[Eeglablist] Measurement unit of EEGlab power spectrum analysis.

[Makoto Miyakoshi]

Dear Kay,

> we can represent the term “10*log_10 (uV^2/Hz)” as decibel

Yes. Can we not? When I discuss spectrum I pronounce it as 'decibel'.

Makoto

On Mon, May 23, 2016 at 10:49 AM, Kay Sung <ksung3 at jhmi.edu> wrote:

> Thanks for clarification,
>
>
>
> So, “10*log_10 (uV^2/Hz)”,  is basically the log-transformed power
> spectral density where ‘/Hz’ means the width of frequency bin or the
> minimal unit frequency being analyzed.
>
>
>
> Now, going back to my first question, I wonder these statements are ok.
>
>
>
> Assuming that the frequency bin size is constant within a dataset, since
> the power spectral density, uV^2/Hz, is another form of power measure (it
> is perfectly correlated with the absolute power (uV^2)), we can represent
> the term “10*log_10 (uV^2/Hz)” as decibel because it measure the power with
> respect to the unit spectral density.
>
>
>
> Hope I’m not too wrong about this.
>
>
>
> Thanks.
>
>
>
> Kay Sung
>
>
>
> >>>>>>>>>>>>>>>>>>>>>
>
> For your second question below, the scaling is necessary to keep the power
> values consistent across different frequency bin widths.  If you FFT longer
> lengths of data, the power in any given band is split across more frequency
> bins.  Thus, you scale by the bin width (that’s the per Hertz in the
> denominator) so that you can compare power values across data sets with
> different length FFT windows.
>
>
>
> Hope that helps.
>
>
>
> *Cort Horton, PhD*
>
> Director of Research & Development
>
> Qneuro Inc.  www.qneuro.com
>
> Email: chorton at qneuro.com
>
> [image: Qneuro]
>
>
>
> *Kyongje (Kay) Sung, Ph.D*
>
> Research Associate
> *Johns Hopkins University School of Medicine*
> Cognitive Neurology & Neuropsychology – Department of Neurology
>
>
>
> 1629 Thames Street, Suite 350
>
> Baltimore, MD 21231
> P 443-287-8019 | F 410-955-0188
> ksung3 at jhmi.edu
> web.jhu.edu/cognitiveneurology/index.html
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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