[Eeglablist] Fwd: SIFT toolbox

[AIKATERINI LYMPERIDOU]

Thank you very much for your time and your answer!
That was really helpful for me!


Quoting Matt Gerhold <matt.gerhold at gmail.com>:

> If the residuals aren't white, that means not all the information in the
> timeseries has been absorbed into the coefficient matrix.
>
>
>
> In terms of the math, if you look at the equations for the VAR model, the
> modelling procedure should extract the coefficient matrices upto a given
> lag/model order, added to this is a noise component. The noise component is
> by definition, gaussian and white: all the observations are independent
> from each other, across time and across channels. So, if you have
> successfully modelled the data, then the residuals should be white. If you
> have one or two models across time that don't fit this, i.e. 96.5% of
> models are white, then that is alright as well.
>
>
>
> So, you have to go back to the pre-processing stage and tend to your
> datasets to ensure you get all the information out of the data and into the
> model coefficients. It all has to do with careful pre-processing. They are
> not the easiest models to fit, so you have to proceed with a certain level
> of determination.
>
>
> On Mon, Jul 8, 2019 at 1:52 PM AIKATERINI LYMPERIDOU <
> med1p1040133 at med.uoc.gr> wrote:
>
>> Thank you for the answer Matthew.
>>
>> The thing is that my model pass the "percent consistency test" and the
>> "stability index test" but did not pass the "Residual whiteness test".
>> Is that an important issue as well?
>>
>> Quoting Matt Gerhold <matt.gerhold at gmail.com>:
>>
>> > Aikaterini:
>> >
>> > You do need to validate your model(s). Reviewers and examiners will
>> request
>> > validation statistics. This should include:
>> >
>> >    - Whiteness of residuals
>> >    - Test for model stability
>> >    - A check if the residuals are Gaussian (optional for some)
>> >
>> > This tells us whether the modelling procedure has been successfully
>> applied
>> > to the data. Window length in relation to model order can contribute to
>> > estimation bias, so these parameters have to be chosen carefully. Often,
>> > one has to pre-process cautiously and iterate through a few models in
>> order
>> > to get a good final model that satisfies the criteria.
>> >
>> > Rgds,
>> >
>> > Matthew
>> >
>> >
>> >
>> > On Mon, Jul 8, 2019 at 12:51 PM AIKATERINI LYMPERIDOU <
>> > med1p1040133 at med.uoc.gr> wrote:
>> >
>> >>
>> >> I just noticed that I can compute the connectivity measures
>> >> successfully even my model does not pass the Validation Tests. So if
>> >> you are facing the same problem, just go the next step without
>> >> bothering a lot. The most important things are Model Order selection,
>> >> the selection of the window step and the window length.
>> >>
>> >> Hope you the very best!
>> >>
>> >>
>> >>
>> >> Quoting AIKATERINI LYMPERIDOU <med1p1040133 at med.uoc.gr>:
>> >>
>> >> > Please note that the window for the "Model Validation Results"
>> >> > appears but none of the windows pass the "Residual Whiteness Test"
>> >> > and the graph for the "Whiteness Significance" does not appear.
>> >> >
>> >> > I cannot understand if this is a problem of preprocessing or I am
>> >> > missing something.
>> >> >
>> >> >
>> >> >
>> >> > Quoting AIKATERINI LYMPERIDOU <med1p1040133 at med.uoc.gr>:
>> >> >
>> >> >> Hello to everyone!
>> >> >>
>> >> >> I am using the SIFT toolbox (EEGLAB-compatible toolbox for analysis
>> >> >> and visualization of multivariate causality).
>> >> >>
>> >> >> My data are task-related (button pushed when the subject realize if
>> >> >> he see a figure inside the context of the whole pictue ). I used
>> >> >> filtering (bandpass filter (2:65), hamming window) and
>> >> >> preprocessing and the other steps the group of SIFT recommends for
>> >> >> analysis in their "SIFT_Practicum"
>> >> >> (https://sccn.ucsd.edu/wiki/SIFT) for a sample dataset.
>> >> >>
>> >> >>
>> >> >> Unfortunately, in the step of "Validate Model" I get this error.
>> >> >>
>> >> >> Warning: defaultParallelConfig will be removed in a future release.
>> Use
>> >> >> parallel.defaultClusterProfile instead.
>> >> >> WARNING: The MVAR algorithm 'BSBL L1' depends on BSBL_L1_noise.m,
>> >> >> which cannot be located on the path. This algorithm will not be
>> >> >> available.
>> >> >> Constant detrending each window...
>> >> >> done.
>> >> >> Done.
>> >> >> WARNING: The MVAR algorithm 'BSBL L1' depends on BSBL_L1_noise.m,
>> >> >> which cannot be located on the path. This algorithm will not be
>> >> >> available.
>> >> >> Warning: defaultParallelConfig will be removed in a future release.
>> Use
>> >> >> parallel.defaultClusterProfile instead.
>> >> >> Constant detrending each window...
>> >> >> done.
>> >> >> Done.
>> >> >> Undefined function or variable "f".
>> >> >>
>> >> >> Error in findobjuser (line 45)
>> >> >> h = h(f);
>> >> >> Error in PropertyGrid/FindPropertyGrid (line 409)
>> >> >>            h = findobjuser(@(userdata) userdata.(member) == obj,
>> >> >> '__PropertyGrid__');
>> >> >>
>> >> >> Error in PropertyGrid.OnPropertyChange (line 428)
>> >> >>            self = PropertyGrid.FindPropertyGrid(obj, 'Model');
>> >> >>
>> >> >> Done.
>> >> >>
>> >> >>
>> >> >> Could anyone face the same error as me?
>> >> >> I would really appreciate it if you have any idea why this is
>> happening.
>> >> >>
>> >> >> Thank you,
>> >> >> Katerina
>> >> >>
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>> >> >
>> >> >
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