[Eeglablist] Filter causality pop_eegfiltnew
Vito de Feo
vito.defeo at zmnh.uni-hamburg.de
Mon Jan 27 13:10:57 PST 2014
Hi Tim,
thank you for your answer. I'm trying to evaluate the delay that I have
with the Spectral GC in front of just temporal GC.
Today I friend of mine told me that he has the same problem with the
Chronux toolbox... now I understand that it was the same problem!
Thanks a lot for changing the function! Next time I'll do by myself in
order to contribute to improve the code.
Vito
P.S. I am going to answer also to the other interesting mail you wrote me.
Quoting Tim Mullen <mullen.tim at gmail.com>:
> Hi Vito,
>
>> 1) About the Spectral Granger Analysis it is true that "I can obtain a
>> temporal measure of Granger causality by integrating the GGC
>> (Granger-Geweke Causality) measure provided by SIFT across all
>> frequencies." but this requires much more time if I have to create a
>> lot of models for each trace. For example if I need to create 20000
>> models for one traces and I am just interested to the integral I spend
>> a lot of time calculating the complete spectrum.
>>
>
> Both temporal and spectral GC require the same number of VAR models
> -- one model per time window. Spectral GC includes an additional step
> where the transfer function is obtained via a Fourier transform of the
> model coefficients. But this step takes very little time on modern
> multicore computers (milliseconds). The remaining time would be involved
> in computing the residual covariance matrix and other quantities, which
> you need for temporal GC anyways. But, yes, if you have tens of
> thousands of time windows, there may be a slight overhead to the
> spectral approach (depends on the temporal GC implementation -- some of
> them actually perform F-tests for separately-fit full and restricted
> models, and are much slower than the spectral approach in SIFT)
>
>> 3) I have a problem with the cleanline routine because you use a
>> function called "findpeaks" that I guess you wrote. The problem is that
>> there is already a matlab findpeaks function that also I use a lot. So
>> I have continuosly change path. Is possible to rename the findpeaks
>> function in the next cleanline release?
>
> Actually, the overloaded FINDPEAKS is in the Chronux toolbox, part
> of which is included as an external dependency to cleanline. But, thanks
> for pointing that out -- I went ahead and changed the name of the
> function. Download the latest version of Cleanline:
> https://bitbucket.org/tmullen/cleanline/get/tip.zip
> Or in the open-source tradition, free to just edit the code
> yourself and change that one function name and the 2 calls to it...
>
>
> Tim
>
>
>> Quoting Tim Mullen <mullen.tim at gmail.com>:
>>
>>> Dear Vito, answers below:
>>>
>>>
>>>> About SIFT, comparing it with Anil Seth's Granger toolbox it seems
>>>> that in SIFT are missing a few things (probably I don't know very
>>>> good SIFT):
>>>>
>>>> 1) In SIFT there is only the Spectral Granger
>>>> Analysis, there is not the temporal Granger Analysis. Is this correct?
>>>
>>>
>>>
>>> You can obtain a temporal measure of Granger causality by
>>> integrating the GGC (Granger-Geweke Causality) measure provided by
>>> SIFT across all frequencies.
>>>
>>>
>>>
>>>> 2) In SIFT there is not a stationarity test. Is this correct?
>>>
>>>
>>>
>>> No there is not a direct test for stationarity (e.g.
>>> Augmented Dickey-Fuller). Instead, stability and whiteness tests are
>>> provided. A stable VAR model is always stationary so if the model
>>> passes stability and whiteness tests (e.g. the data can be
>>> appropriately modeled as a stable VAR process), stationarity of the
>>> data is implied. However, in cases where the model residuals are not
>>> white or the model is not stable, it can be useful to run a
>>> stationarity test on the data to determine if this is the problem. For
>>> this, one might consider using the ADF procedure in the GCCA toolbox.
>>> Bear in mind there are a few issues with this: The ADF test is a
>>> univariate -- not multivariate -- stationarity test. We assume the
>>> system is a multivariate autoregressive process (as does GCCA, for
>>> that matter) and are interested in testing for non-stationarity in the
>>> multivariate dataset (e.g. covariance stationarity) rather than
>>> testing each univariate time-series independently. ADF also has low
>>> power, and in many cases fails to reject the unit root hypothesis
>>> (e.g. Perron, 1989, Econometrica). There are alternate proposed
>>> multivariate stationarity test procedures (e.g. Jentsch and Rao, 2013;
>>> Yang and Shahabi, 2005), but these are not implemented in SIFT. In
>>> many cases, the stability and whiteness tests should suffice.
>>>
>>>
>>>
>>>> 3) In SIFT there is a common test for stability and consistence. Is
>>>> this correct?
>>>>
>>>
>>>
>>> No, there are separate tests for stability and
consistency.
