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

[Makoto Miyakoshi]

Hi Scott,

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 ...

Raw scalp topo is useful to evaluate contribution of broad EEG sources. For
high resolution EEG, spline Laplacian (with > 64 channels) may be used.
These two types of data are compensatory. It is recommended to show both of
them together (Nunez and Srinivasan, 2006 Electric Fields of the Brain).
Thus, I agree with Yevgeny. Raw scalp topo has its own usefulness (which
does not mean at all that this is "the original data" in any sense).

If you can't believe such a broad EEG source that spans across multiple
scalp electrodes (and you believe it is rather due to volume conduction),
check out this preprint that is under review. Note that this paper is
entirely based on my SCCN lab meeting on Jan 15, 2019, which has been
available on my Wiki page since then.
https://urldefense.com/v3/__https://www.medrxiv.org/content/10.64898/2026.01.23.26344529v2.full.pdf*html__;Kw!!Mih3wA!CkiOZDkvMIuAqygnlHJVDzdpdlzaWrdn567MZBoSJ1YiRYdgOAj7oWByRGLcDov5RxAlLrklBFBTuSAAVov6jNNRnis$ 

The significance of the current study
The original physiological interpretation of ICA proposed by Makeig and
colleagues
rests on the small-patch source model 14,15,19,23,27–29, an assumption that
has remained
largely unvalidated for more than two decades, despite presence of
counterevidence
31–39. To our knowledge, the present study is the first to explicitly
interrogate this core
premise and to subject it to systematic falsification. Notably, despite the
widespread
adoption of ICA for artifact rejection, its use for extracting putative
brain sources has
remained limited. One plausible reason is that the physiological
interpretation of ICA
critically relies on dipolarity and the associated IDID, yet ICA-derived
dipole fits
frequently yield physiologically implausible depths, which is a pattern we
confirmed in
over 80% of qualified brain components in this study. Such results must
have puzzled
researchers attempting anatomical interpretation, in addition to the more
technical
challenge of post-ICA inter-subject inconsistency. This perspective also
helps explain
why ICA has historically been less integrated with distributed source
modeling
frameworks: dipolarity is inherently a property of single-dipole fitting
and does not
extend to distributed source models.

On Sat, May 16, 2026 at 7:57 AM Евгений Машеров via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Alas, it's a choice between genuine poverty and imagined wealth. Even if
> we're not talking about a "conventional" EEG with 19 channels, but about
> 168 "high-density" EEG channels or hundreds of MEG channels, reconstructing
> the signal from individual voxels turns out to be a Hadamard-ill-posed
> problem. A cubic voxel measuring a centimeter by a centimeter by a
> centimeter is apparently too large for many tasks, but even such a voxel
> has about two thousand (there are two million millimeter voxels). And yet,
> it is received by a dipole potential source, meaning the number of
> parameters increases to six thousand (and I'm not at all sure we can
> justify neglecting the monopole and quadrupole potentials, as taking them
> into account increases the number of parameters to 14 thousand). In other
> words, the amount of information available to us is, at best, 0.3% of what
> we want to extract. And for a typical clinical EEG, it's 0.03%. The rest we
> replace with assumptions. It may be true, but it's an assumption. It may
> lead to plausible results, but plausibility isn't always true.
> As for MRI, thanks to gradient coils, which vary the voltage, it's
> possible to obtain data from specific points. The amount of available
> information is large enough to make a correct decision. Another factor that
> distinguishes MRI from EEG is that, even in MRI of a living organism, we
> don't measure processes specific to living organisms, but purely physical
> ones: the precession of hydrogen nuclei (or other chemical elements) in a
> magnetic field. This effect has been studied quite accurately and is
> reproducible. I'd like to know how the EEG signal is formed (yes, I'm
> familiar with the current theories, and the more I study them, the less I
> understand). And I can't even dream of the brain producing the same signal
> under identical conditions.
> Therefore, I cannot agree with the refusal to study the signal from
> individual scalp electrodes. Despite all the shortcomings you mentioned,
> this is honest information. Moreover, it can be applied practically by
> comparing signals and the body's behavior, obtaining correlations between
> these factors. This is not enough, but it can often be useful.
> Of course, I fully agree with your comment about not confusing a
> conventional name with a real object. But it seems to me that this problem
> goes far beyond the scope of EEG, and is partly philosophical (semantics?
> epistemology? epistemology?) and partly pedagogical (training employees in
> proper "mental hygiene" so that they promptly clear their thinking of
> conventional "technical assumptions").
>
> Your truly Eugen Masherov
>
> > 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
> >>>>>> _______________________________________________
> >>>>>> To unsubscribe, send an empty email to
> >>>>>> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> >>>>>> https://sccn.ucsd.edu/mailman/listinfo/eeglablist    .
> >>>>>>
> >>>>> _______________________________________________
> >>>>> To unsubscribe, send an empty email to
> >> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> >> https://sccn.ucsd.edu/mailman/listinfo/eeglablist    .
> >>>>> _______________________________________________
> >>>>> To unsubscribe, send an empty email to
> >> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> >> https://sccn.ucsd.edu/mailman/listinfo/eeglablist    .
> >>>>
> >>>> --
> >>>>
> >>>> 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) 
> >> _______________________________________________
> >> To unsubscribe, send an empty email to
> >> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> >> https://sccn.ucsd.edu/mailman/listinfo/eeglablist    .
> >
> > --
> > 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 
> > _______________________________________________
> > To unsubscribe, send an empty email to
> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> https://sccn.ucsd.edu/mailman/listinfo/eeglablist   .
> _______________________________________________
> To unsubscribe, send an empty email to
> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> https://sccn.ucsd.edu/mailman/listinfo/eeglablist  .