[Eeglablist] high frequency oscillation- eeg advice

[Makoto Miyakoshi]

Sorry I wrote a wrong thing.

> (as far as I know, clean_rawdata plugin comes with its IIR filter, which
could be a part of BCILAB for online processing).

This is not true. clean_rawdata() uses clean_drifts() that uses FIR Kaiser
with attenuation of -80dB and user specified transition band width. Sorry
for mistake!

Makoto

On Fri, Jan 27, 2017 at 12:34 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Andreas,
>
> >> And I still don't like 0.1Hz high-pass if you use FIR
> > Why? What is the problem?
>
> Assuming people just enter '0.1' to EEGLAB default FIR filter GUI, it'll
> apply 0.05Hz cut-off high-pass filter with Hamming window. When the
> sampling rate is 250Hz, the model order it calculates is 8251, which is 33
> sec long. Meanwhile, people are usually only interested in the first few
> hundreds milliseconds of the averaged signal. And they subtract
> pre-stimulus baseline mean value from the entire epoch anyway. I don't see
> much reason to apply 0.1-Hz high-pass filter in these cases. Of course, I
> saw papers discussing this issue and I have no objection, but intuitively
> it is still weird to me. I think the point is that people want to claim
> that ERP is a broadband phenomenon, but the way they demonstrate it is not
> satisfactory to me.
>
> By the way, people often complain about our recommended -1 to 2 sec epoch
> to be too long, and our recommended 1-Hz high-pass filter too aggressive.
> But doesn't it makes more sense to apply 1-Hz high-pass filter to 3-sec
> epoch data, compared with applying 0.1-Hz high-pass for 0.8 sec epoch data?
> This kind of unbalancedness makes me feel weird.
>
> > What would be your suggested alternative?
>
> I thought IIR would be more reasonable for such a low cutoff frequency,
> but I have never tried it myself (as far as I know, clean_rawdata plugin
> comes with its IIR filter, which could be a part of BCILAB for online
> processing). It depends on your relative time scale. If I analyze hour-long
> resting state data to target minute-long slow changes, I would use FIR with
> no problem.
>
> I did not know much about stability issue, but this time you made me learn
> it a little bit. Thank you Andreas for always pushing my back in this way.
>
> Makoto
>
>
>
> On Fri, Jan 27, 2017 at 3:28 AM, Andreas Widmann <widmann at uni-leipzig.de>
> wrote:
>
>> Dear Makoto,
>>
>> > And I still don't like 0.1Hz high-pass if you use FIR
>> Why? What is the problem? What would be your suggested alternative?
>>
>> > (and I do not know how bad it is to use IIR... I've heard it can become
>> 'unstable' but I've never seen it myself)
>> Here you go:
>> [b,a]=butter(4,0.1/500,'high');
>> isstable(b,a)
>> freqz(b,a)
>>
>> But note that possible instability is not the main problem with IIR
>> application in electrophysiology. There are workarounds (e.g. for this
>> example using zpk: [z,p,k]=butter(4,0.1/500,'high'); sos=zp2sos(z,p,k);
>> isstable(sos)).
>>
>> Best,
>> Andreas
>>
>>
>> >
>> > > I won't be using granger causality but I will be estimating phase
>> during ITC.
>> >
>> > Should be ok.
>> >
>> > Makoto
>> >
>> > On Thu, Jan 26, 2017 at 2:12 PM, Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>
>> wrote:
>> > Dear Makoto,
>> > I actually switched to the pop eeg filt eeglab function and it now
>> Really attenuated anything >40Hz, and my ERPs are cleaner. However I
>> filtering between 0.1-40Hz at the same time in the GUI (I interned the high
>> pass and low pass simultaneously). Is this OK to do? The frequency response
>> looks OK.
>> >
>> > The filter order was automatically set very high by the GUI, but it's
>> continuous data and I have room without events at the beginning and end of
>> the data so any edge effects can be disgusted. What do you think?
>> >
>> > Also, this is for my ERP analysis- I trained ICA on a 1Hz high pass
>> filtered set.
>> > I won't be using granger causality but I will be estimating phase
>> during ITC.
>> > Best wishes, and thanks,
>> > Jumana
>> >
>> > ------------------------------------------
>> > Jumana Ahmad
>> > Post-Doctoral Research Worker in Cognitive Neuroscience
>> > EU-AIMS Longitudinal European Autism Project (LEAP) & SynaG Study
>> > Room M1.26.Department of Forensic and Neurodevelopmental Sciences (PO
>> 23) | Institute of Psychiatry, Psychology & Neuroscience | King’s College
>> London | 16 De Crespigny Park | London SE5 8AF
>> >
>> > Phone: 0207 848 5359| Email: jumana.ahmad at kcl.ac.uk | Website:
>> www.eu-aims.eu | Facebook: www.facebook.com/euaims
>> >
>> > From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>> > Sent: 26 January 2017 23:49:50
>> > To: Ahmad, Jumana
>> > Cc: eeglablist at sccn.ucsd.edu
>> > Subject: Re: [Eeglablist] high frequency oscillation- eeg advice
>> >
>> > Dear Jumana,
>> >
