[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
fit.

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)

Tim


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 6.5.1.3 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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