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Hi Robert,
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<div class="">In principle it’s OK, and computationally efficient, to (left)-multiply the spatial filter with the fourier coefficients (in a for-loop across frequencies, or something like that), to get the source-level fourier representation. </div>
<div class="">Practically, you would need to do something like: </div>
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<div class="">nchan = numel(freq.label);</div>
<div class="">nfreq = numel(freq.freq);</div>
<div class="">nrpttap = size(freq.fourierspctrm,1);</div>
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<div class="">fourier_source = filter*reshape(permute(freq.fourierspctrm,[2 3 1]), [nchan nfreq*nrpttap]));</div>
<div class="">fourier_source = reshape(fourier_source, [nfreq nrpttap]);</div>
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<div class="">Now you have a nfreq*nrpttap matrix that represents per tapered data segment the fourierspctrm.</div>
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<div class="">From this representation, you would want to create the source pair-wise cross-spectral density that can be subjected to the non-parametric factorization code to compute Granger causality.</div>
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<div class="">That is, it’s more or less OK if you know what you’re doing, but can become tricky in terms of correct data administration/bookkeeping. </div>
<div class="">Otherwise, and perhaps more straightforwardly, I’d recommend to do a time-domain source reconstruction (create a set of ‘virtual channels’, there’s are some examples in the wiki’s tutorials how to do it), and then use ft_freqanalysis and ft_connectivityanalysis
on the virtual channel data.</div>
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<div class="">Best wishes,</div>
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<div class="">Jan-Mathijs</div>
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<blockquote type="cite" class="">
<div class="">On 22 Aug 2016, at 17:24, Seymour, Robert (Research Student) <<a href="mailto:seymourr@aston.ac.uk" class="">seymourr@aston.ac.uk</a>> wrote:</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">Dear Fieldtrip Users, </div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">I am trying to compute non-parametric granger causality between several ROIs in source-space. I am using the following code to obtain the fourier-spectra of my sensor-level data.</div>
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<div class="">cfg = [];</div>
<div class="">cfg.output = 'fourier';</div>
<div class="">cfg.method = 'mtmfft';</div>
<div class="">cfg.taper = 'dpss';</div>
<div class="">cfg.tapsmofrq = 4;</div>
<div class="">cfg.keeptrials = 'yes';</div>
<div class="">cfg.pad = 1;</div>
<div class="">cfg.padtype = 'zero';</div>
<div class="">freq = ft_freqanalysis(cfg, data_fix);</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">I was wondering how to<span style="white-space: pre-wrap; font-size: 10pt;" class=""> project the sensor level fourier-data into source space using the spatial filters computed when calling ft_sourceanalysis?
I.e. how to multiply the freq*filter? I am little unsure how the <span class="">
fourierspctrm is represented and how this will feed into ft_connectivity analysis?</span></span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span style="white-space: pre-wrap; font-size: 10pt;" class=""><span class="">Many thanks,
</span></span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span style="white-space: pre-wrap; font-size: 10pt;" class=""><span class="">Robert Seymour (PhD Student, Aston Brain Centre)</span></span></div>
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