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

[Scott Makeig]

Cedric -

Forgive me if my comment seemed personal - not my intention.  Rather, I
wanted to speak to the potential folly of not critically examining the
simplifying assumptions underlying use of field jargon terms that tend to
'reify' (make-believe-as-real) some phenomenon -- for example something
referred to as '*Xyz*', implanting a belief (conscious or subconscious)
that '*Xyz*' is actually a unitary phenomenon ('*THE Xyz*'). Examples creep
into M/EEG research often.

For example, researchers within a lab might discuss among themselves, "
*The* average response to [some set of event-locked data epochs] ..." <--
But recorded *where*??  Or, but only somewhat less problematic, they might
discuss, "*The* average response  at [*the*] channel '*Cz*' ... " -- though
in fact there is no universal meaning for the term '*Channel Cz*' -- This
is lab jargon meaning: 'the channel in our lab datasets that includes an
electrode affixed to the scalp at point Cz.'

In fact, however, *no* M/EEG channel measures potential fluctuations at a
single point on the scalp -- rather, M/EEG channels each measure the
difference between potentials at *two* (or some linear combination of *more*)
electrode positions.  Each channel sums potentials (+ *and* -) from all
active brain source areas whose surfaces are roughly-normal to the
electrode attachment points (weighted by some function of distance and
incident angle to the cortical surface) -- plus any and all arriving
non-brain source signals (aka 'artifact' sources).

There is nothing innately wrong in simplifying lab communication by
adopting shorthand 'lab jargon' terms.  Problems arise, however, when their
simplicity is taken (consciously *or* subconsciously) as a far too overly
simplistic and/or unitary model of whatever signal feature is of interest.

Aside: I myself have nearly no interest in M/EEG signals captured at any
single scalp channel -- as each channel mixes signals from too many
(related and unrelated) brain areas to be interpretable as brain dynamics -
or related meaningfully to fMRI or any other 3D brain imaging data.  Nearly
no one examines the raw signals arriving at fMRI systems' receiver coils.
Instead, they examine the computed transforms from these (confusing)
signals to (computed) signal strength within 3D brain voxel neighborhoods.
Why -- in 2026 -- focusing EEG research on analysis of raw scalp channel
signals is thought to be of sufficient interest is (I believe) a question
worth considering ...

Scott

On Thu, May 14, 2026 at 2:46 PM Cedric Cannard via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi Scott,
>
> I completely agree with you. I was just mentioning a method if someone
> wants to obtain this oversimplified "IAF" measure, which tries to address
> the simple problems of split peaks, ambiguous peaks, etc. But still over an
> entire recording, and I agree that it is very misleading.
> At the end of my email, I mentioned that your and Julie's IMA approach is
> the best. Or any method that, as you said, can model well the different
> central tendencies of alpha oscillations within session, within subjects,
> and across subjects. I hope to have an opportunity soon to try the IMA
> plugin and run this type of analysis.
>
> - IMA takes an approach orthogonal to FOOOF. Wherease FOOOF considers the
> *form of the log spectrum* of a data source - in itself - IMA pays no
> attention to the mean log spectrum (removing it from consideration first of
> all). IMA then considers the following question: What maximally distinct
> modes of *log spectral variability* does the data source exhibit across
> time? IMA, for example, could possibly isolate multiple modes whose summed
> activities across time (i.e., in the grand mean spectrum) happened to
> cancel each other out at frequencies of interest. Here, FOOOF would not
> find any evidence of them.
>
> This is a very interesting point from your previous email. I will make
> sure this is looked at in the ongoing community project on 1/f / fooof /
> aperiodic activity.
>
> Thank you,
>
> Cedric
>
> Sent with [Proton Mail](
> https://urldefense.com/v3/__https://proton.me/mail/home__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHY5_Rmrw$
> ) secure email.
>
> On Thursday, May 14th, 2026 at 10:11 AM, Cedric Cannard <
> ccannard at protonmail.com> wrote:
>
> > Hi Scott,
> >
> > I completely agree with you. I was just mentioning a method if someone
> wants to obtain this oversimplified "IAF" measure, which tries to address
> the simple problems of split peaks, ambiguous peaks, etc. But still over an
> entire recording, and I agree that it is very misleading.
> >
> > At the end of my email, I mentioned that your and Julie's IMA approach
> is the best. Or any method that, as you said, can model well the different
> central tendencies of alpha oscillations within session, within subjects,
> and across subjects. I hope to have an opportunity soon to try the IMA
> plugin and run this type of analysis.
> >
> > Cedric
> >
> > Sent with [Proton Mail](
> https://urldefense.com/v3/__https://proton.me/mail/home__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHY5_Rmrw$
> ) secure email.
> >
> > On Wednesday, May 13th, 2026 at 10:41 PM, Scott Makeig <
> smakeig at gmail.com> wrote:
> >
> >> 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!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHEHfl6-g$
> ) -- 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!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFvimQzQQ$
> ) or [across ERPs](
> https://urldefense.com/v3/__https://www.jneurosci.org/content/jneuro/19/7/2665.full.pdf__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFcDeDXLQ$
> ) 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!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFfTadagA$
> ) (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!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHE4SGSmQ$
> ) 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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> >>>>
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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%5D(http://sccn.ucsd.edu/%7Escott) 
> _______________________________________________
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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