[Eeglablist] Filter causality pop_eegfiltnew

Tim Mullen mullen.tim at gmail.com
Mon Jan 20 11:32:59 PST 2014

Dear Vito,

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

It appears to me that your data still has some stationarity or other issues
leading to model instability. I recommend trying the following:

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
2) downsample the data to an appropriate sampling rate (256 Hz is typical
for EEG)
3) apply local detrending in SIFT pre-processing, if drift is present
4) decrease the window length to (e.g.) 0.5 sec to produce a more locally
stationarity signal.
5) increase the model order if needed (you might check the model order
selection procedure to help determine this)


On Sun, Jan 19, 2014 at 12:08 PM, Vito de Feo <
vito.defeo at zmnh.uni-hamburg.de> wrote:

>  Dear all,
> sorry if I write again. I am very interested to know better SIFT!
> I have understood that the stability test includes also the stationarity
> test. So sorry for my prevoius question.
> 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.
> 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).
> Thank you!
> Vito
> Quoting Andreas Widmann <widmann at uni-leipzig.de>:
>  Dear all,
>  not directly related to your question and SIFT, but eegfilt is deprecated
> and I would recommend not using it any longer.
>  Best,
>  Andreas
> Am 18.01.2014 um 15:47 schrieb "jfochoaster ." <jfochoaster at gmail.com>:
>    Hello all,
> I'm following the SIFT tutorial, the section is about filtering,
> talk about eegfilt, about the zero-phase (acausal) filter
> Is better forget this section of filtering and use the recommendations in
> the past emails?
>  Are these recommendation critical for the analysis?, I mean, there is a
> lot of work about MVAR models in ECoG data
> Best wishes
> John
> On Fri, Jan 17, 2014 at 11:05 PM, mullen.tim at gmail.com <
> mullen.tim at gmail.com> wrote:
>> 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.
>>  -----Original Message-----
>> Date: Friday, January 17, 2014 1:21:30 pm
>> To: mullen.tim at gmail.com
>> Cc: trotta_gabriele at yahoo.com, drcoben at gmail.com, mmiyakoshi at ucsd.edu,
>> widmann at uni-leipzig.de, eeglablist at sccn.ucsd.edu
>> From: "Vito De Feo" <vito.defeo at zmnh.uni-hamburg.de>
>> Subject: Re: [Eeglablist] Filter causality pop_eegfiltnew
>> 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).
>> Do you think is ok Tim?
>> Best
>> Vito
>> Il giorno 17/gen/2014, alle ore 20:53, mullen.tim at gmail.com ha scritto:
>> > 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.
>> >
>> > See Barnett and Seth 2011 and Mitra and Pesaran 1999 for theoretical
>> discussions.
>> >
>> > Rob, there is no video of the SIFT workshop but the lecture pdfs are
>> online at the eeglab workshop page.
>> >
>> > Tim
>> > -----Original Message-----
>> > Date: Friday, January 17, 2014 10:18:32 am
>> > To: "
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> --
> John Ochoa
> Docente de Bioingeniería
> Universidad de Antioquia
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