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

Tue May 19 10:50:34 PDT 2026

In considering Kukkar and colleagues' preprint alongside this thread, I want to raise several questions in relation to how the preprint does its work. Its opening observation deserves careful examination. ICA-derived dipoles cluster at 19–26 mm below the cortical surface, while the pyramidal generators ICA is taken to recover sit close to the gray matter. That discrepancy is important and has some potential explanations that can be investigated further. The depth and orientation analyses the paper develops bring useful empirical attention to central questions. The Stepanyants reading in the Discussion also appears correct on the textual record, and is worth carrying forward in future writing whatever else one concludes about the larger argument.
The inference from the preprint that needs further examination is the step from the empirical depth distribution to the conclusion that cortical patches must exceed 6–10 cm². The simulation that establishes the patch-size-to-depth-bias mapping uses a four-shell concentric spherical head model. The empirical analysis uses the ICBM152 template with a three-layer BEM forward model and unconstrained DIPFIT inversion. These are distinct models, and they bias depth in the same direction. The spherical model misplaces genuine point sources by 1 to 3 cm (Akalin Acar and Makeig, 2013). The BEM-template pipeline misplaces them by approximately 1 to 2 cm relative to subject-specific MRI (Liu et al., 2023, PMC10336858). The 19–26 mm depth the preprint reports falls within the combined error envelope of these two pipelines. As the analyses are currently presented, the contribution of patch extent cannot be separated cleanly from the contribution of forward-model error.
The control that might help resolve this is doable. Rerunning the analysis on the Leipzig subset for which structural MRI is available, using subject-specific FEM or the New York Head (Huang, Parra, and Haufe, 2016), with a gray-matter-constrained source space, would distinguish the two contributions. If the 80% deeper-than-5-mm figure persists, the large-patch interpretation acquires genuine support. If it diminishes, the explanatory load shifts toward forward-model error and unconstrained inversion.
There is a further point in the SCCN literature that bears on the framing. Scott Makeig and colleagues have already published the methodological tools that address this issue. NFT (Akalin Acar and Makeig, 2010) builds realistic BEM and FEM head models from either individual MRI or high-quality templates. SCALE (Akalin Acar, Acar, and Makeig, 2016) performs joint conductivity and distributed cortical source estimation on realistic FEM, and the simulations in that paper use cortical patch sizes at the 15 cm² scale, while the preprint attributes a 1 cm² scale to the small-patch model. The alternative to spherical-and-template DIPFIT has therefore been available, from the same research group, for over a decade.
Makoto, I would value your perspective on whether the depth distribution your team reports is better understood as evidence against a restrictively small-patch position (from 2012) that the original authors have already substantially revised, or as a sign that the wider EEGLAB user base has not yet absorbed the SCALE-class methodology that addresses these concerns directly. The second reading seems more consistent with what has actually been published.
The methodological tools have been available within this tradition for some time, but they are not yet foregrounded in the user-facing pipeline. DIPFIT with the BEM template remains the default in current EEGLAB tutorials, and Liu, Downey, and colleagues (2023, PMC10336858) showed in a direct four-pipeline comparison that this default produces depth discrepancies of up to 2 cm against subject-specific FEM. The gap between what NFT and SCALE can do and what most users actually run reflects, I suspect, sustained-funding constraints and the difficulty of conveying the centrality of forward-modeling choices in the standard frame of grant review. Funding for EEG methods development has been hard to sustain in recent years. The relationship between forward-modeling assumptions and the inferences we draw from source-localized EEG is foundational, and that case has been made persuasively by Makoto, Scott, and others. But in grant review the arguments for some reason have not landed. The Kukkar et al. preprint, read constructively, is one piece of evidence that this is in fact a central question, and the depth bias it documents has direct implications for any claim about cortical source location from ICA-derived components that follow the DIPFIT and simplifying anatomies more characteristic of EEG use in source inference. A renewed grant initiative, perhaps coordinated across SCCN, Brainstorm, MNE, and FieldTrip teams, to release SCALE-class methodology as a documented, turnkey EEGLAB plugin with workshop training and validation against subject-specific MRI, would address both the methodological gap and the depth-bias concerns the preprint raises. I would be glad to support such an initiative in whatever ways are useful from outside the immediate developer community.
The simulation's source configuration in the preprint also is interesting. A uniform dipole layer with parallel unit-moment dipoles activated simultaneously represents full instantaneous coherence across the patch. The Discussion cites Halgren et al. (2019) and Muller et al. (2018) on traveling waves, and acknowledges that synchrony takes multiple forms across cortex, but the simulation tests only the zero-lag synchronous case (unless I misread the methods or failed to note supplemental files?). Three timescales are operating across the pipeline. The simulation assumes instantaneous full coherence. ICA recovery in the empirical pipeline assumes session-long stationarity of spatial mixing.
