# [Eeglablist] high frequency oscillation- eeg advice

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Tue Feb 7 19:05:31 PST 2017

```Dear Andreas and Jumana,

> (0.2 - 0.2 / 2 and 35 + 10 / 2, respectively).

This is much more understandable for I could not remember how eegfiltnew()
determines the transient band width for the low-pass end.

So for the higher frequencies (above 10Hz), log scale (octaves) is not used
to determine the transient band width. I guess this is related to
conventional practice in EEG research.

Makoto

On Fri, Feb 3, 2017 at 5:26 PM, Andreas Widmann <widmann at uni-leipzig.de>
wrote:

> I hope it is ok to keep the discussion on-list. It might be interesting
> also for others.
>
> > Thanks again. I wanted to follow Steve Luck's ERP advice and filter
> between 0.1 as a half amplitude cutoff - 30/40Hz. I went for 40. What does
> this mean in terms of EEGlab's passband edge?
> You only have to shift the passband edge by half the transition band
> width. In your case I would double the transition band width for the
> high-pass part to reduce the order.
>
>       if EEG.srate == 256;
>           EEG = pop_eegfiltnew(EEG, 0.2,[], 4224, 0, [], 1);
>           EEG = pop_eegfiltnew(EEG, [], 35, 86, 0, [], 0);
>       elseif EEG.srate == 1000;
>           EEG = pop_eegfiltnew(EEG, 0.2,[], 16500, 0, [], 0);
>           EEG = pop_eegfiltnew(EEG, [], 35, 330, 0, [], 0);
>       end
>
> will give you 0.1 Hz high-pass and 40 Hz low-pass cutoff frequencies (0.2
> - 0.2 / 2 and 35 + 10 / 2, respectively). In general it is easier to
> directly use the windowed sinc filter function to specify the cutoff (but
> will give the identical result here if used with Hamming window).
>
> > I also wanted to follow Makoto's advice of using 1 Hz filter for running
> ICA (I apply the weights to 0.1). Was I therefore incorrect to use 1-40Hz
> as a passband edge?
> I assume this also referred to cutoff frequency rather than passband edge
> (similar as in the Winkler et al., 2015, publication). However, I think the
> impact is limited here.
>
> Best,
> Andreas
>
> > Best wishes,
> > Jumana
> >
> > -----Original Message-----
> > From: Andreas Widmann [mailto:widmann at uni-leipzig.de]
> > Sent: 03 February 2017 15:03
> > Cc: mmiyakoshi at ucsd.edu; eeglablist at sccn.ucsd.edu
> > Subject: Re: [Eeglablist] high frequency oscillation- eeg advice
> >
> > Filters need transition bands separating passband and stopband. For FIR
> filters the cutoff frequency is in the center of the transition band. In
> pop_eegfiltnew you specify passband edges but reported are most commonly
> the cutoff frequencies. You compute cutoff frequency as passband edge  +/-
> transition band width / 2, here 0.1 - 0.1 / 2 and 40 + 10 / 2. Transition
> band width is reported in the console output of pop_eegfiltnew and can also
> be computed manually from order. See
> http:%2F%2Fhome.uni-leipzig.de%2F~biocog%2Feprints%2Fwidmann_
> 7Cf50ed9fcf01b42b49aee08d44c45bef8%7C8370cf1416f34c16b83c72407165
> 4356%7C0&sdata=GxppGkTzfuGeht3gOpalHbv2dG5%2FhJwmOJFD5%2BCaj8M%3D&
> reserved=0
> > for an introduction into the basic concepts (and also the equations to
> compute transition band width and order).
> >
> > Hope this helps,
> > Andreas
> >
> >> Am 03.02.2017 um 15:49 schrieb Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>:
> >>
> >> Would it be 40Hz low pass? I am unsure why 0.1 has been halved but 40
> hasn't?
> >> Thank you,
> >> Jumana
> >>
> >>
> >> -----Original Message-----
> >> From: Andreas Widmann [mailto:widmann at uni-leipzig.de]
> >> Sent: 03 February 2017 14:45
> >> Cc: mmiyakoshi at ucsd.edu; eeglablist at sccn.ucsd.edu
> >> Subject: Re: [Eeglablist] high frequency oscillation- eeg advice
> >>
> >> Yes, that looks ok. Be aware that passband edge and not cutoff
> frequency is specified in pop_eegfiltnew (for backward compatibility).
