[FieldTrip] Oscillatory power normalization

[Joseph Dien]

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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> 
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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/