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Hi Philip,<br class="">
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<div class="">Yes, an svd is very similar in spirit to an eigenvalue decomposition (which is the workhorse for the pca implementation).</div>
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<div class="">If your data are organised in a ‘comp’ structure that can be recognized by fieldtrip, you should be able to use ft_rejectcomponent.</div>
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<div class="">Unless there’s a hidden agenda to your question, and you already tried this unsuccessfully.</div>
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<div class="">Best wishes,</div>
<div class="">Jan-Mathijs</div>
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<div class="">On 31 Aug 2020, at 22:45, philip Joadavi <<a href="mailto:p.joadavi@gmail.com" class="">p.joadavi@gmail.com</a>> wrote:</div>
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<div dir="ltr" class="">Dear Jan-Mathijs,
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<div class="">Thank you so much for your quick reply. I tried it and it's working. I used 'SVD' instead of 'PCA'. </div>
<div class="">Just to be sure I have a follow-up question, I want to use <b class="">
ft_rejectcomponent</b> after that (I mean after ft_componentanalysis), can I just use it as it is or I have to do some other tricks also before using ft_rejectcomponent? </div>
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<div class="">Thanks a lot,</div>
<div class="">Philip</div>
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<div dir="ltr" class="gmail_attr">On Wed, Aug 26, 2020 at 8:42 AM Schoffelen, J.M. (Jan Mathijs) <<a href="mailto:jan.schoffelen@donders.ru.nl" class="">jan.schoffelen@donders.ru.nl</a>> wrote:<br class="">
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<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div style="overflow-wrap: break-word;" class="">Hi Philip,
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<div class="">You could try and hack your way around, by transposing the time courses. Assuming you have just a single epoch in your data structure (or all epochs have the exact same length) (otherwise it will not work), you can do: for k = 1:numel(data.trial)
data.trial{k} = data.trial{k}’ ; end</div>
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<div class="">Then you need to fool Fieldtrip into ’thinking’ that the -what used to be the channel dimension- is the time dimension: data.time{:} = (1:size(data.trial{1},2))./data.fsample;</div>
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<div class="">and likewise for the new channel dimension:</div>
<div class="">for k = 1:size(data.trial{1},1)</div>
<div class="">data.label{k} = sprintf(‘chan%05d’,k);</div>
<div class="">end</div>
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<div class="">Then Bob’s your uncle, that is, probably ft_componentanalysis will swallow the data.</div>
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<div class="">Note however, that the pca algorithm implemented in FT is not very clever, so it will probably not deal well with ill-conditioned covariance matrices, which you’re likely to end up with, since the number of time points is typically much larger
than the number of channels.</div>
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<div class="">Best wishes,</div>
<div class="">Jan-Mathijs</div>
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<div class=""><br class="">
<blockquote type="cite" class="">
<div class="">On 25 Aug 2020, at 18:56, philip Joadavi <<a href="mailto:p.joadavi@gmail.com" target="_blank" class="">p.joadavi@gmail.com</a>> wrote:</div>
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<div dir="ltr" class="">Dear all,
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<div class="">I would like to work with PCA on my EEG data using Fieldtrip. As far as I know, the implementation of PCA in Fieldtrip is only on spatial (i.e the variables are the EEG channels (
<b class="">spatial PCA</b>)). </div>
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<div class="">Is that possible to work around the function 'ft_componentanalysis' to perform the
<b class=""><u class="">temporal PCA</u></b> using field trip?</div>
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<div class="">Thanks!</div>
<div class="">Philip Joadavi</div>
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