<div dir="ltr">Dear John,<div><br></div><div>Honestly speaking, these things are quite mysterious to me, but looks like AIC always suggest very high orders, which Tim said not very good and should follow other suggestions... my problem is that I tend to end up with using model order==30 all the time.</div>
<div><br></div><div>Makoto</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">2014-03-24 20:08 GMT-07:00 jfochoaster . <span dir="ltr"><<a href="mailto:jfochoaster@gmail.com" target="_blank">jfochoaster@gmail.com</a>></span>:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>Thanks Makoto and Bethel<br><br>I used another criteria for order selection, the AIC, and obtained higher orders and better results in the validation of the models<br>
<br></div>Best wishes<br></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra">
<br><br><div class="gmail_quote">On Mon, Mar 24, 2014 at 7:17 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">Dear Bethel and John,<div><br></div><div>I also often encounter this problem. It seems there is no guaranteed solution for this. I almost always use order == 30 with piecewise detrending on. Maybe you want to decrease the ICs to include (which could be against your experimental interest, I know...)</div>
<div><br></div><div>Should we contact Tim?</div><div><br></div><div>Makoto<br><div><br></div><div><br></div></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">2014-03-19 15:35 GMT-07:00 Bethel Osuagwu <span dir="ltr"><<a href="mailto:b.osuagwu.1@research.gla.ac.uk" target="_blank">b.osuagwu.1@research.gla.ac.uk</a>></span>:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi<br>
What model order and window size are you using? You might need to give more details about your pre-processing steps including whether you are working with IC or sensors and the use of spatial filters. If your model order is too low for a given window size then your data will not be modeled properly and the residuals will not be white. So you might try using higher model orders but without over fitting.<br>
<br>
Bethel<br>
________________________________________<br>
From: <a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a> [<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>] On Behalf Of jfochoaster . [<a href="mailto:jfochoaster@gmail.com" target="_blank">jfochoaster@gmail.com</a>]<br>
Sent: 18 March 2014 03:30<br>
To: eeglablist<br>
Subject: [Eeglablist] Problems with the whiteness of the residuals<br>
<div><div><br>
eeglab experts,<br>
<br>
I am working with ECoG data in eeglab using the SIFT plugin. When I am validating the model I obtain good consistency, above the 90%, good model stabilty, 100% stable, but bad whiteness of the residuals, none of the windows are white<br>
<br>
What I can do with these results?<br>
<br>
My data are adquired with the subject in resting, 240 Hz of sampling frequency, 16 channels, around 80 epochs of 2 seconds<br>
<br>
I really appreciate any help you can provide<br>
<br>
Best,<span><font color="#888888"><br>
--<br>
John Ochoa<br>
Docente de Bioingeniería<br>
Universidad de Antioquia<br>
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</font></span></blockquote></div><span><font color="#888888"><br><br clear="all"><div><br></div>-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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</font></span></div>
</blockquote></div><br><br clear="all"><br>-- <br>John Ochoa<br>Docente de Bioingeniería<br>Universidad de Antioquia<br>
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</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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