[FieldTrip] Any insight about Transfer Entropy?

[woun zoo]

How are you?

I'd like to get some insight from you for transfer entropy analysis of my
ECoG data before I run all possible parameters. I know this message doesn't
exactly fit in fieldtrip email list cause question is not exactly about
fieldtrip. But there are a few connectivity methods in fieldtrip. So I'd
like to get my questions to reach some of experts in this causality
analysis field. Besides, I don't know if there is nonlinear time series
analysis discussion list out there or not.

I'd like to establish some connectivity (functional or effective) between
frontal and visual channels in ECoG recording.  However, in our data, there
is a very strong driven component, namely, steady state visually evoked
potentials.  SSVEPs in our data appear at several frequencies that are
harmonics of the input frequencies and their sum and difference frequencies
So our data has a completely deterministic (SSVEPs) dynamics and the rest
of background activities.

Data has 20 trials in total. Each trial lasts 2.4sec. Sampling rate is
1200hz. Raw data were bandpass filtered from 0.1Hz to 500hz.

In order to find an effective connectivity, I chose to use TRENTOOL box
that can be incorporated with fieldtrip. I chose Ragwitz method to
determine delay time and embedding dimension. This is where I'd like to get
some good insight for choosing parameters. I attached a script that I'm
using now. I wrote my questions in blue text down below. I really wish to
get some good insight from you because I don't know if my input parameters
are garbage or not.

cfgTEP.toi = [min(data.time{1,1})  max(data.time{1,1})] --> Basically from
trial start to trial end.

cfgTEP.predicttimemin_u= 10;
cfgTEP.predicttimemax_u= 240;  --> For these prediction horizon values, I
don't know where and how these min and max were used in TEragwitz.m
calculation in TEprepare.m. Transfer Entropy calculation method (VW_ds)
fixed 1 as a prediction horizon. I can't find where this min or max of
predicttime goes inside TEragwitz calculation. VW_ds seems to try to
predict one time sample point ahead from the current time sample point. Is
this proper to determine embedding dimension and delay time for SSVEP +
background activities?

cfgTEP.actthrvalue = 100;   --> I don't know the reason why this
autocorrelation time value needs to be set by hand. I know with this
threshold value, you can selectively choose trials. In my data, particular
channels' autocorrelation values were 54 (sample points), etc. Max
autocorrelation was 134 or something. Is this due to noise? If I have
strong oscillatory activities at the driving frequencies, am I not supposed
to see autocorrelation values close to oscillatory period?

cfgTEP.maxlag      = 1000;  --> What will be a good lag number? Isn't it
better to use whole trial length?

cfgTEP.minnrtrials = 7;  --> What is a good number for this when there are
20 trials?

For main parameters for TEragwitz,

cfgTEP.optimizemethod ='ragwitz';
cfgTEP.ragdim         = 1:10;  --> I just chose all possible embedding
dimension from 1 to 10. Should I try go more than 10? But TE analysis
always says, embedding dimension maybe 2, which sounds about right for pure
sine waves like my SSVEP. But with 0.1Hz~500hz bandpass, I have tons of
non-stimulus locked high background activities. I'd like to know if 2 is
really good estimation or not for my data. Also when I chose Cao's method,
it says, 5 or 6.

cfgTEP.ragtaurange    = [0.1 2]; --> For delay time as an initial guess, I
chose this range. But Ragwitz always chose the smallest value. If I put
this range from [1 2], then it chooses 1. If it was [0.5 3], it chose 0.5.
Whatever minimum value I put will be chosen as its delay time, which makes
me wonder about what kind of values I should put here.

cfgTEP.ragtausteps    = 15;         % steps for ragwitz tau steps 15

cfgTEP.repPred        = 600;  --> I just chose this. Depending on what I
put here, final significance of TE changes too.

cfgTEP.flagNei = 'Mass' ;           %neigbour analyse type

cfgTEP.sizeNei = 4;  --> It follows the results of Kraskov (2004) paper. I
think this range is between [embedding dimension 2*embedding dimension].
But should I vary this too? For example, should I try 15, 30, 50 etc?


For Surrogate analysis in the below, I don't know which options are common
to use for non-parametric statistical analysis.

cfgTESS.optdimusage = 'indivdim';
cfgTGAA.select_opt_u     = 'product_evidence'; % 'max_TEdiff';
cfgTGAA.select_opt_u_pos = 'shortest';

I'm sorry if these questions are not exactly relevant to fieldtrip
community. If there is nonlinear time series analysis community, I'd like
to post this message over there. But I really appreciate if you could give
me some good insight about playing with parameters for ECoG steady-state
visual evoked potential data.

Thank you very much.
Have a nice day.
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