<div dir="ltr">Dear Vito,<div><br></div><div>Correct, SIFT can work on both continuous data or epoched multi-trial data and uses the third dimension (epochs) to determine which is the case. If you wish to operate on epoched single trials, you may concatenate the trials into a 2D matrix (chs x pnts*trials) In this case, bear in mind that if you're using a sliding window approach, you will need to discard the results within the period corresponding to 1/2 of the window length at the beginning and end of each trial, since the sliding window will have overlapped two trials here. For single-trial analysis, unless the number of variables (channels/sources) you are modeling is low, you will likely need to use regularized VAR estimators (ridge regression or sparse VAR) or a Kalman filter approach (est_fitMVARKalman) to achieve a reasonable model fit.</div>
<div><br></div><div>It appears to me that your data still has some stationarity or other issues leading to model instability. I recommend trying the following:</div><div><br></div><div>1) clean your data to remove trials, channels/sources, or data subspaces with large artifacts -- especially blinks and muscle artifacts, which are not well-modeled by a stationarity VAR process</div>
<div>2) downsample the data to an appropriate sampling rate (256 Hz is typical for EEG)</div><div>3) apply local detrending in SIFT pre-processing, if drift is present</div><div>4) decrease the window length to (e.g.) 0.5 sec to produce a more locally stationarity signal.</div>
<div>5) increase the model order if needed (you might check the model order selection procedure to help determine this)</div><div><br></div><div>Tim</div><div class="gmail_extra"><br><br><div class="gmail_quote">On Sun, Jan 19, 2014 at 12:08 PM, Vito de Feo <span dir="ltr"><<a href="mailto:vito.defeo@zmnh.uni-hamburg.de" target="_blank">vito.defeo@zmnh.uni-hamburg.de</a>></span> wrote:<br>
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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!</p><div><br><br>
Vito <br><br>
Quoting Andreas Widmann <<a href="mailto:widmann@uni-leipzig.de" target="_blank">widmann@uni-leipzig.de</a>>:</div><p></p><div><div>
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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>
<div>
Andreas</div>
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Am 18.01.2014 um 15:47 schrieb "jfochoaster ." <<a href="mailto:jfochoaster@gmail.com" target="_blank">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" target="_blank">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>
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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>
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-----Original Message-----<br>
Date: Friday, January 17, 2014 1:21:30 pm<br>
To: <a href="mailto:mullen.tim@gmail.com" target="_blank">mullen.tim@gmail.com</a><br>
Cc: <a href="mailto:trotta_gabriele@yahoo.com" target="_blank">trotta_gabriele@yahoo.com</a>, <a href="mailto:drcoben@gmail.com" target="_blank">drcoben@gmail.com</a>, <a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>, <a href="mailto:widmann@uni-leipzig.de" target="_blank">widmann@uni-leipzig.de</a>, <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>
From: "Vito De Feo" <<a href="mailto:vito.defeo@zmnh.uni-hamburg.de" target="_blank">vito.defeo@zmnh.uni-hamburg.de</a>><br>
Subject: Re: [Eeglablist] Filter causality pop_eegfiltnew<br>
</div>
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" target="_blank">mullen.tim@gmail.com</a> ha scritto:<br><div>
<br>
> 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>
><br>
> See Barnett and Seth 2011 and Mitra and Pesaran 1999 for theoretical discussions.<br>
><br>
> Rob, there is no video of the SIFT workshop but the lecture pdfs are online at the eeglab workshop page.<br>
><br>
> Tim<br>
> -----Original Message-----<br>
> Date: Friday, January 17, 2014 10:18:32 am<br>
> To: "<br>
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