[Eeglablist] Critical pitfall of spectral power analysis?

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Sep 22 11:48:33 PDT 2025 

Happy to explain, Mate, as I've been excited since writing that simulation.
I want to brag around it.

My understanding is that the differential effects of IPSPs and EPSPs on
spectral shape are derived from the different temporal decay profiles of
excitatory and inhibitory signals, rather than specifically on the
involvement of the dendrites. But I don't want to misrepresent the core
idea here.

If you read EFB, you'll see the opposite: the 'dendritic filter' is the
only origin of the 'low-pass filter effect' according to this classic book.
EFB adopts passive, subthreshold membrane potentials as sources of EEG.
Counting on the active membrane model is a relatively new thing since 2006
ish (the Murakami and Okada paper; Eric Halgren once told me personally
that this was one of the key papers)

I recommend you take a look at this Github page. You can see a gif movie
which tells you what's going on in 5 sec.
https://urldefense.com/v3/__https://github.com/MakotoMiyakoshi/cableTheoryDemo/tree/main__;!!Mih3wA!FTObKOGIIbMm2X1u8M9AwEWdjVmXGl5L5d_cCH6Rw3ZykuNA9qHeZnWMwpD11HgtRrC6dkYa3Qgp6ZGJadNtYXJ2MH0$ 
There are a few important facts.

   1. EEG, either scalp or dura, measures extracelluler electric field
   generated by return currents. Therefore, when Gao et al. (2016) talks about
   the difference between AMPA and GABA_A spike width, you should still ask
   'Ok, but is it an intracelluler current or extracelluler current? If the
   former is the case, how does it coule with the extracellular electric
   field? I need to know the extracelluler current because that's what we
   measure. Is it via dendritic backprojection of membrane potentials, which
   is capacitively coupled with the extracelluler potential field as Hodgkin
   and Huxley showed in 1952,...' I could be wrong here because I'm not a
   specialist in this field, but
   2. Return currents receives low-pass filter effects as its source moves
   further away from the location of synaptic input.
   3. Thus, high-frequency input == no time for the return current source
   from the location of the synaptic input == less low pass filter effect, and
   vice versa.
   4. Volume conduction has no frequency filter effect.

This single cable theory analysis gives us this qualitative insight.
If you read Reimann et al. (2013), you can see a massive simulation of this
model. However, they did not focus on the low-pass filter effect.

If the above cable theory if correct, the low-pass filter effect (i.e., the
1/f effect) depends on

   - length of dendrite
   - location of synaptic input

Thus, it depends on the thalamic laminer input structure (lemniscal = L3/4,
extralemnsical = L1) and local cellular anatomy like the ratio between L3
vs. L5 neurons etc.. We probably need approaches using pharmacological or
opto-genetic manipulation to switch on and off a particular circuits
quickly to see how 1/f changes network-specifically (which also has
thalamo-cortical state dependency, which is still different from Gao's
AMPA/GABA_A contributions which arise from local circuits.

Despite EFB's passive subthreshold model, I believe that active dendrite
model by Murakami and Okada (2006) and Reimann (2013), hence Gao et al.
(2016) and Donoghue et al. (2020) as well, are also true. But I do not know
which factor is more dominant in scalp-recorded EEG measurement.

If it's complicated, we can meet on Zoom to discuss it in person. It would
be much easier.

