[Eeglablist] EEG parameterization

Tim Mullen mullen.tim at gmail.com
Fri Feb 14 19:08:06 PST 2014 

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:
> >
>
>
>
>
> --
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>
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