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Hi Eva,
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<div class="">Apologies, here’s an afterthought: the documentation of the function mentions that the first dimension of the input should be the ‘rpt’ dimension, so I suspect you’d need to shiftdim your input arguments with a ‘-1’.</div>
<div class="">Normally, you’d access ft_connectivity_granger through the higher level function ft_connectivityanalysis, which would take care of this step under-the-hood.</div>
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<div class="">Concretely, probably it works if you do</div>
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<div class="">H = shiftdim(freq.transfer,-1);</div>
<div class="">S = shiftdim(freq.crsspctrm,-1);</div>
<div class="">Z = shiftdim(freq.noisecov,-1);</div>
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<div class="">g = ft_connectivity_granger(H,Z,S,’dimord’,’rpt_chan_chan_freq_time’);</div>
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<div class="">Best wishes,</div>
<div class="">Jan-Mathijs</div>
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<blockquote type="cite" class="">
<div class="">On 21 Jun 2021, at 22:28, Eva Masson via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>> wrote:</div>
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Hi FieldTrip Community !</div>
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<o:p class=""> </o:p></div>
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I am trying to compute spectrally resolved Granger causality on (i)EEG data using the parametric approach, with mvar modelling. At this level, it is easier for my data than non parametric modelling.</div>
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<o:p class=""> </o:p></div>
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From the original data, I have selected two channels to compute Granger causality from channel 1 to channel 2 and from channel 2 to channel 1.</div>
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<o:p class=""> </o:p></div>
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I am currently struggling with the last step, from which I am expecting an output in the form of a 4-D structure containing the Granger causality power for each selected frequency and each time point for :</div>
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Channel 1 to channel 1 (not very informative)</li><li class="MsoListParagraph" style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;">
Channel 1 to channel 2</li><li class="MsoListParagraph" style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;">
Channel 2 to channel 1</li><li class="MsoListParagraph" style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;">
Channel 2 to channel 2 (not very informative)</li></ul>
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<o:p class=""> </o:p></div>
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To do so, I am using the function<span class="Apple-converted-space"> </span><i class="">ft_connectivity_granger</i>, which I think is suited for spectrally resolved Granger causality, also while keeping the time dimension.</div>
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<o:p class=""> </o:p></div>
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Here is my sloppy code :</div>
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<o:p class=""> </o:p></div>
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<i class="">[mvar_granger, V, n] = ft_connectivity_granger(mvar_freq.transfer, mvar_freq.noisecov, ...<o:p class=""></o:p></i></div>
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<i class=""> mvar_freq.crsspctrm, 'dimord', 'chan_chan_freq_time');<o:p class=""></o:p></i></div>
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<o:p class=""> </o:p></div>
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In which mvar_freq is the structure obtained after ft_mvaranalysis and transformed into the frequency domain with ft_freqanalysis_mvar. To be honest, I don’t know what V stands for in the output.</div>
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<o:p class=""> </o:p></div>
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Here are the dimords of the mvar_freq fields used as inputs:</div>
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Transfer (H) and crsspctrm (S): chan_chan_freq_time</div>
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Noisecov (Z) : chan_chan_freq</div>
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<o:p class=""> </o:p></div>
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After running the previous line, I do not get an error, but a mvar_granger structure in 3D containing a lot of 0 values. Honestly, I don’t really understand what it represents.</div>
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<o:p class=""> </o:p></div>
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If someone knows how to work with ft_connectivity_granger in such a way that I can get the kind of output I am looking for (probably something like chan_chan_freq_time, right ?), I am all ears to your wisdom !</div>
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<o:p class=""> </o:p></div>
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I would be very thankful for any help on this topic <span style="font-family: "Segoe UI Emoji", sans-serif;" class="">😊</span></div>
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<o:p class=""> </o:p></div>
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Cheers !</div>
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Eva</div>
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<o:p class=""> </o:p></div>
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PS : Thanks a lot for this FieldTrip mailing list. I am learning so much thanks to it !</div>
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<a href="https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!qsFd-6Mz_z_IYoW7fp2Uz2hx-Hk4GXhPN248fH66xtrB8n0cs5bB4qqdcO1zHqqgfhjYa-La7WY4iCo$" style="font-family: Helvetica; font-size: 14px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;" class="">https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!qsFd-6Mz_z_IYoW7fp2Uz2hx-Hk4GXhPN248fH66xtrB8n0cs5bB4qqdcO1zHqqgfhjYa-La7WY4iCo$</a><span style="caret-color: rgb(0, 0, 0); font-family: Helvetica; font-size: 14px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; float: none; display: inline !important;" class=""></span></div>
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