Makoto

On Mon, Sep 22, 2025 at 1:14 PM Gyurkovics, Mate via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear all,
>
> sorry, I was a disengaged from emails for a couple of days. It sounds like
> in the meantime there are now at least two very interesting strands of
> simulations being run, one on comparing the E:I balance idea and the
> dendrite-length idea of 1/f generation, and another looking at the removal
> of the aperiodic component from the full spectrum. I think both of these
> are exciting and I'm happy to contribute in any way I can.
>
> @Makoto: As for the first, regarding the generative model of 1/f, I can
> easily see both of these mechanisms contributing to surfcae-level aperiodic
> components. I have not had the chance to look at your simulations yet,
> Makoto, but I'll be curious to see how you model these effects, and
> specifically how they might contribute to the signal measured on the
> outside of the skull. I think that is potentially the shakiest part of the
> E:I balance idea (i.e., that the framework works well for intracranial
> data, but not necessarily for scal-derived). To be fair, it was not
> (primarily) formulated to explain M/EEG data anyway.
>
> A propos, M/EEG data, I have some simultaneous MEG/EEG recordings, so we
> could easily compare 1/f dynamics across modalities. I wonder if the two
> ideas you describe make differential predictions for these two modalities
> with different sensitivity profiles for sources of different orientations.
>
> Finally, I'm not sure I follow why you refer to the E:I balance stuff as
> active dendritic filtering? My understanding is that the differential
> effects of IPSPs and EPSPs on spectral shape are derived from the different
> temporal decay profiles of excitatory and inhibitory signals, rather than
> speficially on the involvement of the dendrites. But I don't want to
> misrepresent the core idea here.
>
> @Cedric: As for Cedric's work, that is much closer to what we are
> discussing in Gyurkovics et al., 2021, the paper that started this whole
> discussion. It's still not clear to me how a GLM approach would account for
> both additive and multiplicative effects (we would essentially just be
> looking at the residuals after partialling out aperiodic parameters, which
> is tantamount to subtraction of the predicted signal, so the real question
> would still be whether we are entering logged values or not, right?).
> However, I'll be happy to see your simulations. It's also not clear to me
> what the entered predictors would be if you do not "layer it" on top of
> IRASA or fooof or some other method that gives you aperiodic parameters.
> What else would you be regressing out then, if not these estimates of
> aperiodic contribution?
>
> As a sidenote, if my memory is correct, IRASA itself is also agnostic
> about HOW you separate the aperiodic and periodic parts. It gives you an
> estimate of the former (in linear space, I think), and then you can just do
> whatever you want with that, subtract that from the spectrum, or divide the
> spectrum by that, etc. So it's still up to you as a researcher to make this
> decision (and we argue this is a key decision to make).
>
> As for the derivative, I agree, I do not see this working without massive
> consequences elsewhere. It should attentuate lower frequencies and thus
> flatten the spectrum, but I'm not sure it provides a principled way of
> separating aperiodic and periodic components?
>
> Thanks again for the exciting discussions,
> Mate
> ________________________________
> Feladó: eeglablist <eeglablist-bounces at sccn.ucsd.edu>, meghatalmazó:
> Cedric Cannard via eeglablist <eeglablist at sccn.ucsd.edu>
> Elküldve: 2025. szeptember 19., péntek 22:20
> Címzett: EEGLAB List <eeglablist at sccn.ucsd.edu>
> Tárgy: Re: [Eeglablist] Critical pitfall of spectral power analysis?
>
> Dear Jinwon,
>
> Interesting idea. My understanding is that the temporal derivative
> steepens high-frequency content and reduces the dominance of low
> frequencies, thereby minimizing the 1/f slope (also called prewhitening).
> It does reduce 1/f scaling, but at a cost: oscillatory peaks can be
> distorted (e.g., attenuated in alpha relative to higher bands), and the
> signal’s statistical properties are altered in ways that complicate
