[Eeglablist] ERSP graphs in microvolts (not in dB)?

[Thomas Ferree]

The usual thinking is that the log of the power spectrum estimate
(whereestimate
is computed, e.g., as average over trials) is distributed as chi^2,and chi^2
approaches Gaussian for many degrees of freedom.

See this paper (section 2.2) for parametric statistical tests
of differences
between power spectra:

Bokil H, Purpura K, Schoffelen J-M, Thomson D, Mitra P (2007).  Comparing
power spectra and

coherences for groups of unequal size.  Journal of Neuroscience Methods 159:
337-345.

We have looked at the distribution of some of our data and have found it
well
fit by chi^2, but I suspect the result is data set dependent.

-- 
Thomas Ferree, PhD
Department of Radiology
UT Southwestern Medical Center

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On Tue, Sep 16, 2008 at 2:15 PM, arno delorme <arno at ucsd.edu> wrote:

> Dear Zach,
>
> > Thanks for the info - I think a lot of us have been wondering about
> > this one ( I actually asked pretty much the same question before
> > your response but it hasn't showed up in the archive yet).  However,
> > the trouble that I am still having is that I'm wondering what is the
> > best way to approach this data for (parametric) statistical
> > analysis.   That is, the default output is in dB which is on a
> > logarithmic scale and thus not appropriate for parametric stats.
>
> Parametric statistics are applicable whenever the data has a
> probability distribution which is close to gaussian. I do not know if
> absolute amplitude has a more gaussian distribution than log power.
> You should test it.
>
> > There is the possibility of nonparametric tests, but my options may
> > be more limited than those of parametric techniques (plus they seem
> > to be somewhat frowned upon in some circles).
>
> Non-parametric ootstrap or permutation test are the norm for single
> subject analysis. For multi-subject analysis, there are applicable
> too. If should test if your data is gaussian to apply a parametric test.
>
> Best,
>
> Arno
>
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