Dear Baris,<div><br></div><div>Check this out-->
<a href="http://www.sciencedirect.com/science/article/pii/S1746809411000061">http://www.sciencedirect.com/science/article/pii/S1746809411000061</a> </div><div><br></div><div>REGICA is a hybrid method combining the ICA with the regression-based techniques, dealing with the known problems of both methodologies...</div>
<div><br></div><div>If you search the EEGLAB site you can find the REGICA plugin...</div><div><br></div><div>Best Regards</div><div><br></div><div>Manousos Klados<br><br><div class="gmail_quote">2012/2/16 Baris Demiral <span dir="ltr"><<a href="mailto:demiral.007@googlemail.com">demiral.007@googlemail.com</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi,<br>
<br>
Here are the questions:<br>
<br>
a) If we take out artifactual ICs (say, eye blinks), do the final<br>
sensor data loose their crucial phase information?<br>
b) If we apply linear regression based algorithms to exclude<br>
artifacts, will this influence the sensor level phase information?<br>
c) How do these two methods influence sensor based connectivity analysis?<br>
d) Which sensor-based connectivity measures are robust against volume<br>
conduction?<br>
<br>
I favor source- and ICA-based multivariate connectivity analyses where<br>
you really do not need to take out ICs, but work on the components of<br>
interest.<br>
But, there are plenty of papers out there reporting only pairwise<br>
sensor connectivity while ignoring the effects of volume conduction<br>
and artifact correction.<br>
<br>
Thanks,<br>
Baris<br>
--<br>
Ş. Barış Demiral, PhD.<br>
Department of Psychiatry<br>
Washington University<br>
School of Medicine<br>
660 S. Euclid Avenue<br>
Box 8134<br>
Saint Louis, MO 63110<br>
Phone: <a href="tel:%2B1%20%28314%29%20747%201603" value="+13147471603">+1 (314) 747 1603</a><br>
<br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br>Manousos A. Klados<div>________________________________________________<br><div><div>B.Sc Mathematics</div><div>M.Sc Medical Informatics</div><div>________________________________________________</div>
PhD Candidate -- Research Assistant<br>Group of Applied Neurosciences<br>Lab of Medical Informatics<br>School of Medicine<br>Aristotle University of Thessaloniki<br>P.O. Box 323 54124 Thessaloniki Greece<br>_________________________________________________<br>
Tel: +30-2310-999332<br>Fax:+30-2310-999263<br>Website: <a href="http://lomiweb.med.auth.gr/gan/mklados" target="_blank">http://lomiweb.med.auth.gr/gan/mklados</a><br><br>________________________________________________________________<br>
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