[Eeglablist] RegIca AR and SCRLS algorithm

[Μανούσος Κλάδος]

Dear Mahesh,

Let me understand something. In this kind of dataset ICA is running and the problem is with the regression part? If 
so you can perform reg in smaller segments of ics... Also you are able to try another regression scheme based one a single step regression and not in adaptive filters...

Also watch your eog signals ... You have 4 channels but from these channels 2 bipolar signals are obtained  which are going to be used as an input in every adaptive filter...

Everything you need About regica I am available for all of you in my e-mail or even better at Skype(mklados) you can also follow me at twitter (@mklados) where future versions of regica will be announced as well as many other artifact rejection and neuroscientific staff will be pointed

Sincerely yours
Manousos Klados

P.S.

Which is Florian?


___________________________
Manousos Klados
PhD Candidate
Group of Applied Neuroscience 
Lab of Medical Informatics
Medical School
Aristotle University of Thessaloniki
___________________________
iPhone

4 Απρ 2011, 11:10 μ.μ., ο/η Mahesh Casiraghi <mahesh.casiraghi at gmail.com> έγραψε:

> Dear EEGLabbers,
> 
> 
> 
> I am trying here to test if the hybrid methodology proposed by Florian and his group [http://lomiweb.med.auth.gr/gan/mklados/index.php?option=com_k2&view=item&id=25:regica] may be effective in removing eye-artifacts in an experiment where 10 secs epochs need to be segmented.
> 
> 
> 
> The concept of the methodology seems promising to me, but I am nonetheless a bit puzzled with respect to which regression algorithm might be adopted. When it comes to run the code on a real subject (512Hz sampling rate, continuous data, 64 EEG and 4 EOG chans, about 1.30 hours) LMS and CRLS become both unstable and fail after just few steps. H INF ew and tv algorithms, do that too. It seems to me the only option left is to make use of SCRLS_regression.m from the AAR toolbox, but as the relative documentation suggests, the function is not really optimized for fast computation, and the reg procedure seems to take ages to converge [running it in matlab 64, on a 4cores pc, for a 1.30 hours continuous EEG and it is still trying to converge after 23 hours of computation, just one processor used].
> 
> 
> 
> Question is: can someone out there with some experience with AAR toolbox and/or SCRLS algorithm provide some insights on how to play around with the 'lambda', 'sigma', and 'precision' fields of the opt structure so as to come up with a sufficiently accurate output in an acceptable amount of time? I was unable to find any detailed summary or list of practical guidelines/hints concerning these parameters. Furthermore, perhaps someone is aware of a more effective SRLS reg routine...
> 
> 
> 
> Here the code I used to reshape the tri-dimensional chans x samps x trials matrix, run regica, and come back to the cleaned 3 dims matrix. As you see, opt parameters are default, with the exception of .20 instead of .25 for correlation threshold, note that srls is default here. 
> 
> 
> 
> 
>   EEG2DIM.data = reshape(EEG.data, size(EEG.data,1),size(EEG.data,3)*size(EEG.data,2));
> 
>   opt.EOG = [EEG2DIM.data(1,:);EEG2DIM.data((67:69),:)]; %Fp1(1) plus HEOG1, HEOG2, & VEOG
> 
>   opt.M=3;
> 
>   opt.lambda=0.9999;
> 
>   opt.sigma=0.01;
> 
>   opt.prec=50;
> 
>   opt.crittype = 'correlation';
> 
>   opt.corthr = 20;
> 
>   [EEG1] = regica(EEG2DIM.data((1:64),:),opt);
> 
>   [EEG2] = reshape(EEG1, size(EEG1,1), size(EEG.data,2)/size(EEG.data,3), size(EEG.data,3));
> 
>   EEG.data((1:64),:) = EEG2((1:64),:);
> 
> 
> Any insight would be really appreciated,
> 
> 
> 
> Mahesh
> 
> 
> 
>    
> 
> 
> 
> Mahesh M. Casiraghi
> PhD candidate - Cognitive Sciences
> Roberto Dell'Acqua Lab, University of Padova
> Pierre Jolicoeur Lab, Univesité de Montréal
> mahesh.casiraghi at umontreal.ca
> 
> I have the conviction that when Physiology will be far enough advanced, the poet, the philosopher, and the physiologist will all understand each other.
> Claude Bernard
> 
> 
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