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

[Scott Makeig]

Eugen -

You are correct in saying that the band*width* of a spectral measure
depends on the length of the spectral window you use - and (for frequencies
near the half-sampling_rate Nyquist frequency) on the frequency resolution
of the signal -- as well as on the spectral measure you are using (you
mention FFT). The FFT was a great advance in spectral measurement when
computers were (relative to today) very slow, as it made spectral analysis
practical in many circumstances.  That is no longer the case for typical
applications in offline analysis of EEG. Thus, the EEGLAB time/frequency
function does not need to use FFTs (*Fast* Fourier Transforms) computing at
a limited set of frequencies (N/window_length, N=1,2,3,...), but rather,
Fourier transforms at whatever set of center frequencies you wish (equally
or log spaced, etc.).

What is of interest for this thread are differences in exact central
frequencies of alpha bursts.  If you have spectral measurements across
(e.g.) trials or bursts, you can build a distribution of central
frequencies, and then develop statistical margin of error measures that
could distinguish alpha central frequencies with any degree of resolution
(even 0.1 Hz or less), given enough data.

In particular, Independent Modulator Analysis (IMA), in 'learning from the
data' to account for its log spectral power variations across time as a sum
of activities in a set of fixed spectral bands (the Independent Modulators
or IMs), can find central frequencies for each IM peak with quite high
resolution (depending on data length, homogeneity, etc.).

Scott

On Thu, Apr 30, 2026 at 10:11 AM Евгений Машеров via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> It seems to me that there is no single, task-independent way to describe
> the alpha rhythm. Dividing it into subranges is justified in some cases.
> For example, it has been proposed to distinguish three subranges:
> Alpha-1 (low frequency): ~7.7–9.2 Hz (sometimes defined as 8–9 Hz or 8–10
> Hz). Associated with relaxation processes and often predominates during
> decreased cognitive activity or in certain pathological conditions.
> Alpha-2 (mid frequency): ~9.3–10.5 Hz (often 10–11 Hz). Reflects active,
> quiet wakefulness.
> Alpha-3 (high frequency): ~10.6–12.9 Hz (often 12–13 Hz). Associated with
> sensory and cognitive attention processes, functionally closer to the beta
> rhythm.
>
> However, specifying their boundaries with an accuracy of tenths of a hertz
> may be related to processing features, including the sampling frequency and
> the number of points in the Fourier transform. For example, with a sampling
> frequency of 500 Hz and 1024 FFT points, the frequency step will be 0.488
> Hz, meaning specifying them more accurately than half a hertz is pointless.
> However, if 4096 FFT points are used with the same sampling frequency, the
> step will be 0.122 Hz. Using window functions results in a blurring of the
> spectral peaks and a widening of the range boundaries. This can lead to
> incompatibility between data recorded under different conditions and
> processed with different algorithms. Additional uncertainty is introduced
> by the possibility of using total power or peak power, the average
> frequency over the range, or the frequency of the most pronounced peak.
> I believe that different approaches should be used for different classes
> of problems, but a single approach should be used within a single class of
> problems whenever possible.
>
> Your truly
>
> Eugen Masherov
>
> > 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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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott