[Eeglablist] Filter causality pop_eegfiltnew
Vito de Feo
vito.defeo at zmnh.uni-hamburg.de
Mon Jan 27 02:48:50 PST 2014
Dear Tim,
thank you very much for all you answers and advises. I am following all
your advises and I will let you know.
Here some issues:
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.
2) Thank you very much for explaining me that "A stable VAR model is always
stationary". I was not sure about that. Thank you.
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?
Best,
Vito
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
>
>
> --
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