> interpretation. It is thus a blunt tool compared to model-based separation
> (like FOOOF) or regression-based covariates (GLM). It can be useful in
> specific contexts such as highlighting sharp transients or ripples, as in
> Cox et al. (2017), but it is not a general solution for
> oscillation/aperiodic separation in EEG.
>
> I’ve been reading about IRASA (Wen & Liu, 2016):
> https://urldefense.com/v3/__https://pmc.ncbi.nlm.nih.gov/articles/PMC4706469/__;!!Mih3wA!GV4DGiGOw26Mc54x3I3afDKbEcfCSXJf-y1fFCQe3Wh5yyBMpSeMIhyZqV6k96k6h_WunX3lvkHwcnnzBaHelJDr6Q$
>
> This method separates fractal (aperiodic) from oscillatory components
> through irregular resampling. Its strength is that it does not assume a
> parametric form, making the decomposition data-driven and robust across
> datasets. The limitations, however, are (if I understand correctly):
> - if oscillatory peaks are very strong, broad, or overlapping, separating
> them cleanly is difficult—resampling shifts peaks, but disentangling
> overlaps can remain messy.
> - IRASA also presumes that the fractal component is scale-free; if the
> aperiodic background departs from a pure power law (e.g., knees, plateaus),
> the isolation can be imperfect.
> - High-frequency noise (such as EMG or amplifier noise) can further
> interfere, particularly when spectral flattening overlaps the analysis band.
> - because IRASA requires relatively long data segments and repeated
> resamplings, it sacrifices temporal specificity compared to methods
> designed for short windows or transient events.
>
> Alternatively, FOOOF offers a complementary parametric strategy,
> explicitly fitting the aperiodic component (offset and slope) and modeling
> oscillatory peaks on top. This provides clean, interpretable parameters,
> which is useful for group comparisons or linking spectral features to
> physiology. But the approach depends heavily on model quality: noisy
> spectra, overlapping peaks, or deviations from a pure power-law background
> can bias fits, and overfitting or misfitting are ongoing concerns.
>
> The GLM approach I proposed would  complement, not replace, these methods.
> By treating aperiodic parameters (offset, slope, or IRASA-derived
> estimates) as covariates, it adjusts oscillatory estimates statistically
> instead of subtracting background activity. This allows additive and
> multiplicative contributions to be tested explicitly, and reduces the risk
> of conflating broadband shifts with narrowband effects. In practice, GLM
> can be applied directly to raw or dB spectra, or layered on top of IRASA or
> FOOOF outputs to provide a more rigorous inference framework.
>
> As a next step, I plan to extend the simulations to compare these
> approaches side by side (derivative, IRASA, FOOOF, and GLM) against known
> ground-truth oscillatory changes, and to evaluate how GLM behaves when
> combined with each.
>
> Cedric
>
>
>
> On Thursday, September 18th, 2025 at 7:17 PM, 장진원 via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Dear all,
> >
> > Thank you for sharing all the interesting ideas in this topic. I just
> > wonder whether utilizing the derivative of the EEG time series to
> minimize
> > 1/f scaling (Cox et al., 2017) is more or less effective than other
> methods
> > like FOOOF or GLM proposed by
> >
> > Cedric
> >
> >
> > .
> >
> > Best Regards,
> > Jinwon Chang
> >
> > 2025년 9월 18일 (목) 오후 11:29, Makoto Miyakoshi via eeglablist <
> > eeglablist at sccn.ucsd.edu>님이 작성:
> >
> > > Hello Mate and all,
> > >
> > > I became curious about the 1/f^n issue, so I spent a whole day today
> > > investigating this problem.
> > > Following Cedric's obsession, I uploaded simulation code and results to
> > > Github, YouTube, and SCCN Wiki page.
> > >
> > >
> https://urldefense.com/v3/__https://github.com/MakotoMiyakoshi/cableTheoryDemo/tree/main__;!!Mih3wA!CORpSZkbn8mntguT3SylRvIRIqrj-8phD2Bm9zBE0rNFZZa8DMJcfa0mXv2pG2xDJfbgRregAtjdLfj53TBo4g-7dEc$
> > >
> > >
> https://urldefense.com/v3/__https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline*Where_does_power_distribution_come_from.3F_.28For_510.2C000_page_views.2C_Added_on_09.2F17.2F2025.29__;Iw!!DZ3fjg!7laPJVE-aD5lzcXtJK-VV5uu70cV0ZVdaJNWbqM88Mg2JboVI71dPCsxVYi76q1L_edm6Elk8xph9xCUtNLkbhvIYOc$
> > >
> > >