The Bayesian distributed-source methods the field has developed for this problem, including cMEM (Chowdhury et al., 2013; Heers et al., 2016), Champagne (Wipf et al., 2010), and Multiple Sparse Priors (Friston et al., 2008), require stable cortical source distributions over the analysis epoch, typically hundreds of milliseconds to seconds. Each assumption is appropriate for some generators and inappropriate for others. Sudden-isolated-sensory-stimulus vertex responses satisfy the instantaneous-coherence case. Ongoing alpha and mu rhythms do not, because intracranial recordings document phase gradients of 0.2 to 1.5 m/s across cortex (Halgren et al., 2019; Zhang, Watrous, Patel, and Jacobs, 2018). Rerunning the simulation with a phase-graded layer at realistic alpha propagation speeds, or with a partially coherent layer at the intra-patch coherence decay reported by Lindén et al. (2011), would separate patch area from synchrony structure within the existing framework. The authors acknowledge that 32–72 cm² of cortical extent would be required to account for the depth bias under the large-patch model alone, "unusually large even under the large-patch hypothesis," in their own description. On their own arithmetic, patch extent is one of several contributors and is not the principal one. Assigning expected magnitudes to head-model error, centroid displacement from time-averaging propagating sources, and ICA aggregation of correlated generators (Ilmoniemi and Sarvas, 2019), before attributing the residual to genuine patch extent, would clarify what the data can support.
This consideration connects directly to the alpha thread Scott, Cedric, and Eugen have been working through. Onton's IMA results and Haegens, Cousijn, Wallis, Harrison, and Nobre (2014) on inter- and intra-individual alpha peak frequency variability both indicate that alpha is heterogeneous in time and space. The temporal window of synchrony assumed by a given method then determines whether alpha is recovered as a unitary source or as multiple generators. ICA with session-long stationary mixing tends toward the first outcome. cMEM, Champagne, and MSP with shorter analysis windows tend toward the second. IMA decomposes log-spectral variability over time, taking a different route to relaxing the timescale assumption. Looking at these together, the IAF question Scott has raised and the patch-size question Makoto's preprint raises emerge as two faces of the same underlying issue. Implicit timescale assumptions in our methods do not consistently match the timescales of the underlying physiology. The path forward involves better forward modeling paired with source-model assumptions tested explicitly against the temporal structure of the data.
A direction is available that draws on what each of these threads already offers. SCALE-class FEM forward modeling with anatomical source-space constraint addresses the depth-bias side. IMA addresses the spectral-variability side. Bayesian patch-extent estimation in the cMEM and Champagne family estimates a continuous source-extent parameter, which removes the need to choose between idealized alternatives. These methods can be paired, and that pairing is a natural next step. The Leipzig dataset the preprint already uses, with structural MRI available for a subset of participants, would be a strong candidate for that integrated reanalysis.
With appreciation for the large amount of work from all of the teams involved, and for this thread, Michael Spezio LIVE Lab, Scripps College

________________________________
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Makoto Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu>
Sent: Monday, May 18, 2026 9:39 PM
To: EEGLAB List <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] Which is the best way to measure the "alpha" oscillation?

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://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fwww.medrxiv.org*2Fcontent*2F10.64898*2F2026.01.23.26344529v2.full.pdf*html__*3BKw!!Mih3wA!CkiOZDkvMIuAqygnlHJVDzdpdlzaWrdn567MZBoSJ1YiRYdgOAj7oWByRGLcDov5RxAlLrklBFBTuSAAVov6jNNRnis*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146195619*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=6fCpZieZz72emBSY9OqpQ9K9cc0dgIkA6Rl9CqltOZQ*3D&reserved=0__;JSUlJSUlJSUlJSUqJSUlJSUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS2xmK78wQ$ <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://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fproton.me*2Fmail*2Fhome__*3B!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHY5_Rmrw*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146214246*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=GRaqVSPXErif1QA*2F9do*2FEApNsYCtYCzMFtLfiJWHfTA*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS2z12AV2g$ <https://urldefense.com/v3/__https://proton.me/mail/home__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHY5_Rmrw$>
> >> ) secure email.
