[Eeglablist] Which is the best way to measure the "alpha" oscillation?

[Antonios Dougalis]

Dear All,

In order to be able to make better sense in the analysis between subjects
in any oscillatory band, I would like to strongly recommend the method of
Cohen 2014 J Neuroscience named 'frequency sliding'.

Cohen MX. Fluctuations in oscillation frequency control spike timing and
coordinate neural networks. J Neurosci. 2014 Jul 2;34(27):8988-98. doi:
10.1523/JNEUROSCI.0261-14.2014. PMID: 24990919; PMCID: PMC6608248.

https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/24990919/__;!!Mih3wA!EO3YGMc0DTFfSkbPOgaMO4TTuxQFA3Hg_1NAEOj1cClJdmNiDMg6aERJ1DSyoEdpaRhUavmUmR-pQe8GECiRqwa5xZMQ1Fgw$ 

In an nutshell, a power spectrum would yield the static power of the
oscillatory peaks of the spectrum for any given segment of EEG. In
frequency sliding, the idea is to generate a downsampled time domain signal
where the instantaneous frequency (IF) of a band of choice is interrogated.
In this way instead of asking, 'is the peak of alpha at 8.2 Hz or so' one
can look at the change in the peak frequency of the oscillation in time and
quantify various metrics on IF like range, stability or even the second
moment of the IF vs time which is the frequency of the frequency!

I highly recommend this for anyone who is really interested in dissecting
out intra-oscillatory band swings especially when these are not stationary
to a partciular central frequency

Kindest Regards

Antonios Dougalis

On Sat, 2 May 2026 at 19:49, 장진원 via eeglablist <eeglablist at sccn.ucsd.edu>
wrote:

> Dear all,
>
> I appreciate some good ideas on identifying alpha frequency peaks. I think
> one interesting part of alpha oscillation is when we start statistical
> analysis across different groups. Depending on environmental or individual
> differences, some subjects show two distinctive alpha frequency peaks,
> while others might demonstrate only one distinctive peak on power spectrum
> (of course there are many cases in which peaks are non-identifiable).
>
> In this sense, it's not easy to make an a priori decision on whether such
> spectral differences are derived from individual differences on EEG or
> significant group features. Maybe implemtation of different pipelines
> altogether is a rough solution.
>
> Best Regards,
> Jinwon
> On Thu, Apr 30, 2026 at 9:53 PM Makoto Miyakoshi via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Hi Jinwon,
> >
> > > Which do you think is the best way to characterize the
> > neurophysiological activity often represented as "alpha" oscillation?
> >
> > FOOOF. See Cedric's recent simulation
> >
> >
> https://urldefense.com/v3/__https://github.com/sccn/OneOverF/discussions/12*discussioncomment-15457848__;Iw!!Mih3wA!GfEA5WyYuIcYmr4WOWO3EJhMidF84M_wSHKSSlcpqa5bKxQRVbe8p_Bur6_VQGKMEUZ8fCFYednTtsYAbx22J_Dqo2Q$
> >
> > Makoto
> >
> > On Wed, Apr 29, 2026 at 10:19 PM 장진원 via eeglablist <
> > eeglablist at sccn.ucsd.edu> wrote:
> >
> > > Hi all,
> > >
> > > There have been long controversies on measuring alpha frequency power.
> > Some
> > > researchers (especially in clinical fields where electrical engineering
> > is
> > > not familiar) use frequency bands (8-12Hz or 8-13Hz) with FFT or
> Welch's
> > > method to obtain spectral power. Other behavioral scientists prefer
> > > subdivisions such as lower alpha band (8-10Hz) and higher alpha band
> > > (10-12Hz). Recent advancement on FOOOF also enables the isolation of
> > > periodic components to discover individual frequency peaks. There are
> > > numerous other techniques that could specify the regions of eeg
> > activities.
> > > Which do you think is the best way to characterize the
> neurophysiological
> > > activity often represented as "alpha" oscillation?
> > >
> > > Best Regards,
> > > Jinwon Chang
> > > _______________________________________________
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