>>>
>>> Best,
>>> Tim
>>>
>>>
>>>>
>>>>
>>>> Il giorno 18/gen/2014, alle ore 19:50,
>>>> Andreas Widmann ha scritto:
>>>>
>>>>
>>>>> Dear all,
>>>>>
>>>>> not directly related to your question and SIFT,
>>>>> but eegfilt is deprecated and I would recommend not using it any
>>>>> longer.
>>>>>
>>>>> Best,
>>>>> Andreas
>>>>>
>>>>> Am 18.01.2014 um 15:47 schrieb "jfochoaster ."
>>>>> <jfochoaster at gmail.com>:
>>>>>
>>>>>
>>>>>> Hello all,
>>>>>>
>>>>>> I'm following the SIFT tutorial, the section 6.5.1.3 is about
>>>>>> filtering, talk about eegfilt, about the zero-phase (acausal) filter
>>>>>>
>>>>>> Is better forget this section of filtering and use the
>>>>>> recommendations in the past emails?
>>>>>>
>>>>>> Are these recommendation critical for the
>>>>>> analysis?, I mean, there is a lot of work about MVAR models in ECoG
>>>>>> data
>>>>>>
>>>>>> Best wishes
>>>>>>
>>>>>> John
>>>>>>
>>>>>>
>>>>>> On Fri, Jan 17, 2014 at 11:05 PM,
>>>>>> mullen.tim at gmail.com <mullen.tim at gmail.com> wrote:
>>>>>>
>>>>>>> Oh thats interesting. I had not seen Anil's multitaper filter
>>>>>>> (might be fairly recent). But possibly it is exactly the same
>>>>>>> approach that is in Cleanline. If this is the method advocated by
>>>>>>> Mitra and Pesaran as in the Chronux toolbox then indeed its the
>>>>>>> same. And highly recommended.
>>>>>>> -----Original Message-----
>>>>>>> Date: Friday, January 17, 2014 1:21:30 pm
>>>>>>> To: mullen.tim at gmail.com
>>>>>>> Cc: trotta_gabriele at yahoo.com, drcoben at gmail.com,
>>>>>>> mmiyakoshi at ucsd.edu, widmann at uni-leipzig.de,
>>>>>>> eeglablist at sccn.ucsd.edu
>>>>>>> From: "Vito De Feo" <vito.defeo at zmnh.uni-hamburg.de>
>>>>>>> Subject: Re: [Eeglablist] Filter causality pop_eegfiltnew
>>>>>>>
>>>>>>> Before using the Cleanline (that I used today for the first time)
>>>>>>> I did't use the notch filter, I used a multi taper filtering made
>>>>>>> by Anil Seth. I know that filtering is very bad for later VAR
>>>>>>> modeling, especially notch and high pass. Low pass is better
>>>>>>> (usually I use multi taper filtering to remove the noise lines and
>>>>>>> a low pass causal filter with cut off filtering of 100 Hz).
>>>>>>> Do you think is ok Tim?
>>>>>>> Best
>>>>>>> Vito
>>>>>>>
>>>>>>> Il giorno 17/gen/2014, alle ore 20:53, mullen.tim at gmail.com ha
>>>>>>> scritto:
>>>>>>>
>>>>>>>> Do not notch filter your data! This can be very bad for later VAR
>>>>>>>> modeling -- and IMO bad in general. You can use an adaptive
>>>>>>>> spectral regression method such as that in the Cleanline plugin
>>>>>>>> for eeglab to remove line noise.
>>>>>>>>
>>>>>>>> See Barnett and Seth 2011 and Mitra and Pesaran 1999 for
>>>>>>>> theoretical discussions.
>>>>>>>>
>>>>>>>> Rob, there is no video of the SIFT workshop but the lecture pdfs
>>>>>>>> are online at the eeglab workshop page.
>>>>>>>>
>>>>>>>> Tim
>>>>>>>> -----Original Message-----
>>>>>>>> Date: Friday, January 17, 2014 10:18:32 am
>>>>>>>> To: "
>>>>>>>
>>>>>>>
>>>>>>> _______________________________________________
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>>>>>>
>>>>>> --
>>>>>> John Ochoa
>>>>>> Docente de Bioingeniería
>>>>>> Universidad de Antioquia
>>>
>>>
>>> --
>>> --------- αντίληψη -----------
>>
>>
>>
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>
>
> --
> --------- αντίληψη -----------
--
Pflichtangaben gemäß Gesetz über elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG):
Universitätsklinikum Hamburg-Eppendorf
Körperschaft des öffentlichen Rechts
Gerichtsstand: Hamburg
Vorstandsmitglieder:
Prof. Dr. Christian Gerloff (Vertreter des Vorsitzenden)
Prof. Dr. Dr. Uwe Koch-Gromus
Joachim Prölß
Rainer Schoppik
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