[Eeglablist] EEG parameterization

Tim Mullen mullen.tim at gmail.com
Fri Feb 14 19:09:35 PST 2014 

also, the noise covariance (corresponding to the covariance of "erreur" in
your equations) is returned in PE_est.

Tim


On Fri, Feb 14, 2014 at 7:08 PM, Tim Mullen <mullen.tim at gmail.com> wrote:

> Dear Ibtissem,
>
> The SIFT function pop_est_fitMVAR(EEG) will allow you to compute the
> desired coefficients from the data stored in an EEGLAB dataset. They will
> be stored in EEG.CAT.MODEL.AR.
>
> Alternately, you can also check out any of the mvar_* functions which will
> return the coefficient matrix A = [A1 A2 .. Ap] for any data matrix Y,
> using different model fitting approaches.
>
> See the short example below (uses the latest version of SIFT from
> bitbucket: https://bitbucket.org/tmullen/sift_public_beta).
>
> format short% generate 30 seconds of data from a bivariate coupled oscillator model
> [data, ~, AR_true] = sim_varmodel('sim',{'Bivariate Coupled Oscillator'},       ...
>                                   'simParams',{'srate' 100 'Nl' 30 'Nr' 1},     ...
>                                   'genParams',{'sigma' 1 'mu' [] 'noiseDistrib' ...
>                                               {'arg_selection' 'gengauss' 'alpha' 1 'beta' 2}},...
>                                   'makeEEGset',[],'plotData',true,'plotGraphicalModel',true);
> AR_true = AR_true{1};
>
> fprintf('\nTrue VAR coefficients (A_true = [A1 A2]):\n');
> disp(AR_true);
> % estimate VAR coefficients using ARFIT
> ModelOrder = 2;
> [AR_est PE_est] = mvar_vieiramorf(data,ModelOrder);
>
> fprintf('\nEstimated VAR coefficients (A_est = [A1 A2]):\n');
> disp(AR_est);
> % compute percent consistency
> PC = norm(AR_est(:)-AR_true(:))/norm(AR_true(:));
> fprintf('\nPercent consistency: %0.5g%%\n',(1-PC)*100);
>
> True VAR coefficients (A_true = [A1 A2]):
>     0.6000         0         0    0.6500
>          0    0.5000         0   -0.3000
>
> Estimated VAR coefficients (A_est = [A1 A2]):
>     0.5992   -0.0105   -0.0014    0.6466
>     0.0047    0.5277    0.0019   -0.3039
>
> Percent consistency: 97.121%
>
> [image: Inline image 1]
> [image: Inline image 2]
>
>
> Published with MATLAB® 7.14
>
> Best,
> Tim
>
>
>
> On Wed, Feb 5, 2014 at 1:08 AM, Ibtissem KHOUAJA BENFRADJ <
> ibtissem.khouaja at live.fr> wrote:
>
>>
>> Thank you for all your answers.
>>
>> I dont want to disturb you, but one last question please.
>> How can I calculate the coefficients of the matrix.
>>  It is very important to pass the things you taught me.
>>
>>    Y1(t)=a1 y(n-1) - a2 y(n-2)+.....+erreur
>>
>> The goal is to study the time and space variance between electrodes.
>>
>> Thak you in advance for your help
>>
>> Best regards,
>>
>> --------------------
>> Ibtissem KHOUAJA BENFRADJ
>> PhD in computer science
>> Speciality Signal Processing
>> Laboratory LTIM, University of Monastir, Tunisia
>> http://www.labtim.org/accueil.php
>> Laboratory LIGM, Univerisity of Paris-East, France
>> http://ligm.u-pem.fr/
>>
>>
>>
>>
>> ------------------------------
>>
>> ------------------------------
>> From: ibtissem.khouaja at live.fr
>> To: mullen.tim at gmail.com
>> Date: Tue, 28 Jan 2014 01:10:11 +0100
>> CC: eeglablist at sccn.ucsd.edu
>> Subject: Re: [Eeglablist] EEG parameterization
>>
>>
>> Thank you Tim so much for your answers, your support keeps me going
>> andhelp me to better understand the needs of my work.
>>
>>
>> I extrcted the matrix chains EEG from an (.edf) file,
>> I want to find the linear equation that characterizes the dynamics of
>> these signals as described in the article:
>>  "A MATLAB toolbox for Granger causal connectivity analysis",
>>
>> x1(t) = 0.95 √2x1(t − 1) − 0.9025x1(t − 2) + w1(t)
