[FieldTrip] NaNs in Granger Spectrum

[Helen Wieffering]

Dear Jörn,

Thank you very much for your detailed reply. I think my issue was
definitely due to a poorly thought out time window and zero padding
combination - but with a constant time window in ft_freqanalysis I'm now
successfully getting Granger causality to calculate, and to produce a full
spectrum of values rather than just the NaNs!
It's especially helpful to have the attached papers and to get a sense of
how others are approaching the procedure.

Thank you, again!

Best,
Helen

On Thu, Nov 19, 2015 at 3:30 AM, Jörn M. Horschig <jorn at artinis.com> wrote:

> Dear Helen,
>
>
>
> I am not quite sure why there are only nans or 0s in your granger
> spectrum. It could be because of the nans in your data. You could test that
> by just putting the time from e.g. 0.3s to 0.7s in. As a general tip, I
> would always segment trials into larger chunks that you want to do your
> final analysis on, otherwise edge artifacts (either from filtering or like
> here in the TFR) will show up. Also, I used constant 400ms time windows
> <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138685>
> (link)
> <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138685>,
> which apart from making the time-windows of the TFR look ‘nicer’, it also
> makes it easier to compare across frequencies. With the frequency-dependent
> time window, you could run into issues with SNR across frequencies, as the
> lower frequency content is estimated by a longer time windows, and
> averaging over more data should lead to a higher SNR. Thus, the lower
> frequency content should be more reliable when using frequency-dependent
> time windows.
>
>
>
> Two other general tips:
>
> 1)      If I recall correctly, the Wilson factorization that is being
> used for estimating the transfer matrix assumes that your data contains all
> frequencies from DC to the Nyquist frequency. So better not restrict your
> analysis from 1 to 20Hz, but use 0 to fsample/2.
>
> 2)      If you have strong noise components in your data, for example
> line noise, your power spectrum will sharply rise near the line noise
> frequency, and quickly fall off a few Hz after that. Such sharp changes
> will lead to strange effects in your Granger estimation, showing up as
> changes in estimated peak frequencies. Filtering such artifacts out though
> should be done with care though, see e.g.
> http://www.ncbi.nlm.nih.gov/pubmed/21864571 and
> http://www.ncbi.nlm.nih.gov/pubmed/20026279. I would advise against
> trying to filter out line noise based on experience with my own data. I
> found it more suitable to use zero padding, which “artificially” increases
> your frequency resolution, thereby smoothes the power spectrum and thus
> avoid prevents sharp changes in there. Btw, note that the two above papers
> are on parametric Granger, which might be differently affected by filtering
> than the nonparametric version (that’s at least what I found using
> simulated data – parametric Granger was more invariant to different
> preprocessing pipelines)
>
>
>
> Best of luck ;)
>
> Greetings,
>
> Jörn
>
>
>
> *--*
> *Jörn M. Horschig, PhD*, Software Engineer
> Artinis Medical Systems <http://www.artinis.com/>  |  +31 481 350 980
>
>
>
> *From:* fieldtrip-bounces at science.ru.nl [mailto:
> fieldtrip-bounces at science.ru.nl] *On Behalf Of *Helen Wieffering
> *Sent:* Wednesday, November 18, 2015 15:47
> *To:* FieldTrip discussion list <fieldtrip at science.ru.nl>
> *Subject:* [FieldTrip] NaNs in Granger Spectrum
>
>
>
> Dear Fieldtrip users,
>
> I am working with a 128 channel EEG data and am currently in the process
> of calculating non-parametric Granger causality measures. I am following
> the steps outlined in Dhamala, Rangarajan, and Ding (2008) from NeuroImage.
>
> However, I'm facing the curious problem that my output data from calling
> ft_connectivity has a grangerspctrm made up only of NaN and 0 values. Does
> anyone have insight as to why that might be? I've tried playing around with
> cfg.toi and the location of our virtual channels, but neither seems to
> help.
>
> My code is below, where 'source01' and 'source02' refer to virtual
> channels we created based off powspctrm data from beamformer source
> localization on our own data, according to steps outlined in the FT
> connectivity tutorial.
>
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> *data = label: {2x1 cell}trial: {1x155 cell}time: {1x155 cell}fsample:
> 250cfg: [1x1 struct]sampleinfo: [155x2 double]% perform multitaper
> analysiscfg = [];cfg.method = 'mtmconvol';cfg.channelcmb = { 'all'
> 'all'};cfg.fsample = 250;cfg.channel = data.label;cfg.output =
> 'powandcsd';cfg.foi = 0:1:20;                      cfg.toi =
> -0.5:0.004:1.5;       cfg.taper = 'hanning';cfg.t_ftimwin = 4 ./
> cfg.foi;            freq = ft_freqanalysis(cfg, data_both);*
>
> Note: I've checked the values in freq.powspctrm, and though there are NaNs
> around the temporal edges, there is real data amid the time frame of
> interest. I've attached a sample TFR plot.
>
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> *% compute granger causalitycfg = [];cfg.method = 'granger';cfg.channel =
> {'source01' 'source02'};cfg.channelcmb = {'all' 'all'};granger =
> ft_connectivityanalysis(cfg, freq);% plotcfg = [];cfg.parameter =
> 'grangerspctrm';cfg.zlim = [0 .5];cfg.refchannel =
> 'source02';cfg.directionality = 'outflow';ft_singleplotTFR(cfg, granger);*
>
> However, this plot is a blank no matter which values I seem to choose as a
> zlim.
>
> Any tips are much appreciated - thanks!
>
> (Please excuse my re-posting of a similar message twice - I'm simply
> hoping to catch a few more responses. As far as I can tell, this issue has
> not yet been addressed in the digest archives - though if I'm mistaken
> please feel free to point me to the appropriate thread!)
>
> Best,
>
> Helen Wieffering
>
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