> https://urldefense.com/v3/__https://www.youtube.com/watch?v=SitmGp8LYtY__;!!Mih3wA!CORpSZkbn8mntguT3SylRvIRIqrj-8phD2Bm9zBE0rNFZZa8DMJcfa0mXv2pG2xDJfbgRregAtjdLfj53TBosE7VLyc$
> > >
> > >
> https://urldefense.com/v3/__https://www.youtube.com/watch?v=iaRQsaU1_2s__;!!Mih3wA!CORpSZkbn8mntguT3SylRvIRIqrj-8phD2Bm9zBE0rNFZZa8DMJcfa0mXv2pG2xDJfbgRregAtjdLfj53TBoDhtKxC8$
> > >
> > > I think the mechanism you are referring to, Makoto, is what's usually
> just
> > > described as "dendritic filtering", is it not? I'm aware of this
> process
> > > plus the low-pass filtering that happens as a consequence of spatial
> > > summation.
> > >
> > > Yes, a dendritic filtering. But to be honest I was not aware of the
> fact
> > > that there were actually two scenarios, one is passive (like EFB) and
> the
> > > other is active (like Gao et al.)
> > > I once asked Paul whether the subthreshold passive cable theory was
> > > sufficient to explain the generative model of EEG signals. He said the
> > > subthreshold model was 'good enough'. As I read Reimann et al. (2013)
> this
> > > time, I found that the difference between passive vs. active cable is
> > > present but probably not very critical after all. As we can easily
> imagine,
> > > the active model with action potentials adds more power at higher
> > > frequencies (in FOOOF terminology, 'flatter aperiodic') If I were an
> > > American, I would say duh--action potentials are spikes. But Reimann
> and
> > > colleagues also investigated the impact on laminer CSD patterns, which
> was
> > > good to confirm.
> > >
> > > As my simulation confirms, we can make testable hypotheses on
> modulation of
> > > 1/f-ness.
> > > For example, selective engagement of pyramidal neurons whose soma are
> at L5
> > > should show more low-pass filter effect simply because their dendrites
> are
> > > longer than those that reside at L3.
> > > Also, any preferential engagement of neural populations that are known
> to
> > > use non-myelinated axons should produce more low-pass filter effects
> etc
> > > etc..
> > >
> > > I want to see a quantitative comparison between the dendritic length
> model
> > > within the framework of the passive cable theory and AMPA/GABA_A model
> > > within the framework of Gao's action potential-based model. Is the
> latter
> > > so prominently larger than the former so that we can safely forget it?
> It's
> > > an empirical question.
> > >
> > > Makoto
> > >
> > > On Sun, Aug 31, 2025 at 6:30 PM Gyurkovics, Mate mategy at illinois.edu
> > > wrote:
> > >
> > > > Thanks again for all the interesting points. I'm certianly learning
> a lot
> > > > on the physics side - and also about the PSD in different animals, I
> was
> > > > genuinely unaware of all this, but sounds super interesting.
> > > >
> > > > I think the mechanism you are referring to, Makoto, is what's usually
> > > > just
> > > > described as "dendritic filtering", is it not? I'm aware of this
> process
> > > > plus the low-pass filtering that happens as a consequence of spatial
> > > > summation. These are the two main ones I was thinking of, so you are
> > > > right,
> > > > it was very imprecise on my part to talk about the low-pass filtering
> > > > properties of the tissue.
> > > >
> > > > As demonstrated in a classical study by Lopes da Silva and van
> > > > Leeuwen (1977), alpha oscillation is generated within the cortex
> which is
> > > > only 4-5 mm thick.
> > > >
> > > > Just out of curiosity, what point was this sentence supporting?
> > > >
> > > > I'm very happy to learn the conceptual distinction between trivial
> and
> > > > non-trivial contributions to the changes of 1/f power distribution.
> Thank
> > > > you Mate. Your works are impressive.
> > > >
> > > > Very nice of you to say this, Makoto - I would, however, also like to
> > > > stress that this particular distinction just reflects how I
> personally
> > > > think about this problem (i.e., the contribution of the ERP to the
> > > > spectrum), I'm sure reasonable people could disagree. (Although this
> is
> > > > basically the logic we published in the Journal of Neuroscience paper
> > > > linked above.)
> > > >
> > > > @Eugen - you raise many interesting points. (I certainly agree that
> in
> > > > scalp recordings at leat, oscillatory activity is sparse, and most
> > > > robustly
