[FieldTrip] When to detrend/demean
Ingrid Nieuwenhuis
inieuwenhuis at berkeley.edu
Mon Jan 7 22:53:19 CET 2013
Thanks JM, all clear now. FAQ updated: Done!
Cheers,
Ingrid
On 1/7/2013 12:07 PM, jan-mathijs schoffelen wrote:
> Dear Ingrid and others,
>
> I forgot to mention, the default has changed one year ago, according
> to the following thread on the list:
>
> http://mailman.science.ru.nl/pipermail/fieldtrip/2012-January/004666.html
>
> Apologies for not having updated the FAQ. Ingrid, would you mind
> giving this a shot?
>
> Best,
>
> Jan-Mathijs
>
>
>
> On Jan 7, 2013, at 8:31 PM, Ingrid Nieuwenhuis wrote:
>
>> Hi Roemer,
>>
>> Thanks for pointing to these questions. I'm a little bit confused
>> about the default behavior with cfg.polyremoval as described there.
>> So it seems the default of polyremoval for ft_freqanalysis is 1,
>> meaning detrending always happens unless you specify otherwise,
>> correct? So that means you don't have to call preprocessing with
>> cfg.detrend = 'yes', correct? But you do have to specify cfg.demean =
>> 'yes' in preprocessing? Or does removing the linear trend (which is
>> the default) in ft_freqanalysis automatically also demean the data?
>> From the text under the figure in
>> http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange it
>> almost looks that way. Because it talks about cfg.polyremoval in the
>> context of demeaning, not detrending. I don't find that text very
>> clear by the way. Also, the title above the figure says "TFR before
>> (left) and after (right) subtracting the DC component in the time
>> domain", while when I look at the code it seems it should be "TFR of
>> channel with large DC component (left) and channel without DC
>> component (right) after ft_freqanalysis without demeaning". I'd be
>> happy to update the FAQ, but first wanted to check whether I
>> understand correctly :)
>>
>> Cheers,
>> Ingrid
>>
>>
>> On 1/7/2013 4:28 AM, Roemer van der Meij wrote:
>>> Hi Vitoria,
>>>
>>> I have only one thing to add to Ingrid's clear explanation. For
>>> frequency analysis, it's mostly a matter of noise. If you do not
>>> demean, the 0Hz been can bleed into all other frequency bins in a
>>> funny but patterned way. For detrending, the same story applies.
>>> When not detrending, the power of the center frequency of the linear
>>> trend (this frequency is very low), can bleed into other bins.
>>>
>>> The FAQs have two great example on this:
>>> http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange
>>> http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange_part_ii
>>>
>>> Both are specific for when using 'mtmconvol' as frequency method
>>> (why this is so is explained shortly in the first FAQ), although in
>>> principle the issues could also occur using the other methods.
>>>
>>> Hope it helps!
>>>
>>> All the best,
>>> Roemer
>>>
>>>
>>>
>>> On Sun, Jan 6, 2013 at 3:24 AM, Ingrid Nieuwenhuis
>>> <inieuwenhuis at berkeley.edu <mailto:inieuwenhuis at berkeley.edu>> wrote:
>>>
>>> Hi Vitoria,
>>>
>>> The problem with these things is, it depends on what your
>>> interested in (hypothesis) and which methods you're using to
>>> analyze the data (ERP or frequanalysis, with our without ICA).
>>> All analysis steps have different pro and cons, so depending on
>>> your hypotheses and effects, what's good in one setup can be bad
>>> in the next. So you have to think of what the measures do, and
>>> how that effects your data.
>>>
>>> But now for some answers :) I'm just using a lot of experience
>>> and some common sense, maybe people can add in some refs and
>>> math if they know :)
>>> Demeaning just subtracts the mean of the specified time window
>>> (or indeed whole trial) from all samples
>>> detrending removes linear trend (you can also remove higher
>>> order trends, just for completeness)
>>>
>>> For ERPs you generally do want to demean using the baseline
>>> window, so the effect cancels out pre-stim. You don't want to
>>> detrend here, since often the ERP can have late components, and
>>> the signal might not be back to baseline yet. If you detrend in
>>> such a case, you will decrease the value samples late in the
>>> trials and increase the values during baseline. You will tilt
>>> the data (end down thus start up). But if you expect a linear
>>> trend due to equipment drift over longer time, that can muddle
>>> the ERP effect, then you might want to detrend. Also when the
>>> signal is noisy (high amplitude noise) at the end (due to speach
>>> artifacts), detrending might be dangerous.