> Report these filters as 0.05 Hz high-pass and 45 Hz low-pass (incl. specs:
> zero-phase Hamming windowed sinc FIR, order, and optimally transition band
> width).
> >>
> >> Best,
> >> Andreas
> >>
> >>> Am 02.02.2017 um 13:00 schrieb Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>:
> >>>
> >>> Thank you both for your explanation.
> >>>
> >>> Does this therefore look reasonable:
> >>>
> >>>       if EEG.srate == 256;
> >>>
> >>>           EEG = pop_eegfiltnew(EEG, 0.1,[], 8448, 0, [], 1);
> >>>           EEG = pop_eegfiltnew(EEG, [], 40, 86, 0, [], 0);
> >>>
> >>>       elseif EEG.srate == 1000;
> >>>           EEG = pop_eegfiltnew(EEG, 0.1,[], 33000, 0, [], 0);
> >>>           EEG = pop_eegfiltnew(EEG, [], 40, 330, 0, [], 0);
> >>>       end
> >>>
> >>> I have different sample rates. I work with a very large dataset
> recorded with different systems.
> >>> Best wishes,
> >>> Jumana
> >>>
> >>> From: eeglablist-bounces at sccn.ucsd.edu
> >>> [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Makoto
> >>> Miyakoshi
> >>> Sent: 31 January 2017 03:29
> >>> To: Andreas Widmann <widmann at uni-leipzig.de>
> >>> Cc: eeglablist at sccn.ucsd.edu
> >>> Subject: Re: [Eeglablist] high frequency oscillation- eeg advice
> >>>
> >>> Dear Andreas,
> >>>
> >>> Thank you for your explanations.
> >>>
> >>>> This was just a wild guess based on experience.
> >>>
> >>> Oh ok I see.
> >>>
> >>>> But note that the EEGLAB iirfilt-plugin does implement an Elliptic
> and not a Butterworth IIR filter.
> >>>
> >>> That's true. If I remember correctly, Elliptic filter can make
> theoretically the sharpest filter.
> >>>
> >>>> In MATLAB you compute the impulse response with impz (see the code
> example in my previous post).
> >>>
> >>> Yes, this is a new thing I learned this time! This function is very
> convenient.
> >>>
> >>> Thanks Andreas as always.
> >>>
> >>> Makoto
> >>>
> >>>
> >>>
> >>> On Sat, Jan 28, 2017 at 1:26 AM, Andreas Widmann <
> widmann at uni-leipzig.de> wrote:
> >>> Dear Makoto
> >>>
> >>>>>> A roughly equivalent Butterworth filter will have an estimated
> effective (numerically relevant) impulse response duration of 20592 samples
> (~82s!):
> >>>>
> >>>> I tried to replicate it with EEGLAB iirfilt() but the filter order
> was only 6. How did you calculate this number?
> >>> Not sure whether I understand the question.
> >>>
> >>> Do you mean how I estimated the order (4) of the Butterworth filter in
> my example to be "roughly equivalent“ to the FIR variant? This was just a
> wild guess based on experience. In case necessary I could check how rough
> "roughly equivalent" really is. But note that the EEGLAB iirfilt-plugin
> does implement an Elliptic and not a Butterworth IIR filter.
> >>>
> >>> Or do you mean the comparison of your iirfilt order (6) to the impulse
> response duration (20592) of my example? One cannot directly compare the
> order of IIR and FIR filters. This is a (unfortunately common)
> misconception. For FIR filters (implemented by convolution) the order
> equals the duration of the impulse response (minus one sample). This does
> not hold for IIR filters implemented *recursively*. However, even if the
> impulse response of IIR filters is infinite by definition in digital
> implementations it is practically limited by numerical precision. That is,
> you can compute the impulse response of a digital IIR filter which is now
> also finite and can be applied by convolution exactly like a FIR filter
> finally giving the same result (within limits of numerical precision).