> >>
> >> On Thursday, May 14th, 2026 at 10:11 AM, Cedric Cannard <
> >> ccannard at protonmail.com> wrote:
> >>
> >>> Hi Scott,
> >>>
> >>> I completely agree with you. I was just mentioning a method if someone
> >> wants to obtain this oversimplified "IAF" measure, which tries to
> address
> >> the simple problems of split peaks, ambiguous peaks, etc. But still
> over an
> >> entire recording, and I agree that it is very misleading.
> >>>
> >>> At the end of my email, I mentioned that your and Julie's IMA approach
> >> is the best. Or any method that, as you said, can model well the
> different
> >> central tendencies of alpha oscillations within session, within
> subjects,
> >> and across subjects. I hope to have an opportunity soon to try the IMA
> >> plugin and run this type of analysis.
> >>>
> >>> Cedric
> >>>
> >>> Sent with [Proton Mail](
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fproton.me*2Fmail*2Fhome__*3B!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHY5_Rmrw*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146226290*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=jQa4sM7cBl5q9urSQQWzdg8SwDDPkPkt0s8Ksldsviw*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS3jzu-B3Q$ <https://urldefense.com/v3/__https://proton.me/mail/home__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHY5_Rmrw$>
> >> ) secure email.
> >>>
> >>> On Wednesday, May 13th, 2026 at 10:41 PM, Scott Makeig <
> >> smakeig at gmail.com> wrote:
> >>>
> >>>> Cedric -
> >>>>
> >>>> When you write of detecting (the) "Individual Alpha Frequency (IAF)",
> >> you reify the term [introduced 30 years ago by Klimesch](
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fjournals.lww.com*2Fclinicalneurophys*2Ffulltext*2F1996*2F11000*2FAlpha_Frequency*2C_Reaction_Time*2C_and_the_Speed_of.6.aspx*3Fcasa_token*3DoAAhkPMByY0AAAAA*3AOXBNWqxRPYfRdk-oqwrPa9gGH8oibz3ujgYREp5YQ1kRsHCjfCozo8ChE6KN2k4MoP-x8NBacQQygifizS7MNJw__*3B!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHEHfl6-g*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146237945*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=a2NV0aZzA2CF4hoOrzjd*2BD5OwEc2KNPda*2BotAKwhzbU*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS1T67CSkA$ <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$>
> >> ) -- a term that our work (specifically, results shown in [this poster](
> >> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Fsccn.ucsd.edu*2F*julie*2FAlphaPosterMini.pdf&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146249616*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=2An*2BzoKe3xzSAgVo1sOLK*2FQM3o8i0Rc0chbDzQ*2BrKIk*3D&reserved=0__;JSUlJX4lJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS3ucqKJ2A$ <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://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Fsccn.ucsd.edu*2F*julie*2FAlphaIMposter.pdf&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146261290*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=qw4mrOzFRd3kLRBKe0UZUlrPhF54op68okFtoqeA6dM*3D&reserved=0__;JSUlJX4lJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS1WogvSWg$ <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://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fjournals.plos.org*2Fplosone*2Farticle*2Ffile*3Fid*3D10.1371*journal.pbio.0020176*26type*3Dprintable__*3BLw!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFvimQzQQ*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146274811*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=XucJDrqFkWQ1*2Bgh8IkV5*2Ffw9Z5Ytbvf05iB*2BFDruhg0*3D&reserved=0__;JSUlJSUlJSUlJSUlJSolJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS3T1zq4-A$ <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$>
> >> ) or [across ERPs](
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fwww.jneurosci.org*2Fcontent*2Fjneuro*2F19*2F7*2F2665.full.pdf__*3B!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFcDeDXLQ*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146286303*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=KJAztfNlUp9AyTaYV2pNEWBY4HwUFAtSI98wKAVQTeI*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS2MwPKtXQ$ <https://urldefense.com/v3/__https://www.jneurosci.org/content/jneuro/19/7/2665.full.pdf__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFcDeDXLQ$>
> >> ) each averaging event-related activity in one of the many task
> conditions
> >> -- are maximally distinct.