>> > It's a bad idea to perform ICA with 0.1Hz high-pass filtered data. The
>> cutoff frequency is too low. See this page and the referenced paper.
>> >
>> > https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline
>> #High-pass_filter_the_data_at_1-Hz_.28for_ICA.2C_ASR.2C_and_CleanLine.29
>> >
>> > > A 30Hz low pass does not help to get rid of the oscillation, which is
>> really significant in the data.
>> >
>> > Check the channel frequency spectra and tell me if you see peaks in it.
>> If necessary, you can cut it off using a designed low-pass filter (not like
>> Butterworth...)
>> >
>> > > I use a butterworth filter, which is good for ERP analysis with low
>> phase distortion.
>> >
>> > Do not make qualitative judgement just because something is NOT a
>> classic Butterworth. Of course, if the attenuation is small, the phase
>> 'distortion' is small. But if such small attenuation is not useful, it does
>> not help at all! Also, be careful with the word 'phase'. Particularly
>> people who do not know basics of signal processing believe phase as some
>> magical thing. If you are not performing Granger Causality Analysis or
>> something, you don't need to be so worried about phase issue in practice.
>> >
>> > > I also already run ICA, but in some datasets there is a very
>> significant high frequency oscillation.
>> >
>> >
>> > Remember, to eliminate this is more important than being afraid of
>> qualitative phase issue.
>> >
>> > > However, I can see the high frequency oscillations in my ERP, which
>> is not ideal and now I need to try and get rid of it further.
>> >
>> > Can I filter again on top of the data which already has already
>> undergone ICA- I only use ICA to remove blinks?
>> >
>> >
>> > You'd better to filter the data on continuous state. If you need to
>> filter the epoched data, the half of filter length from both ends becomes
>> unreliable.
>> >
>> > > Should I do cleanline, although it would have to be after ICA now- I
>> read this is not advisable.
>> >
>> > > Should I use a notch filter?
>> >
>> >
>> > If you see > 20dB line noise, Cleanline may not help. In this case, I
>> would simply apply a designed low-pass filter, either Hamming (-50dB) or
>> Blackman (-70dB) using firfilt(). See 'Tools' -> 'Filter the data' ->
>> 'Windowed sinc FIR filter'.
>> >
>> > There are different guys saying different things about data
>> preprocessing. It is confusing, I know! The only good solution for this is
>> to become an engineer yourself...
>> >
>> > Makoto
>> >
>> >
>> >
>> > On Tue, Jan 24, 2017 at 7:00 AM, Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>
>> wrote:
>> > Hi  Everyone,
>> >
>> > I am running a large scale ERP analysis. I filtered 1-40Hz (ICA AMICA),
>> or 0.1-40Hz for the ERP dataset. A 30Hz low pass does not help to get rid
>> of the oscillation, which is really significant in the data. I use a
>> butterworth filter, which is good for ERP analysis with low phase
>> distortion.
>> >
>> > I also already run ICA, but in some datasets there is a very
>> significant high frequency oscillation.
>> >
>> > I do not use cleanline, which is not typical in the literature I have
>> been basing my pipeline on.
>> >
>> >
>> >
>> > However, I can see the high frequency oscillations in my ERP, which is
>> not ideal and now I need to try and get rid of it further.
>> >
>> > Can I filter again on top of the data which already has already
>> undergone ICA- I only use ICA to remove blinks?
>> >
>> > Should I do cleanline, although it would have to be after ICA now- I
>> read this is not advisable.
>> >
>> > Should I use a notch filter?
>> >
>> >
>> >
>> > Any help would be appreciated.
>> >
>> > Best wishes,
>> >
>> > Jumana
>> >
>> >
>> >
>> > ------------------------------------------
>> >
>> > Jumana Ahmad
>> >
>> > Post-Doctoral Research Worker in Cognitive Neuroscience
>> >
>> > EU-AIMS Longitudinal European Autism Project (LEAP) & SynaG Study
>> >
>> > Room M1.09. Department of Forensic and Neurodevelopmental Sciences (PO
>> 23) | Institute of Psychiatry, Psychology & Neuroscience | King’s College
>> London | 16 De Crespigny Park | London SE5 8AF
>> >
>> >
>> >
>> > Phone: 0207848 0260| Email: jumana.ahmad at kcl.ac.uk | Website:
>> www.eu-aims.eu | Facebook: www.facebook.com/euaims
>> >
>> >
>> >
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>> >
>> >
>> > --
>> > Makoto Miyakoshi
>> > Swartz Center for Computational Neuroscience
>> > Institute for Neural Computation, University of California San Diego
>> >
>> >
>> >
>> > --
>> > Makoto Miyakoshi
>> > Swartz Center for Computational Neuroscience
>> > Institute for Neural Computation, University of California San Diego
>> > _______________________________________________
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>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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