<div dir="ltr">How are you?<br><br>I'd like to get some insight from you for <span><span class="">transfer</span></span> <span><span class="">entropy</span></span> 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.<br>
<br>
<div>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.<br><br></div><div>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.<br>
</div><div><br></div><div>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.<br>
<br>cfgTEP.toi = [min(data.time{1,1}) max(data.time{1,1})] <span style="color:rgb(0,0,255)">--> Basically from trial start to trial end.</span><br><br>cfgTEP.predicttimemin_u= 10;<br>cfgTEP.predicttimemax_u= 240; <span style="color:rgb(0,0,255)">--> 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. </span><span style="color:rgb(0,0,255)"><span style="color:rgb(0,0,255)">I can't find where this min or max of
predicttime goes inside TEragwitz calculation. </span>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? <br>
<br><span style="color:rgb(0,0,0)">cfgTEP.actthrvalue = 100; </span>
--> 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?<br>
<br><span style="color:rgb(0,0,0)">cfgTEP.maxlag = 1000; </span> --> What will be a good lag number? </span><span style="color:rgb(0,0,255)"><span style="color:rgb(0,0,255)">Isn't it
better to use whole trial length? <br><br></span><span style="color:rgb(0,0,0)">cfgTEP.minnrtrials = 7; </span>
--> What is a
good number for this when there are 20 trials? <br>
</span></div><div><span style="color:rgb(0,0,255)"><br></span></div><div><span style="color:rgb(0,0,255)"><font color="#000000">For main parameters for TEragwitz,<br><br>cfgTEP.optimizemethod ='ragwitz';<br>cfgTEP.ragdim = 1:10; <font color="#0000ff">--> I just chose all possible embedding dimension from 1 to 10. Should I try go more than 10?</font> <span style="color:rgb(0,0,255)">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 </span><span style="color:rgb(0,0,255)">when I chose Cao's method, it says, 5 or 6<font color="#000000">. </font></span><br>
<br>cfgTEP.ragtaurange = [0.1 2]; <span style="color:rgb(0,0,255)">-->
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 minimu<font color="#000000"><span style="color:rgb(0,0,255)">m value I put will be chosen as its delay time, which makes me wonder about</span></font> what kind of
values I should put here.</span><br>
<br>cfgTEP.ragtausteps = 15; % steps for ragwitz tau steps 15<br><br>cfgTEP.repPred = 600; <span style="color:rgb(0,0,255)">--> I just chose this<font color="#000000">.</font> Depending on what I put here, final significance of TE changes too.</span><br>
<br>cfgTEP.flagNei = 'Mass' ; %neigbour analyse type<br><br>cfgTEP.sizeNei = 4; <font color="#0000ff">--> 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?<br>
<span style="color:rgb(0,0,0)"><br></span></font></font></span></div><div><span style="color:rgb(0,0,255)"><font color="#000000"><font color="#0000ff"><span style="color:rgb(0,0,0)"><br>For Surrogate analysis<font color="#0000ff"> in the below, I don't know which options are common to use for non-parametric statistical analysis.</font></span></font></font></span><span style="color:rgb(0,0,255)"><font color="#000000"><font color="#0000ff"><span style="color:rgb(0,0,0)"><br>
<br>cfgTESS.optdimusage = 'indivdim';</span> <br><span style="color:rgb(0,0,0)">cfgTGAA.select_opt_u = 'product_evidence'; % 'max_TEdiff';<br>cfgTGAA.select_opt_u_pos = 'shortest'; <br>
<br></span></font></font></span></div><div><span style="color:rgb(0,0,255)"><font color="#000000"><font color="#0000ff"><span style="color:rgb(0,0,0)">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. </span><br>
<span style="color:rgb(0,0,0)"><br></span></font></font></span></div><div>Thank you very much.<br></div>Have a nice day.<img src="https://mail.google.com/mail/u/1/images/cleardot.gif"><br></div>