> >>>
> >>> In MATLAB you compute the impulse response with impz (see the code
> example in my previous post). If I remember correctly you may specify
> required numerical precision as an input argument to impz. I trusted the
> default here. You have to get the filter coefficients (b, a) of your EEGLAB
> iirfilt filter and feed them to impz. The length of the resulting impulse
> response times two is the duration to be compared to the order (or IR
> duration) of a FIR filter. Times two because you have to apply the
> non-linear phase IIR derived filter in forward and reverse direction (to
> get zero-phase).
> >>>
> >>> Does this help? Best,
> >>> Andreas
> >>>
> >>>>
> >>>> Makoto
> >>>>
> >>>>
> >>>>
> >>>> On Fri, Jan 27, 2017 at 2:35 PM, Andreas Widmann <
> widmann at uni-leipzig.de> wrote:
> >>>> Dear Makoto,
> >>>>
> >>>> to my understanding these are two different and mostly unrelated
> issues:
> >>>>
> >>>> (1) Recommendations for very low high-pass cutoff frequencies:
> >>>> The filter implementation (IIR vs. FIR) doesn’t really matter here.
> It is correct that you need very long epochs to resolve these very low
> frequencies. However, it is not valid to reverse this conclusion. Also, for
> short epochs apparently small differences in cutoff frequency may matter.
> You do filter the continuous data, that is, epoch length does not directly
> influence the filter effects. High-pass filter distortions (mainly due to
> larger late and slow components biasing smaller early and fast components)
> were shown empirically several times, e.g. most convincingly by Acunzo et
> al.
> >>>>
> >>>> But I agree that this issue is a matter of debate (mainly affecting
> ERP research). Personally, I recommend to individually adjust filter
> cutoffs to the properties of signal and noise. It is in principle possible
> to apply higher high-pass cutoff frequencies if noise level really requires
> this and it is properly demonstrated that filter distortions do not bias
> the conclusions. But people always ask for general recommendations and you
> have to be on the save side...
> >>>>
> >>>> (2) IIR vs. FIR
> >>>> In the first part you argue against long impulse response durations.
> For IIR filters these are usually even (considerably) longer. Besides other
> issues (as the non-linear phase property of IIR filters and its
> side-effects) this is the main reason why I do not recommend IIR filters
> for offline processing in electrophysiology/ERPs. Following your example
> with a slightly higher cutoff of 0.1 Hz and a Hamming windowed sinc FIR
> filter you will have a impulse response duration of 4127 samples (~16s). A
> roughly equivalent Butterworth filter will have an estimated effective
> (numerically relevant) impulse response duration of 20592 samples (~82s!):
> >>>>
> >>>> [b, a]=butter(4, 0.1/125, 'high');
> >>>> h = impz(b, a);
> >>>> length(h)*2 % Times 2 as you have to apply the non-linear phase
> >>>> filter in forward and reverse direction to achieve zero-phase
> >>>> doubling the impulse response duration
> >>>>
> >>>> Thus, if you define filter efficiency as achieved roll-off per
> impulse response duration (as I do) IIR filters can be considerably less
> efficient. Impulse response duration defines the extent the signal is
> convoluted and artifacts and distortions are potentially smeared.
> >>>>
> >>>>> 0.1Hz IIR high-pass filter can be found in EEG amplifiers, so in
> that sense it is not strange at all.
> >>>>
> >>>> IIR filters have their merits if high throughput and/or low delays
> are required. FIR filters have large delays and are computationally less
> efficient (there are however highly optimized implementations). Delay and
> throughput do not matter for offline processing but both are highly
> relevant in amplifiers and BCI applications. Thus, IIR filters are
> frequently preferred for online processing (despite non-linear phase).
> >>>>
> >>>>> I thought it seemed a bit odd to report it like this in a paper.
> >>>>
> >>>> I do not see a problem as long as you reason your approach.
