[FieldTrip] Oscillatory power normalization

Tue May 15 01:05:10 CEST 2012

Eelke,

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.

Joe,

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.
Matt

--
Univ. of Colorado at Boulder
Dept. of Psychology and Neuroscience
matthew.mollison at colorado.edu
http://psych.colorado.edu/~mollison/

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
> dominate?
>
> 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?
>
> Cheers!
>
> Joe
>
>
>
> 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
> ft_freqanalysis
> >> to make these transformations (e.g., log10 normalization, dB
> normalization
> >> [EEGLab does this], vector length normalization, etc.; the only thing I
> see
> >> 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
> important
> >> 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
> issue
> >> 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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> > fieldtrip at donders.ru.nl
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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
> http://homepage.mac.com/jdien07/
>
>
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