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

[Scott Makeig]

Cedric -

When you write of detecting (the) "Individual Alpha Frequency (IAF)", you
reify the term introduced 30 years ago by Klimesch
<https://urldefense.com/v3/__https://journals.lww.com/clinicalneurophys/fulltext/1996/11000/Alpha_Frequency,_Reaction_Time,_and_the_Speed_of.6.aspx?casa_token=oAAhkPMByY0AAAAA:OXBNWqxRPYfRdk-oqwrPa9gGH8oibz3ujgYREp5YQ1kRsHCjfCozo8ChE6KN2k4MoP-x8NBacQQygifizS7MNJw__;!!Mih3wA!H8nFsgPl6moOZD26yYE4dWZNiyufmDPpw3sS1NMOe-5Q1Tmi7DHdgBiHGRT4pH3IzDHim5JQnt1wEPHW--Re$ >
-- a term that our work (specifically, results shown in this poster
<https://sccn.ucsd.edu/~julie/AlphaPosterMini.pdf > by Julie Onton)
demonstrated to us was clearly a major oversimplification. Alpha range
activities, whether from occipital/parietal ('alpha'), somatomotor ('mu'),
auditory ('tau') cortex, or elsewhere, are in general not fixed within
subject -- neither over space (cortical source location) nor over time
(within session, as shown in this poster)
<https://sccn.ucsd.edu/~julie/AlphaIMposter.pdf >.

I've often seen how, in science, giving some phenomenon a (singular) name
can give rise to an uncritically held belief that what is being named is in
fact a singular phenomenon -- e.g., 'the' (supposedly unitary) 'P300' ERP
peak versus its other originally proposed designation ('Late Positive
Complex (LCP)' summing distinct evoked activities in multiple cortical
areas. In these papers, we showed a late positive peak in scalp ERPs
(across a range of scalp channels) can be accounted as summing
positive-going potentials (with differing time courses) from a number of
cortical areas whose projected signals -- either across the session
<https://urldefense.com/v3/__https://journals.plos.org/plosone/article/file?id=10.1371*journal.pbio.0020176&type=printable__;Lw!!Mih3wA!H8nFsgPl6moOZD26yYE4dWZNiyufmDPpw3sS1NMOe-5Q1Tmi7DHdgBiHGRT4pH3IzDHim5JQnt1wEFa70Axu$ >
or across ERPs <https://urldefense.com/v3/__https://www.jneurosci.org/content/jneuro/19/7/2665.full.pdf__;!!Mih3wA!H8nFsgPl6moOZD26yYE4dWZNiyufmDPpw3sS1NMOe-5Q1Tmi7DHdgBiHGRT4pH3IzDHim5JQnt1wEJ3JjEyj$ >
each averaging event-related activity in one of the many task conditions --
are maximally distinct.

Here, the example is the concept of '*the*' IAF. giving it a unitary name
('the IAF') does not means it exists as such -- though the claim did build
on early visual observations
<https://urldefense.com/v3/__https://journals.sagepub.com/doi/pdf/10.1177/003591575705001013__;!!Mih3wA!H8nFsgPl6moOZD26yYE4dWZNiyufmDPpw3sS1NMOe-5Q1Tmi7DHdgBiHGRT4pH3IzDHim5JQnt1wECayF4uA$ > (more
than 70 years ago) that alpha peak frequencies in EEG data recorded under
similar conditions can and do differ between individuals. [Note interesting
fact: the first EEG Fourier analysis was reported by Grass
<https://urldefense.com/v3/__https://journals.physiology.org/doi/pdf/10.1152/jn.1938.1.6.521__;!!Mih3wA!H8nFsgPl6moOZD26yYE4dWZNiyufmDPpw3sS1NMOe-5Q1Tmi7DHdgBiHGRT4pH3IzDHim5JQnt1wEMklNYiQ$ > nearly 90
years ago -- in 1938!]

On Wed, May 13, 2026 at 7:31 PM Cedric Cannard via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> There is also the non-parametric technique for detecting Individual Alpha
> Frequency (IAF) developed by Corcoran:
>
> https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/29357113/__;!!Mih3wA!Fo8w8N5UBFlY7iDyii253gF46GFSvgj4a3s8T2MvPfEjRiwul0MvwVpdQewM9tiwTQWymxmZ5e-GTAXmq2V2j4I-Ew$
> It offers both the alpha peak frequency, and center of gravity, to account
> for I individuals with split peaks, absent peaks, etc.
> And detects the insidious end frequency bounds from the data, so it is
> assumption free.
>
> -> This method is easily available via the BranBeats EEGLAB plugin
> (feature extraction mode):
> https://urldefense.com/v3/__https://github.com/amisepa/BrainBeats__;!!Mih3wA!Fo8w8N5UBFlY7iDyii253gF46GFSvgj4a3s8T2MvPfEjRiwul0MvwVpdQewM9tiwTQWymxmZ5e-GTAXmq2XDs00OGw$
>
>
> Although I think Scott’s IMAT recommendation is the strongest.
>
> Cedric
>
> Sent from Proton Mail for iOS.
>
> -------- Original Message --------
> On Sunday, 05/03/26 at 07:59 m za via eeglablist <eeglablist at sccn.ucsd.edu>
> wrote:
> 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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-- 
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