> >>>>
> >>>> Best,
> >>>> Andreas
> >>>>
> >>>>> Am 27.01.2017 um 21:34 schrieb Makoto Miyakoshi <mmiyakoshi at ucsd.edu
> >:
> >>>>>
> >>>>> 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 <
> >>>>>> 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
> >>>>>>
> >>>>>> ------------------------------------------
> >>>>>> 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:
> >>>>>> 8a08d44c434afd%7C8370cf1416f34c16b83c724071654356%7C0&sdata=9bFL
> >>>>>> Fj%2FkVl61CL%2BK749qenX14JOTzEBSNn9t9%2BBCJUk%3D&reserved=0 |
> >>>>>> 4fe3049459e8a08d44c434afd%7C8370cf1416f34c16b83c724071654356%7C0
> >>>>>> &sdata=d3Z6OAcHSkY4ww7a%2Bda2ir1pLENlcVPz%2FsDgLl%2F51fo%3D&rese
> >>>>>> rved=0
> >>>>>>
> >>>>>> From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> >>>>>> Sent: 26 January 2017 23:49:50
> >>>>>> 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.
> >>>>>>
> >>>>>> %2Fsccn.ucsd.edu%2Fwiki%2FMakoto%2527s_preprocessing_pipeline%23
> >>>>>> High-pass_filter_the_data_at_1-Hz_.28for_ICA.2C_ASR.2C_and_Clean
> >>>>>> 459e8a08d44c434afd%7C8370cf1416f34c16b83c724071654356%7C0&sdata=
> >>>>>> N0z4Ru6QsY24Bg4PD7FFPrfC999R8%2F7hLVk5ZvonLDU%3D&reserved=0
> >>>>>>
> >>>>>>> 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-
> >>>>>>
> >>>>>>> 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 <
> >>>>>> 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-
> >>>>>>
> >>>>>> Should I use a notch filter?
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>> Any help would be appreciated.
> >>>>>>
> >>>>>> Best wishes,
> >>>>>>
> >>>>>> Jumana
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>> ------------------------------------------
> >>>>>>
> >>>>>>
> >>>>>> 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:
> >>>>>> 8a08d44c434afd%7C8370cf1416f34c16b83c724071654356%7C0&sdata=9bFL
> >>>>>> Fj%2FkVl61CL%2BK749qenX14JOTzEBSNn9t9%2BBCJUk%3D&reserved=0 |
> >>>>>> 4fe3049459e8a08d44c434afd%7C8370cf1416f34c16b83c724071654356%7C0
> >>>>>> &sdata=d3Z6OAcHSkY4ww7a%2Bda2ir1pLENlcVPz%2FsDgLl%2F51fo%3D&rese
> >>>>>> rved=0
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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
> >>>>>> _______________________________________________
> >>>>>> Eeglablist page:
> >>>>>> 2Fsccn.ucsd.edu%2Feeglab%2Feeglabmail.html&data=01%7C01%7Cjumana
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> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>> --
> >>>>> Makoto Miyakoshi
> >>>>> Swartz Center for Computational Neuroscience Institute for Neural
> >>>>> Computation, University of California San Diego
> >>>>> _______________________________________________
> >>>>> Eeglablist page:
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> >>>>
> >>>>
> >>>>
> >>>>
> >>>> --
> >>>> Makoto Miyakoshi
> >>>> Swartz Center for Computational Neuroscience Institute for Neural
> >>>> Computation, University of California San Diego
> >>>> _______________________________________________
> >>>> Eeglablist page:
> >>>> 724071654356%7C0&sdata=jzEWqaGKCyggwj%2F0Wct9n3Rn%2BXigjJrn2cA%2BiNX
> >>>> JfIc%3D&reserved=0 To unsubscribe, send an empty email to
> >>>> eeglablist-unsubscribe at sccn.ucsd.edu
> >>>> For digest mode, send an email with the subject "set digest mime" to
> >>>> eeglablist-request at sccn.ucsd.edu
> >>>
> >>>
> >>>
> >>>
> >>> --
> >>> Makoto Miyakoshi
> >>> Swartz Center for Computational Neuroscience Institute for Neural
> >>> Computation, University of California San Diego
> >>> _______________________________________________
> >>> Eeglablist page:
> >>> 654356%7C0&sdata=jzEWqaGKCyggwj%2F0Wct9n3Rn%2BXigjJrn2cA%2BiNXJfIc%3D&
> >>> reserved=0 To unsubscribe, send an empty email to
> >>> eeglablist-unsubscribe at sccn.ucsd.edu
> >>> For digest mode, send an email with the subject "set digest mime" to
> >>> eeglablist-request at sccn.ucsd.edu
> >
>
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.
> ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to
> eeglablist-request at sccn.ucsd.edu
>

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