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Hi Masoud et al.,
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<div class="">Apologies: I was inaccurate in my previous e-mail in explaining the GC coefficient. It’s the
<u class="">negative</u> natural logarithm of the residual variance ratio, where the model with the larger number of parameters (i.e. the one which predicts a signal based on the past of the other signals as well) is in the numerator. </div>
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<div class="">BW,</div>
<div class="">JM</div>
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<div class="">Begin forwarded message:</div>
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">From:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">Jan Mathijs Schoffelen <<a href="mailto:janmathijs.schoffelen@donders.ru.nl" class="">janmathijs.schoffelen@donders.ru.nl</a>><br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Subject:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class=""><b class="">Re: [FieldTrip] A question about connectivity values</b><br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Date:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">4 January 2023 at 13:25:18 CET<br class="">
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<div class="">Hi Masoud,<br class="">
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Theoretically, the Granger causality coefficient is a natural logarithm of a ratio of residual variances, where this ratio is typically >1, because the model implicitly used for the numerator has more parameters, so typically a lower residual variance. In other
words, GC > 0 but can become larger than 1. <br class="">
<br class="">
If the input data into the algorithm are numerically not well behaved, I can imagine that the output GC will be unexpected. In your case, you estimate a series of models consisting of 62x62x6 (i.e. >18.000) parameters (for each time window). If the number of
samples in your data is insufficient and/or there’s linear dependence between channels, the estimated parameters, and by consequence the spectral quantities, and the GC will not make sense.<br class="">
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Best wishes and good luck,<br class="">
<br class="">
Jan-Mathijs<br class="">
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<blockquote type="cite" class="">On 4 Jan 2023, at 09:26, masoud via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>> wrote:<br class="">
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Hello dear fieldtrip community<br class="">
I have been using fieldtrip for quite some time and now I am dealing with dynamic connectivity, the preprocessing of the data which I am using was done in fieldtrip as well as MVAR analysis, this is the code for calculating dynamic connectivity:<br class="">
cfg.method = 'biosig';<br class="">
cfg.t_ftimwin = 5;<br class="">
cfg.toi = toi;<br class="">
cfg.order = 6;<br class="">
cfg.output = 'parameters';<br class="">
mvardata = ft_mvaranalysis(cfg, data);<br class="">
cfg = [];<br class="">
cfg.method = 'mvar';<br class="">
cfg.output = 'fourier';<br class="">
freq = ft_freqanalysis(cfg, mvardata);<br class="">
cfg = [];<br class="">
cfg.method = 'granger';<br class="">
stat = ft_connectivityanalysis(cfg, freq);<br class="">
so the output of connectivity (stat) is a 4-d matrix(channel, channel, frequency, windows) which for my data is (62, 62, 126, 12)<br class="">
the values of the channel x channel are mostly negative like -13.2650 , -5.7060 or between -1 and 1.
<br class="">
as my understanding the output should be between 0 and 1. So where am I doing it wrong? And if I change the method from 'granger' to 'pdc' or 'dtf' what should be the range of output?
<br class="">
thank you so much in advance <br class="">
best regards<br class="">
Masoud<br class="">
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