> > > > occurs when the brain is not engaged with a task, in the form of
> alpha
> > > > activity, but that is about as bold as I can be here.)
> > > >
> > > > is sinusoidal (with sharp peaks in the spectrum) activity and
> broadband
> > > > (not necessarily 1/f) activity generated by different mechanisms?
> > > >
> > > > I think, to put it very simply, this is one of the fundamental
> questions
> > > > underlying this discussion here. If they are separate AND fairly
> > > > independent, that is when the conclusions of our paper hold. If they
> are
> > > > separate but interact, our conclusions will sometimes hold, other
> times
> > > > maybe not. If they reflect the same underlying mechanism, our
> conclusions
> > > > would likely rarely be a concern. I personally think that they likely
> > > > reflect mechanisms that are separate at least to some extent - plus
> > > > 1/f-like broadban activity likely reflects several, not just one,
> > > > generative mechanisms as highlighted by Makoto and others here too.
> > > >
> > > > Moreover, the function 1/f may simply be the result of the Fourier
> > > > transform of a single excitatory or inhibitory postsynaptic
> potential.
> > > >
> > > > As far as I recall, this point is covered in Gao et al. (2017), the
> paper
> > > > that first linked 1/f-like features to excitation/inhibition balance.
> > > >
> > > > Thanks,
> > > > Mate
> > > >
> > > > ------------------------------
> > > > Feladó: Евгений Машеров emasherov at yandex.ru
> > > > Elküldve: 2025. augusztus 30., szombat 9:07
> > > > Címzett: Gyurkovics, Mate mategy at illinois.edu
> > > > Másolatot kap: EEGLAB List eeglablist at sccn.ucsd.edu; Wirsing,
> > > > Karlton
> > > > kwirsing at vt.edu; Cedric Cannard ccannard at protonmail.com; 장진원 <
> > > > jinwon06292 at gmail.com>; Makoto Miyakoshi mmiyakoshi at ucsd.edu
> > > > Tárgy: Re: [Eeglablist] Critical pitfall of spectral power analysis?
> > > >
> > > > As a (naive and insufficiently substantiated hypothesis) — is
> sinusoidal
> > > > (with sharp peaks in the spectrum) activity and broadband (not
> > > > necessarily
> > > > 1/f) activity generated by different mechanisms? Moreover, sinusoidal
> > > > activity is a manifestation not of action, but of inaction of the
> brain.
> > > > Physiological (alpha rhythm when closing the eyes, possibly also mu
> > > > rhythm
> > > > in the absence of proprioceptive signals, sleep spindles) or
> pathological
> > > > (alpha coma and low-frequency sinusoids in the delta or theta
> ranges). It
> > > > can be associated with the regulation of the level of constant
> potential
> > > > and, in general, with metabolic processes carried out by the integral
> > > > regulator generating oscillations (but we see oscillations directly
> only
> > > > as
> > > > an idle rhythm). Broadband activity seems to be directly associated
> with
> > > > the functioning of individual neurons. Moreover, the function 1/f may
> > > > simply be the result of the Fourier transform of a single excitatory
> or
> > > > inhibitory postsynaptic potential. These mechanisms are
> interconnected,
> > > > but
> > > > different. Perhaps the mathematical apparatus for their study should
> also
> > > > be different.
> > > >
> > > > Your truly
> > > >
> > > > Eugen Masherov,
> > > > Burdenko Neurosurgery Institute
> > > >
> > > > > Thanks again everyone, for these very interesting points.
> > > > >
> > > > > Just to add to something that was said recently - yes, 1/f (or
> rather,
> > > > > 1/f^x) features are quite ubiquitous, I think practically any time
> series
> > > > > with some amount of autocorrelation will have a similar shape:
> > >
> > >
> https://urldefense.com/v3/__https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(14)00085-0__;!!Mih3wA!E0DB3LnNc6JHuXWc3N5M5iAUeusaX8LTN3dqbu9ZUeyzmDOwf3GVazliJU65YVxC6mC4VLMvHBOlkHKz4AeGoXXj$
> > >
> > > > - its ubiquity is covered nicely in this lovely paper, as far as I
> > > > remember.
> > > >
> > > > > I also get most of Makoto's points about how just the location of
> the
> > > > > neuronal inputs, either in terms of proximity to the soma or in
> terms of
> > > > > cortical layers, will affect the strength of the low-pass
> filtering, and
> > > > > thus the shape of the 1/f scaling. This is super interesting, and
> this
> > > > > and
