialdef.eventtype = 'Stimulus';<br>
>> cfg.trialdef.eventvalue ={Stimulus}';<br>
>> cfg.trialdef.prestim = 2; %latency in seconds<br>
>> cfg.trialdef.poststim = 2; %latency in seconds<br>
>> cfg=ft_definetrial(cfg);<br>
>><br>
>> cfg.channel = [1:69]; %discard channels not used<br>
>> cfg.preproc.reref = 'yes';<br>
>> cfg.preproc.refchannel = {'M1' 'M2'}; %avg mastoids<br>
>> cfg.continuous = 'yes';<br>
>> cfg.preproc.detrend = 'no';<br>
>> %Baseline correction<br>
>> %cfg.preproc.demean = 'yes';<br>
>> cfg.prepoc.baselinewindow = [-0.2 0];<br>
>> %Filtering<br>
>> cfg.preproc.lpfilter = 'yes';<br>
>> cfg.preproc.hpfilter = 'yes';<br>
>> cfg.preproc.lpfreq = 100;<br>
>> cfg.preproc.hpfreq = 1;<br>
>> data=ft_preprocessing(cfg);<br>
>><br>
>> cfg = [];<br>
>> cfg.method = 'runica';<br>
>> cfg.demean = 'yes';<br>
>> ica_cleaned = ft_componentanalysis(cfg,data);<br>
>><br>
>> % %Save ICA data<br>
>> % outfile=strcat(save_dir,'/',infile,'_', sti,'.mat')<br>
>> % save(outfile, 'ica_cleaned', '-v7.3')<br>
>><br>
>> cfg = [];<br>
>> cfg.component = 1:20;<br>
>><br>
>> cfg.layout = 'biosemi64.lay';<br>
>> cfg.comment = 'no';<br>
>> figure();<br>
>> ft_topoplotIC(cfg,ica_cleaned)<br>
>><br>
>> The data is in the BrainVision Format ( .vhdr, .vmrk and .seg), obtained as suggested by Fieldtrip website: <a href="http://www.fieldtriptoolbox.org/getting_started/brainvision/" rel="noreferrer" target="_blank">http://www.fieldtriptoolbox.org/getting_started/brainvision/</a><br>
>><br>
>> Can I ignore the Warning?<br>
>><br>
>> Best,<br>
>> Helena Pereira<br>
>><br>
>> Eelke Spaak <<a href="mailto:e.spaak@donders.ru.nl" target="_blank">e.spaak@donders.ru.nl</a>> escreveu no dia terça, 9/06/2020 à(s) 08:15:<br>
>>><br>
>>> Dear Helena,<br>
>>><br>
>>> In general, you can probably safely ignore the warning. It just means<br>
>>> that FieldTrip is unsure of the dimensionality of the 'comp' field,<br>
>>> because there is no 'compdimord' and also the dimensionality does not<br>
>>> match the data in any straightforward way.<br>
>>><br>
>>> However, the structure of your componentanalysis output is somewhat<br>
>>> strange. From .topo and .unmixing, it appears there is only one<br>
>>> component. However, .label has 69 elements, and there is no .topolabel<br>
>>> (which usually is there). Is the output you posted directly produced<br>
>>> by ft_componentanalysis (i.e. it is the 'ica_cleaned' variable<br>
>>> immediately after the step you showed)?<br>
>>><br>
>>> Best,<br>
>>> Eelke<br>
>>><br>
>>> On Mon, 8 Jun 2020 at 21:42, Helena Pereira<br>
>>> <<a href="mailto:hri.pereira@campus.fct.unl.pt" target="_blank">hri.pereira@campus.fct.unl.pt</a>> wrote:<br>
>>> ><br>
>>> > Dear FieldTrip users,<br>
>>> ><br>
>>> > I'm running the ft_componentanalysis function after applying ICA to my data. Although I'm not getting any error, the following warning came out:<br>
>>> ><br>
>>> > Warning: could not determine dimord of "comp" in:<br>
>>> ><br>
>>> > label: {69×1 cell}<br>
>>> > cfg: [1×1 struct]<br>
>>> > topo: [69×1 double]<br>
>>> > topodimord: 'chan_comp'<br>
>>> > comp: 7<br>
>>> > unmixing: [1×69 double]<br>
>>> > unmixingdimord: 'chan_topochan'<br>
>>> ><br>
>>> > Can this influence the way the components are displayed and consequently my results? Is this associated with the layout that I used or with the "demean" option?<br>
>>> ><br>
>>> > My code is as follows:<br>
>>> > cfg = [];<br>
>>> > %cfg.channel = 1:64;<br>
>>> > cfg.method = 'runica';<br>
>>> > cfg.demean = 'yes';<br>
>>> > ica_cleaned = ft_componentanalysis(cfg, data);<br>
>>> ><br>
>>> > cfg = [];<br>
>>> > cfg.component = 1:20;<br>
>>> > cfg.layout = 'biosemi64.lay';<br>
>>> > cfg.comment = 'no';<br>
>>> > ft_topoplotIC(cfg, ica_cleaned )<br>
>>> ><br>
>>> > Thanks in advance,<br>
>>> > Keep safe,<br>
>>> > Helena Pereira<br>
>>> ><br>
>>> ><br>
>>> ><br>
>>> ><br>
>>> ><br>
>>> ><br>
>>> > _______________________________________________<br>
>>> > fieldtrip mailing list<br>
>>> > <a href="https://mailman.science.ru.nl/mailman/listinfo/fieldtrip" rel="noreferrer" target="_blank">https://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br>
>>> > <a href="https://doi.org/10.1371/journal.pcbi.1002202" rel="noreferrer" target="_blank">https://doi.org/10.1371/journal.pcbi.1002202</a><br>
>>><br>
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>>> <a href="https://doi.org/10.1371/journal.pcbi.1002202" rel="noreferrer" target="_blank">https://doi.org/10.1371/journal.pcbi.1002202</a><br>
><br>
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</blockquote></div>