> >>>>
> >>>> Here, the example is the concept of 'the' IAF. giving it a unitary
> name
> >> ('the IAF') does not means it exists as such -- though the claim did
> build
> >> on early [visual observations](
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fjournals.sagepub.com*2Fdoi*2Fpdf*2F10.1177*2F003591575705001013__*3B!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFfTadagA*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146297661*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=QzMCpIrtB*2FYhwxckmJmXTYcC1zOmH0jLtp9SvKmgvAk*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS0DpAWzGQ$ <https://urldefense.com/v3/__https://journals.sagepub.com/doi/pdf/10.1177/003591575705001013__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxFfTadagA$>
> >> ) (more than 70 years ago) that alpha peak frequencies in EEG data
> recorded
> >> under similar conditions can and do differ between individuals. [Note
> >> interesting fact: the first EEG Fourier analysis was [reported by
> Grass](
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fjournals.physiology.org*2Fdoi*2Fpdf*2F10.1152*2Fjn.1938.1.6.521__*3B!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHE4SGSmQ*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146309053*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=n0FV2F06xKRiGfChd4hL6VcmnZ9Di0mX7gmENChg8zc*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS1W33gbHQ$ <https://urldefense.com/v3/__https://journals.physiology.org/doi/pdf/10.1152/jn.1938.1.6.521__;!!Mih3wA!APd-2_tZlNmRq_OmhshOOp0zLZDgmYpfoM_PeYTkV4LPEM3O8TTvlkUNGYsPY5GKxTqelQPpCNC8a1ZMnxHE4SGSmQ$>
> >> ) nearly 90 years ago -- in 1938!]
> >>>>
> >>>> On Wed, May 13, 2026 at 7:31 PM Cedric Cannard via eeglablist <
> >> eeglablist at sccn.ucsd.edu> wrote:
> >>>>
> >>>>> There is also the non-parametric technique for detecting Individual
> >> Alpha Frequency (IAF) developed by Corcoran:
> >>>>>
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fpubmed.ncbi.nlm.nih.gov*2F29357113*2F__*3B!!Mih3wA!Fo8w8N5UBFlY7iDyii253gF46GFSvgj4a3s8T2MvPfEjRiwul0MvwVpdQewM9tiwTQWymxmZ5e-GTAXmq2V2j4I-Ew*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146320373*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=hGeVwUVPW1S62q7rwvFt6zTtR5GLS3zsX*2BU9AW1WK8w*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS3b_b12Gg$ <https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/29357113/__;!!Mih3wA!Fo8w8N5UBFlY7iDyii253gF46GFSvgj4a3s8T2MvPfEjRiwul0MvwVpdQewM9tiwTQWymxmZ5e-GTAXmq2V2j4I-Ew$>
> >>>>> It offers both the alpha peak frequency, and center of gravity, to
> >> account for I individuals with split peaks, absent peaks, etc.
> >>>>> And detects the insidious end frequency bounds from the data, so it
> is
> >> assumption free.
> >>>>>
> >>>>> -> This method is easily available via the BranBeats EEGLAB plugin
> >> (feature extraction mode):
> >>
> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fgithub.com*2Famisepa*2FBrainBeats__*3B!!Mih3wA!Fo8w8N5UBFlY7iDyii253gF46GFSvgj4a3s8T2MvPfEjRiwul0MvwVpdQewM9tiwTQWymxmZ5e-GTAXmq2XDs00OGw*24&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146331863*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=rdSmSNeB44n3g41JvGcp43tlxgG*2B4aFKR6vOs4dmSeU*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS0ielm_MQ$ <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://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Fsccn.ucsd.edu*2Fmailman*2Flistinfo*2Feeglablist&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146343312*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=QphxhW6*2F0SOxjmFbFU*2FXW8D*2FArG8QKQayTOTthhdytQ*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS1Y5lhKEg$ <https://sccn.ucsd.edu/mailman/listinfo/eeglablist >    .
> >>>>>>
> >>>>> _______________________________________________
> >>>>> To unsubscribe, send an empty email to
> >> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> >> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Fsccn.ucsd.edu*2Fmailman*2Flistinfo*2Feeglablist&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146354870*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=qlK7Nud9x1y4nEP6*2BhcxvHzk3f87Ytazwj7Fk*2Fs6LQk*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS1eaH8tGQ$ <https://sccn.ucsd.edu/mailman/listinfo/eeglablist >    .
> >>>>> _______________________________________________
> >>>>> To unsubscribe, send an empty email to
> >> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> >> https://urldefense.com/v3/__https://nam02.safelinks.protection.outlook.com/?url=https*3A*2F*2Fsccn.ucsd.edu*2Fmailman*2Flistinfo*2Feeglablist&data=05*7C02*7Cmspezio*40scrippscollege.edu*7Cbf3cf14a1fa94e8e67e708deb57c683e*7C47274664281d4e3282489661a922b78c*7C0*7C0*7C639147743146366153*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=ZeA*2FPK0lTSSt5z5i7Vi3BrKeDGgO8nmDI0lKYlAc24I*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!Mih3wA!GRZaw-eJRFKt2O-Yn6ChUlatIvzMPoIxQ93j_ZMYFlLTb8K7ORFPoX48-s9cNKvaNAriwbiigcs6BHbRNN2RKS3neptl4g$ <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, [
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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,
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