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Hi Roemer,<br>
<br>
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
<a class="moz-txt-link-freetext" href="http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange">http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange</a> 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 :)<br>
<br>
Cheers,<br>
Ingrid<br>
<br>
<br>
<div class="moz-cite-prefix">On 1/7/2013 4:28 AM, Roemer van der
Meij wrote:<br>
</div>
<blockquote
cite="mid:CA+WpQ37gyb0Twxk3m6-m+9K1qbPDXudSXxKcuYnRtimQ9-Uqjg@mail.gmail.com"
type="cite">
<div dir="ltr">Hi Vitoria,
<div><br>
</div>
<div style="">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. </div>
<div style=""><br>
</div>
<div style="">The FAQs have two great example on this:</div>
<div style=""><a moz-do-not-send="true"
href="http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange">http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange</a><br>
</div>
<div style=""><a moz-do-not-send="true"
href="http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange_part_ii">http://fieldtrip.fcdonders.nl/faq/why_does_my_tfr_look_strange_part_ii</a><br>
</div>
<div style=""><br>
</div>
<div style="">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.<br>
</div>
<div style=""><br>
</div>
<div style="">Hope it helps!</div>
<div style=""><br>
</div>
<div style="">All the best,</div>
<div style="">Roemer</div>
<div style=""><br>
</div>
<div style=""><br>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">
On Sun, Jan 6, 2013 at 3:24 AM, Ingrid Nieuwenhuis <span
dir="ltr"><<a moz-do-not-send="true"
href="mailto:inieuwenhuis@berkeley.edu" target="_blank">inieuwenhuis@berkeley.edu</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px
0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">Hi
Vitoria,<br>
<br>
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.<br>
<br>
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 :)<br>
Demeaning just subtracts the mean of the specified time
window (or indeed whole trial) from all samples<br>
detrending removes linear trend (you can also remove
higher order trends, just for completeness)<br>
<br>
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.<br>
<br>
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.<br>
<br>
Hope this helps somewhat,<br>
Ingrid
<div class="">
<div class="h5"><br>
<br>
<br>
On 1/5/2013 2:21 AM, Vitória Magalhães Piai wrote:<br>
<blockquote class="gmail_quote" style="margin:0px 0px
0px
0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">Dear
ftrippers,<br>
<br>
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?<br>
<br>
As relevant background, our EEG datasets involve
speech production on every trial.<br>
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).<br>
I see in the tutorial that the cfg for preprocessing
is pretty simple, and ft_preprocessing default has
no detrend/demean.<br>
But in the FT example 'Reading and pre-processing
EEG data', the cfg is<br>
<br>
cfg.demean = 'yes';<br>
cfg.baselinewindow = [-0.2 0];<br>
<br>
<br>
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).<br>
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.<br>
<br>
Then, when calculating ERPs, I had both demean and
detrend before timelocking.<br>
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).<br>
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.)?<br>
<br>
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?<br>
- preprocess with default (so NO detrend and NO
demean)<br>
- then demean and detrend for ft_timelockanalysis
and ft_freqanalysis<br>
<br>
Thanx a lot, and (keeping to the Dutch tradition)
all the best for 2013!<br>
Vitoria<br>
<br>
<br>
<br>
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</blockquote>
<br>
</div>
</div>
<span class=""><font color="#888888">
-- <br>
Ingrid Nieuwenhuis PhD<br>
Postdoctoral Fellow<br>
Sleep and Neuroimaging Laboratory<br>
Department of Psychology<br>
University of California, Berkeley<br>
California 94720-1650<br>
Tolman Hall, room 5305</font></span>
<div class="">
<div class="h5"><br>
<br>
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</blockquote>
</div>
<br>
</div>
<br clear="all">
<div><br>
</div>
-- <br>
<font size="3"><font color="darkblue"><font face="calibri">Roemer
van der Meij M.Sc.<br>
PhD Candidate<br>
Donders Institute for Brain, Cognition and Behaviour<br>
Centre for Cognition<br>
P.O. Box 9104<br>
6500 HE Nijmegen<br>
The Netherlands<br>
Tel: +31(0)24 3655932<br>
E-mail: <a moz-do-not-send="true"
href="mailto:r.vandermeij@donders.ru.nl" target="_blank">r.vandermeij@donders.ru.nl</a></font></font></font>
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<pre wrap="">_______________________________________________
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</blockquote>
<br>
<pre class="moz-signature" cols="72">--
Ingrid Nieuwenhuis PhD
Postdoctoral Fellow
Sleep and Neuroimaging Laboratory
Department of Psychology
University of California, Berkeley
California 94720-1650
Tolman Hall, room 5305</pre>
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