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Dear all,<br><br><br>
sorry if I write again. I am very interested to know better SIFT!<br><br>
I have understood that the stability test includes also the stationarity test. So sorry for my prevoius question.<br>
Now I am trying to understand if SIFT uses a multitrial approch or a single trial approach. I guess that if the signal is not epoched it use a single trial approach.<br><br>
Now I have problems with the consistency (I attach two pictures). The consistency is very low as you can see. What can I do? Should I increase the model order or should I decrease the moving windows length? (now it is 1.5 s, I could decrese to 0.5 s beacuse the signal is strongly not stationary).<br><br>
Thank you!<br><br>
Vito <br><br>
Quoting Andreas Widmann <<a href="mailto:widmann@uni-leipzig.de">widmann@uni-leipzig.de</a>>:</p>
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Dear all,</div>
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not directly related to your question and SIFT, but eegfilt is deprecated and I would recommend not using it any longer.</div>
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Best,</div>
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Andreas</div>
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Am 18.01.2014 um 15:47 schrieb "jfochoaster ." <<a href="mailto:jfochoaster@gmail.com">jfochoaster@gmail.com</a>>:<br>
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Hello all,<br>
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I'm following the SIFT tutorial, the section 6.5.1.3 is about filtering, talk about eegfilt, about the zero-phase (acausal) filter<br>
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Is better forget this section of filtering and use the recommendations in the past emails?<br>
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Are these recommendation critical for the analysis?, I mean, there is a lot of work about MVAR models in ECoG data</div>
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Best wishes<br>
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John</div>
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On Fri, Jan 17, 2014 at 11:05 PM, <a href="mailto:mullen.tim@gmail.com">mullen.tim@gmail.com</a> <span dir="ltr"><<a href="mailto:mullen.tim@gmail.com" target="_blank">mullen.tim@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Oh thats interesting. I had not seen Anil's multitaper filter (might be fairly recent). But possibly it is exactly the same approach that is in Cleanline. If this is the method advocated by Mitra and Pesaran as in the Chronux toolbox then indeed its the same. And highly recommended.<br><div class="im">
-----Original Message-----<br>
Date: Friday, January 17, 2014 1:21:30 pm<br>
To: <a href="mailto:mullen.tim@gmail.com">mullen.tim@gmail.com</a><br>
Cc: <a href="mailto:trotta_gabriele@yahoo.com">trotta_gabriele@yahoo.com</a>, <a href="mailto:drcoben@gmail.com">drcoben@gmail.com</a>, <a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a>, <a href="mailto:widmann@uni-leipzig.de">widmann@uni-leipzig.de</a>, <a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a><br>
From: "Vito De Feo" <<a href="mailto:vito.defeo@zmnh.uni-hamburg.de">vito.defeo@zmnh.uni-hamburg.de</a>><br>
Subject: Re: [Eeglablist] Filter causality pop_eegfiltnew<br>
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Before using the Cleanline (that I used today for the first time) I did't use the notch filter, I used a multi taper filtering made by Anil Seth. I know that filtering is very bad for later VAR modeling, especially notch and high pass. Low pass is better (usually I use multi taper filtering to remove the noise lines and a low pass causal filter with cut off filtering of 100 Hz).<br>
Do you think is ok Tim?<br>
Best<br>
Vito<br><br><br>
Il giorno 17/gen/2014, alle ore 20:53, <a href="mailto:mullen.tim@gmail.com">mullen.tim@gmail.com</a> ha scritto:<br><div class="im HOEnZb">
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> Do not notch filter your data! This can be very bad for later VAR modeling -- and IMO bad in general. You can use an adaptive spectral regression method such as that in the Cleanline plugin for eeglab to remove line noise.<br>
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> See Barnett and Seth 2011 and Mitra and Pesaran 1999 for theoretical discussions.<br>
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> Rob, there is no video of the SIFT workshop but the lecture pdfs are online at the eeglab workshop page.<br>
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> Tim<br>
> -----Original Message-----<br>
> Date: Friday, January 17, 2014 10:18:32 am<br>
> To: "<br>
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<br><br clear="all"><br>
--<br>
John Ochoa<br>
Docente de Bioingeniería<br>
Universidad de Antioquia</div>
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