>>>
>>> For frequency analysis, demeaning has (as far as I know) no
>>> effect, since subtracting a constant does not change the
>>> frequency info in the signal. I know people do tend to detrend
>>> before freq analysis (so I also tend to do that), but I have to
>>> admit, I don't know why really. Maybe to get rid of the drift,
>>> so it does not end up in the low frequencies. But again, the
>>> effect of detrending (which freqs it affects) depends how long
>>> your time window is, and which frequencies your interested in.
>>> If you are interested in really low frequencies, detrending
>>> might change your effects.
>>>
>>> Hope this helps somewhat,
>>> Ingrid
>>>
>>>
>>>
>>> On 1/5/2013 2:21 AM, Vitória Magalhães Piai wrote:
>>>
>>> Dear ftrippers,
>>>
>>> I'm having a discussion with a colleague on something that
>>> is still a bit unclear to us. Since I trust the knowledge
>>> going around here a lot, I thought it would be my best
>>> chance to get a good answer: When should we demean/detrend?
>>>
>>> As relevant background, our EEG datasets involve speech
>>> production on every trial.
>>> We read in the data, use ft_databrowser to mark the
>>> artefacts and then do complete artefact rejection with
>>> ft_rejectartifact. The trials often include speech (onset).
>>> I see in the tutorial that the cfg for preprocessing is
>>> pretty simple, and ft_preprocessing default has no
>>> detrend/demean.
>>> But in the FT example 'Reading and pre-processing EEG data',
>>> the cfg is
>>>
>>> cfg.demean = 'yes';
>>> cfg.baselinewindow = [-0.2 0];
>>>
>>>
>>> In my data, I used cfg.demean = 'yes'; with no cfg for the
>>> baseline window because I don't want to correct the signal
>>> with a specific interval (and I assume this will take the
>>> whole segment then).
>>> Our concern is that, given that people speak during part of
>>> the trial (always towards the end), using demean here is not
>>> a good idea (the signal changes induced by moving the jaws,
>>> etc., are included in the calculation). Is this necessarily
>>> the case or can it be fixed with subsequent computations
>>> (see below)? Do I need to go through artefact rejection
>>> again? My guess would be that the damage caused by having
>>> demean here doesn't change that much where the eyeblinks are
>>> and I always take quite broad windows to mark the artefacts,
>>> so at least for the AR I should be safe, but I'd like to
>>> check that with you guys.
>>>
>>> Then, when calculating ERPs, I had both demean and detrend
>>> before timelocking.
>>> But for the TFRs, I didn't do any of these (dunno why). I'm
>>> using the ft_freqanalysis after the 2011 change (removing
>>> the first order linear trend from the time domain data).
>>> Do I need to redo my TFRs or is it enough if I do sanity
>>> checks and everything is in place (like visual alpha and
>>> gamma, etc.)?
>>>
>>> And my last question, for once and for all, so that I get it
>>> right next time from the start (assuming that I'll always
>>> have EEG speech production data with ERPs and TFRs
>>> analysed). Is this the best way to do it?
>>> - preprocess with default (so NO detrend and NO demean)
>>> - then demean and detrend for ft_timelockanalysis and
>>> ft_freqanalysis
>>>
>>> Thanx a lot, and (keeping to the Dutch tradition) all the
>>> best for 2013!
>>> Vitoria
>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>> --
>>> Ingrid Nieuwenhuis PhD
>>> Postdoctoral Fellow
>>> Sleep and Neuroimaging Laboratory
>>> Department of Psychology
>>> University of California, Berkeley
>>> California 94720-1650
>>> Tolman Hall, room 5305
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>>
>>>
>>> --
>>> Roemer van der Meij M.Sc.
>>> PhD Candidate
>>> Donders Institute for Brain, Cognition and Behaviour
>>> Centre for Cognition
>>> P.O. Box 9104
>>> 6500 HE Nijmegen
>>> The Netherlands
>>> Tel: +31(0)24 3655932
>>> E-mail: r.vandermeij at donders.ru.nl <mailto:r.vandermeij at donders.ru.nl>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>> --
>> Ingrid Nieuwenhuis PhD
>> Postdoctoral Fellow
>> Sleep and Neuroimaging Laboratory
>> Department of Psychology
>> University of California, Berkeley
>> California 94720-1650
>> Tolman Hall, room 5305
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> Jan-Mathijs Schoffelen, MD PhD
>
> Donders Institute for Brain, Cognition and Behaviour,
> Centre for Cognitive Neuroimaging,
> Radboud University Nijmegen, The Netherlands
>
> Max Planck Institute for Psycholinguistics,
> Nijmegen, The Netherlands
>
> J.Schoffelen at donders.ru.nl
> Telephone: +31-24-3614793
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
--
Ingrid Nieuwenhuis PhD
Postdoctoral Fellow
Sleep and Neuroimaging Laboratory
Department of Psychology
University of California, Berkeley
California 94720-1650
Tolman Hall, room 5305
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