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Hello,<br>
<br>
I tried to answer your questions regarding the TRENTOOL parameters
below. We will soon provide a user manual for the current TRENTOOL
version on the website (<a class="moz-txt-link-abbreviated" href="http://www.trentool.de">www.trentool.de</a>), which should also help
with some of the questions raised in your email. <br>
<br>
cfgTEP.toi = [min(data.time{1,1}) max(data.time{1,1})] -->
Basically from trial start to trial end. <br>
PW: This is correct, you should use as much data as possible. <br>
<br>
cfgTEP.predicttimemin_u= 10; <br>
cfgTEP.predicttimemax_u= 240; --> I am not sure where and how
these min and max were used in TEragwitz calculation in TEprepare.m.
VW_ds fixed 1 as a prediction horizon. I'm not sure if it's good to
predict just next time sample point for SSVEP + noisy data? <br>
<br>
PW: TRENTOOL allows you to reconstruct the delays of an interaction
(see Wibral, 2013, <i>Measuring Information Transfer Delays</i>).
Interaction delays are reconstructed by scanning over a range of
assumed interaction delays u, specified by the parameters
'predicttimemin_u', 'predicttimemax_u', and 'predicttimestepsize'.
TRENTOOL will actually run the TE estimation for each assumed u,
i.e. TE will be estimated between all pairs of channels for each
prediction time u. The Ragwitz criterion will be used for each
estimation to determine the respective embedding parameters. In a
second step, TRENTOOL will reconstruct the interaction delay by
finding the value for u for which TE becomes maximal. <br>
Note, that you also have to provide the step size in
'cfgTEP.predicttimestepsize'. TRENTOOL will build a vector
[cfgTEP.predicttimemin_u:cfgTEP.predicttimestepsize:cfgTEP.predicttimemax_u]
to estimate TE for each u. You have specified a rather broad range
of interaction delays to be scanned here. This will result in a very
long running time. Maybe you could reconsider the values for u that
you want to scan (i.e. use assumed interaction delays that are
biologically plausible)? <br>
<br>
cfgTEP.actthrvalue = 100; --> I don't know the reason why this
autocorrelation time value needs to be set by hand cause I thought
embedding delay time gets automatically decided by autocorrelation.
Is there a special logic behind setting this by hand? For particular
two channels, their ACT values were 54 sample points, etc. Max ACT
was 134 or something. Is this due to noise? If I have strong
oscillatory activities, am I not supposed to see ACT values close to
oscillatory period? <br>
<br>
PW: This is only a threshold value. If the actual ACT is higher for
individual trials, these trials will be excluded from the analysis.
The value you put here should be based on the filtering of the data
prior to TE analysis. E.g. if you highpass filter your data at 10 Hz
and have a sampling rate of 1200Hz, you shouldn't find any
autocorrelation above 120 samples. Thus, you may use 120 as a
threshold here.<br>
<br>
cfgTEP.maxlag = 1000; --> 1000 is default. What will be a
good lag number to see autocorrelation? Should I use a half of total
sample points of data (2880/2 = 1440)? <br>
PW: Half the number of sample points is fine.<br>
<br>
cfgTEP.minnrtrials = 7; --> Does this mean if trial selection
rule by ACT value rejects more than 13 trials out of total 20
trials, program won't run? What is a good number for this when I
have 20 trials? <br>
PW: This is correct, if you end up with less than the number of
trials specified here, the analysis will not run. Because of the
permutation statistics used later, this value should be set to at
least 12.<br>
<br>
For main parameters for TEragwitz, <br>
<br>
cfgTEP.optimizemethod ='ragwitz'; <br>
cfgTEP.ragdim = 1:10; --> I just chose all possible
embedding dimension from 1 to 10. Should I try to put more than 10?
But TE analysis always says, embedding dimension maybe 2, which
sounds about right for pure sine waves like SSVEPs. But with
0.1Hz~500hz bandpass, I have tons of non-stimulus locked low and
high noisy activities. But when I chose Cao's method, it says, 5 or
6. <br>
PW: 1:10 is alright here. Ragwitz is the recommended method for
parameter estimation. <br>
<br>
cfgTEP.ragtaurange = [0.1 2]; --> For delay time, 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. So I'd really like to know what kind of values I should put
here. <br>
PW: The values you provided here are ok ('ragtaurange' determines
the embedding delay). The values, that are returned by Ragwitz'
optimization (tau = 0.1, dim = 2), indicate that there are a lot of
fast dynamics in your data. This may indicate a lot of high
frequency noise. Consider filtering (forward only!) in the range
were you expect neural activity (e.g. 0.5 to 300 Hz or similar).<br>
<br>
cfgTEP.ragtausteps = 15; % steps for ragwitz tau steps 15
<br>
<br>
cfgTEP.repPred = 600; --> I just chose this. I could vary
this. Depending on what I put here, final significance of TE changes
too.<br>
PW: This parameter determines how many data points are used for
optimization of the embedding parameters by the Ragwitz criterion.
Here, TRENTOOL will use the first 600 points in each trial to
optimize embedding parameters. This number should be as high as
possible (depending on the values you chose for cfgTEP.actthrvalue,
fgTEP.ragdim, cfgTEP.ragtaurange).<br>
<br>
cfgTEP.flagNei = 'Mass' ; %neigbour analyse type <br>
<br>
cfgTEP.sizeNei = 4; --> Ideally I guess I might have to vary
size of neighborhood in phase space <br>
PW: 4 is fine here (default).<br>
<br>
For Surrogate analysis, <br>
<br>
cfgTESS.optdimusage = 'indivdim'; <br>
cfgTGAA.select_opt_u = 'product_evidence'; % 'max_TEdiff';
--> I just chose 'product_evidence' because help file of
InteractionDelayReconstruction_analyze.m says 'max_TEdiff' could be
problematic in certain case. Which one is normal to use? <br>
PW: We recommend the use of 'max_TEdiff' . We will change the help
text in a future release. <br>
<br>
cfgTGAA.select_opt_u_pos = 'shortest'; --> Also for this, I don't
know which one is normal to use. <br>
PW: 'shortest' is fine here. <br>
<br>
<br>
I hope this helps,<br>
<br>
best regards<br>
<br>
Patricia<br>
<br>
<br>
<br>
<div class="moz-cite-prefix">Am 30/01/2014 19:38, schrieb woun zoo:<br>
</div>
<blockquote
cite="mid:CAOF2uG-9VjKSq6jZSMb5g35zOomsViXQD+wZVYQ-eMMgumXbmg@mail.gmail.com"
type="cite">
<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 moz-do-not-send="true"
src="https://mail.google.com/mail/u/1/images/cleardot.gif"><br>
</div>
<br>
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