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

[m za]

Hi all,

I think one key limitation of many traditional approaches is that they rely
purely on stationary spectral analysis, while EEG is inherently
non-stationary and dynamic.
In that sense, time–frequency methods (e.g., wavelet-based approaches) can
provide a more informative characterization of alpha by capturing its
temporal variability, rather than relying only on averaged spectral power.
At the same time, separating oscillatory peaks from the aperiodic
background (e.g., using methods like FOOOF (Fitting Oscillations and One
Over F)) is important to avoid confounds in alpha power estimation.
This is particularly important given inter-individual variability, where
using individualized peak frequencies and accounting for aperiodic activity
can improve the reliability of alpha characterization. Adaptive
decomposition methods may also offer complementary ways to capture
subject-specific structure, although this requires further validation.

On Thu, 30 Apr 2026, 06:10 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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