> > > > > dendritic filtering are certainly discussed in the literature to
> some
> > > > > extent. I am a bit more sceptical whether such subtle differences
> could
> > > > > contribute to 1/f changes in scalp recordings, but Makoto suggests
> they
> > > > > could and I trust his expertise.
> > > > >
> > > > > If you perform an ERP task, it would change 1/f power
> distribution, not
> > > > > surprisingly, because task-triggered cortico-cortical and
> > > > > thalamo-cortical
> > > > > inputs are recruited.
> > > > >
> > > > > This is a very interesting point. In our 2021 paper linked above,
> we
> > > > > also make the point that 1/f shape should change in an
> event-related
> > > > > design, but for a more trivial reason: ERPs are non-oscillatory
> (in the
> > > > > simple sense that they are transient bursts that do not repeat
> with a
> > > > > clear
> > > > > period), and will thus have a 1/f shape in the frequency domain
> (indeed,
> > > > > they do, there are some figures in the paper). Thus, 1/f scaling
> will
> > > > > change after an event trivially because there are well-known
> > > > > non-band-limited changes happening in the EEG (the ERPs). We tried
> to
> > > > > correct for the contribution of the ERPs and still found
> post-stimulus
> > > > > 1/f
> > > > > changes that we consider non-trivial (a steepening to be specific).
> > > > > These,
> > > > > then, could be explained by the mechanism that Makoto suggests
> (which we
> > > > > did not consider in the paper, as it seemed maybe a bit
> small-scale to
> > > > > explain scalp-derived effects) and/or by Gao et al.'s
> > > > > excitation-inhibition
> > > > > balance idea (this is the framework we used in the paper). It
> certainly
> > > > > cannot be explained by the general low-pass filtering properties
> of the
> > > > > tissue or similar more or less fixed variables, as those should not
> > > > > change
> > > > > so rapidly.
> > > > >
> > > > > I share much of your scepticism about oscillatory mechanisms (in
> scalp
> > > > > recordings), Makoto, but if we take the most typical generative
> > > > > mechanisms
> > > > > assigned to these phenomena (interplay of pyramidal cells and
> > > > > interneurons), they seem like they could potentially interact with
> these
> > > > > other mechanisms described above, or be fairly independent.
> > > > >
> > > > > So we've got this really complex picture, where there could be
> > > > > oscillations going on (maybe in alpha only), there could be
> > > > > (independent?)
> > > > > 1/f dynamics happening for multiple reasons, e.g., because of the
> > > > > location
> > > > > and/or the nature (E vs. I) of neuronal inputs changing, and there
> could
> > > > > be
> > > > > ERPs happening too, which might partly be phase-locked
> oscillations, and
> > > > > could also be related to where the neuronal inputs are located, so
> they
> > > > > "straddle" these different mechanisms quite a bit, probably. Not
> too sure
> > > > > about the ERPs to be honest.
> > > > >
> > > > > Two more minor points:
> > > > >
> > > > > I can't put up with the fuzziness of how the term 'oscillation' is
> used
> > > > > in the field now. Is a try-phasic burst, such as a classical
> > > > > event-related
> > > > > N1-P1 waveform, an oscillation?
> > > > >
> > > > > I agree completely that it is very unclear what constitutes an
> > > > > oscillation - basically, how many cycles are enough for something
> to be
> > > > > considered an oscillation, and how do we show that those cycles
> come from
> > > > > the same generative mechanism, and not just multiple successive
> events
> > > > > happening. This is less of a question for longer, more stable
> > > > > oscillations,
> > > > > e.g., alpha at rest.
> > > > >
> > > > > And as for Michael's question: my limited experience with this
> topic
> > > > > would certainly suggest that 1/f dynamics (for whatever reason)
> could
> > > > > change very rapidly, and often in a systematic fashion (e.g.,
> predictably
> > > > > after a stimulus). They also do seem to change on much slower time
> scales
> > > > > as well, e.g., across the lifespan.
> > > > >
> > > > > Thanks,
> > > > > Mate
> > >
> > > _______________________________________________
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> > >
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