[FieldTrip] Oscillatory power normalization
matt.mollison at gmail.com
Tue May 15 01:05:10 CEST 2012
I know this is a slow reply, but I was waiting to see if someone would
comment on Joe's questions. Anyway, thank you for your explanation. It
makes sense that the subtraction would control for 1/f within each
frequency. However, I am curious about the points that Joe brought up and I
hope someone can still comment on them.
Your first point seems quite important, especially when averaging across a
range of frequencies is a common thing to do in the literature. Does anyone
know if it's correct that higher frequencies will get washed out by lower
ones when averaging within a frequency band?
To add to Joe's questions, could normalizing power ever be a bad thing to
do? It seems like it would be reasonable for FieldTrip to at least have the
option so that one could use cfg.keeptrials='no' with ft_freqanalysis and
would not need to have cfg.keeptrials='yes' for followed by the
ft_freqbaseline steps that Stephan mentioned.
Not sure about your second point regarding spectral density, but I would
also like to know more.
Thanks, everyone, for your knowledge in these matters.
Univ. of Colorado at Boulder
Dept. of Psychology and Neuroscience
matthew.mollison at colorado.edu
On Tue, Apr 24, 2012 at 12:41 AM, Joseph Dien <jdien07 at mac.com> wrote:
> I'm new to spectral analysis so take anything I say with a grain of salt:
> 1) If one intends on taking the average of a band (like 8-12Hz for alpha),
> seems like maybe helpful to correct for 1/f so the lower bands don't
> 2) Another issue is spectral density (correcting for frequency bin width
> for discrete Fourier). As far as I can tell, FieldTrip isn't doing this.
> Seems like it should be standard. Or at least it should say in the
> documentation whether it is being done. Am I wrong?
> On Apr 23, 2012, at 5:54 AM, Eelke Spaak wrote:
> > Hi Matt,
> > When you are comparing power across conditions, it is not really
> > necessary to apply an explicit correction for the dominant 1/f
> > component of the raw spectrum. Since this 1/f component is present in
> > both conditions, when you subtract power in one condition from power
> > in another condition (or compute the ratio, or log-ratio, or relative
> > change, or whatever), the 1/f will cancel out and you will only be
> > left with whatever is due to your experimental manipulation. This is
> > true because the contrast is done per frequency. (Note that comparing
> > activity versus baseline is just a special case of looking at a
> > contrast between conditions, so the same argument holds there.)
> > The only time when an explicit correction for 1/f is useful, is when
> > you want to look at raw power. The most dominant oscillatory features
> > (visual alpha, visual contrast induced gamma...) will usually be
> > evident in raw spectra without such a correction, by the way.
> > Correcting for 1/f can be done in many ways, the most simple one is
> > simply taking the logarithm of power, something like:
> > freqCorrected = freqUncorrected;
> > freqCorrected.powspctrm = log10(freqCorrected.powspctrm);
> > Or you could take the first derivative in the time domain (equivalent
> > to multiplying the spectrum with f, search for post by Robert on this
> > on the FT list). Or you could take the log of both the frequency- and
> > power axes, then fit a line, and subtract it, then transform back
> > (10^corrected data).
> > But, the main point is: in the vast majority of typical cognitive
> > experiments, correcting for 1/f is not needed.
> > Best,
> > Eelke
> > On 23 April 2012 05:54, Matt Mollison <matt.mollison at gmail.com> wrote:
> >> Hi FieldTrippers,
> >> In almost all the papers I've read involving oscillatory power, some
> kind of
> >> transformation is done to the data due to the 1/f power spectrum effect
> >> (power decreases as frequency increases). I'm mostly looking at
> >> within-subjects experiments (every subject behaved in all conditions)
> >> comparing conditions across subjects, but it seems like normalizing the
> >> power spectrum should apply in any case (especially if any kind of
> >> parametric stats are done—right?).
> >> Anyway, it's not apparent to me how to use FT functions like
> >> to make these transformations (e.g., log10 normalization, dB
> >> [EEGLab does this], vector length normalization, etc.; the only thing I
> >> is in ft_sourcedescriptives, but I'm not doing source analyses), and it
> >> confuses me why this is the case. I can't find much discussion
> regarding the
> >> 1/f issue on the FT wiki or the mailing list. This seems like an
> >> step that is missing from any frequency analysis workflow. Am I missing
> >> something (meaning I just don't see the option), am I misunderstanding
> >> something (meaning I'm incorrect in this assumption), or is this an
> >> that needs to be fixed?
> >> Thanks,
> >> Matt
> >> --
> >> Univ. of Colorado at Boulder
> >> Dept. of Psychology and Neuroscience
> >> matthew.mollison at colorado.edu
> >> http://psych.colorado.edu/~mollison/
> >> _______________________________________________
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> >> fieldtrip at donders.ru.nl
> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> > _______________________________________________
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> Joseph Dien,
> Senior Research Scientist
> University of Maryland
> E-mail: jdien07 at mac.com
> Phone: 301-226-8848
> Fax: 301-226-8811
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
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