>> x2(t) = 0.5x1(t − 2) + w2(t)
>> x3(t) = −0.4x1(t − 3) + w3(t)
>> x4(t) = −0.5x1(t − 2) + 0.25√2x4(t − 1) + 0.25√2x5(t − 1)+w4(t)
>> x5(t) = −0.25√2x4(t − 1) + 0.25√2x5(t − 1) +w5(t)
>>
>> how these factors can be extracted?
>>
>> thanks in advance,
>>
>> Ibtissem KHOUAJA BENFRADJ
>> PhD in computer science
>> Speciality Signal Processing
>> Laboratory LTIM, University of Monastir, Tunisia
>> http://www.labtim.org/accueil.php
>> Laboratory LIGM, Univerisity of Paris-East, France
>> http://ligm.u-pem.fr/
>>
>>
>>
>>
>> ------------------------------
>> From: mullen.tim at gmail.com
>> Date: Wed, 15 Jan 2014 18:26:48 -0800
>> Subject: Re: [Eeglablist] EEG parameterization
>> To: ibtissem.khouaja at live.fr
>> CC: eeglablist at sccn.ucsd.edu
>>
>> The function (<sift-root>/est/est_fitMVAR_DEKF.m) is included in SIFT
>> 1.0-beta and later, which you can obtain from the EEGLAB plugin manager or
>> the SIFT website.
>>
>> Best,
>> Tim
>>
>>
>> On Wed, Jan 15, 2014 at 5:28 AM, Ibtissem KHOUAJA BENFRADJ <
>> ibtissem.khouaja at live.fr> wrote:
>>
>>
>> Thank you Tim,
>> I actually need the non linear filter to study the EEG signaland to
>> extract these features.
>>
>> I find the linear filte (est_fitMVARKalman) on the net but I can't find
>> the socond one (est_fitMVAREKF).
>> Can you send me the link.
>>
>> thanks a lot.
>> -----------
>> Ibtissem KHOUAJA BENFRADJ
>> PhD in computer science
>> Speciality Signal Processing
>> Laboratory LTIM, University of Monastir, Tunisia
>> http://www.labtim.org/accueil.php
>> Laboratory LIGM, Univerisity of Paris-East, France
>> http://ligm.u-pem.fr/
>>
>>
>>
>>
>> > Date: Wed, 8 Jan 2014 18:04:46 -0800
>>
>> > Subject: Re: [Eeglablist] EEG parameterization
>> > From: mullen.tim at gmail.com
>> > To: ibtissem.khouaja at live.fr; eeglablist at sccn.ucsd.edu
>> > CC:
>> >
>> > There are linear and nonlinear kalman filter-based multivariate
>> autoregressive implementations in the Source Information Flow Toolbox for
>> EEGLAB. See est_fitMVARKalman and est_fitMVARDEKF.
>> >
>> > Tim
>> >
>> > -----Original Message-----
>> > Date: Wednesday, January 08, 2014 1:40:42 pm
>> > To: "EEGLAB-list" <eeglablist at sccn.ucsd.edu>
>> > From: "Ibtissem KHOUAJA BENFRADJ" <ibtissem.khouaja at live.fr>
>> > Subject: Re: [Eeglablist] EEG parameterization
>> >
>> >
>> >
>> >
>> >
>> > Thank you very much for your answers and happy new year 2014.
>> > On my first question, I am based on your set and literature and I will
>> present the EEG signal from a set of evolutionary parameters based on the
>> Kalman filter.
>> > This gives the possibility of reconstructing the signal and to predict
>> its evolution.
>> > Is there someone who work with this type of auto-regressive filter?I
>> need the algorithm in Matlab.
>> > Thank a lot for your precious help, Ibtissem
>> >
>> >
>> >
>> >
>> ---------------------------------------------------------------------------
>> >
>> ---------------------------------------------------------------------------
>> > > From: poil.simonshlomo at gmail.com
>> > > Date: Thu, 19 Dec 2013 21:43:51 +0100
>> > > Subject: Re: [Eeglablist] EEG parameterization
>> > > To: ibtissem.khouaja at live.fr
>> > > CC: eeglablist at sccn.ucsd.edu
>> > >
>> > > Dear Ibtissem,
>> > >
>> > > There are several ways you can characterize an EEG signal. The two
>> > > basic are frequency and amplitude in different frequency bands (and
>> > > spatial location). You can see more here:
>> >
>>
>>
>>
>>
>> --
>> ---------  αντίληψη -----------
>>
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>
>
>
> --
> ---------  αντίληψη -----------
>



-- 
---------  αντίληψη -----------
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