[Eeglablist] Critical pitfall of spectral power analysis?

Thu Aug 28 11:21:39 PDT 2025

Hi Michael,

Are 1/f slope changes a stable trait marker of a person’s biology, or more
of a dynamic state marker that shifts across sessions?

The answer is that the 1/f slope is not as stable as human psychological
traits. Rather, it may change as quickly as your P300 for the following
reason.

The cable equation predicts that the 1/f slope changes depending on where
and when a neuron receives inputs from other neurons: when an input is
received in a further synaptic location relative to the soma, the signal
receives stronger low-pass filter effect.

Another factor is AMPA/GABA_A receptor activity balance. This factor may
have a much longer time constant.

You have seen that eyes open and closed change the 1/f slopes.

I'm still skeptical if I should treat 1/f-ness as an independent EEG trait.
Under certain circumstances it seems ok, but when I see a strong left-hand
side or right-hand side PSD curve changes, it automatically affects the 1/f
slope estimate, and the question is are we looking at the same phenomenon
or two different things.

Makoto

On Thu, Aug 28, 2025 at 1:17 PM Dr. Michael Villanueva <
mvillanueva at alphathetacenter.com> wrote:

> Hello
>
> Are 1/f slope changes a stable trait marker of a person’s biology, or more
> of a dynamic state marker that shifts across sessions? Are the changes
> discussed here stable enough to be picked up meaningfully in short
> recordings (like a few minutes of eyes-closed or task EEG), or are repeated
> sessions necessary to interpret with confidence? I am not a scientist; I
> ask as a clinician.
>
> Thank you
>
> Michael Villanueva
>
>
>
> *From: *eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of
> Gyurkovics, Mate via eeglablist <eeglablist at sccn.ucsd.edu>
> *Date: *Thursday, August 28, 2025 at 8:14 AM
> *To: *EEGLAB List <eeglablist at sccn.ucsd.edu>, Makoto Miyakoshi <
> mmiyakoshi at ucsd.edu>
> *Subject: *Re: [Eeglablist] Critical pitfall of spectral power analysis?
>
> Hi there,
>
> hah, thanks, I was just being realistic - that paper was definitely a team
> effort!
>
> It is interesting to read the discussion on the generative mechanism of the
> EEG alpha and 1/f power distribution.
> The origin of the 1/f-ness of EEG signals is in the cable equation itself.
> If you have Electric Fields of the Brain, see pages 171-192 including
> Figure 4-9 and 4-18. Thus, it is an inherent property of the model. Gao's
> AMPA/GABA_A receptor spike model seems to have a dominant impact on this
> 1/f-ness, which is however an independent mechanism and (assumingly?)
> additively modulates the signals.
>
> I fully agree. I do not have that book at hand, but I suspect you mean
> that just due to the low-pass filtering properties of the brain tissue and
> of the spatial integration inherent in EEG, you will see a 1/f-like shape
> in the PSD, no matter what. That makes sense. I think why there is now an
> increase in interest in 1/f activity in surface-level recordings too is
> that you can observe dynamic, systematic changes in the steepness/offset of
> this 1/f shape within an individual (here's one of our other papers on that
> as an example:
> https://urldefense.com/v3/__https://www.jneurosci.org/content/42/37/7144.abstract__;!!Mih3wA!De_sfMQXYyO4EEUfvJ5VFr75SzASy8YhzlQGw23c5sO-uUrE2z-9N6A5_8BzXR57z1UNkNBdm8m62JFL-nMcZJg_$
> <https://urldefense.com/v3/__https:/www.jneurosci.org/content/42/37/7144.abstract__;!!Mih3wA!De_sfMQXYyO4EEUfvJ5VFr75SzASy8YhzlQGw23c5sO-uUrE2z-9N6A5_8BzXR57z1UNkNBdm8m62JFL-nMcZJg_$>
> ) that cannot be explained by these generative mechanisms because they
> should be fairly stable. So there's (potentially) got to be another
> mechanism on top of these that can account for the conditional changes in
> this shape. Gao et al.'s model is a nice starting point for discussions on
> this other mechanism.
>
> And then on top of these mechanisms you will have the mechanisms
> generating the oscillations (if there are any). This is our fundamental
> point here - there is going to be something (or some things) that generate
> the functionally relevant 1/f dynamics, and there is going to be something
> that generates the oscillations, and if these mechanisms are independent,
> you should try to separate them in a way that assumes independence.
>
> I'm not sure I fully follow your advice to Jinwon, but I think if you are
> interested in event-related spectral changes, and you want to compare two
> conditions, the easiest thing is to not do baseline correction on the
> time-freq representations in either condition, and simply look at the
> conditional difference (condition 1 minus condition 2). That will highlight
> both narrowband and potential broadband differences between the two
> conditions. If there are broadband differences then it might make sense to
> ask whether the narrowband differences are truly narrowband or simply part
> of the broadband change.
>
> As a final note, as far as I know a new version of fooof (called
> specparam) will be released soon that will have an option to do separation
> using division or subtraction.
>
> Thanks,
> Mate
>
> ________________________________
> Feladó: eeglablist <eeglablist-bounces at sccn.ucsd.edu>, meghatalmazó:
> Makoto Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu>
> Elküldve: 2025. augusztus 26., kedd 22:11
> Címzett: EEGLAB List <eeglablist at sccn.ucsd.edu>
> Tárgy: Re: [Eeglablist] Critical pitfall of spectral power analysis?
>
> Hello,
>
> Mate, you were so humble when you wrote you were one of the authors of the
> paper--In fact, you were the first author! I appreciate that you
> investigated this issue. This problem is definitely important to watch out
> for.
>
> I want to advise Jinwon that there is no need to freak out: The 'baseline'
> part, which is cancelled out when calculating ERSP, can be captured by
> calculating standard PSD. Problems can happen when you see a huge
> difference (like Cohen's d > 2.0?) in the alpha-band power in PSD between
> conditions AND you run ERSP analysis within the same frequency range. In
> fact, you can design a simulation in which alpha power difference in PSD,
> quantified by Cohen's d, is parametrically varied, against which the
> statistical significance of ERSPs between conditions within the simulated
> frequency range can be plotted to see how the problem gets worse. I would
> be rather optimistic though, as I do not see such large differences in PSDs
> between conditions very often.
>
> It is interesting to read the discussion on the generative mechanism of the
> EEG alpha and 1/f power distribution.
> The origin of the 1/f-ness of EEG signals is in the cable equation itself.
> If you have Electric Fields of the Brain, see pages 171-192 including
> Figure 4-9 and 4-18. Thus, it is an inherent property of the model. Gao's
> AMPA/GABA_A receptor spike model seems to have a dominant impact on this
> 1/f-ness, which is however an independent mechanism and (assumingly?)
> additively modulates the signals.
>
> Using GLM to address the issue of quantifying an alpha peak in PSD while
> taking into consideration 1/f-ness is interesting. Looks like the next step
> from FOOOF. Thank you Cedric for this excellent demonstration.
>
> Makoto
>
> On Wed, Aug 13, 2025 at 10:25 AM Gyurkovics, Mate via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Hi all,
> >
> > these are some very interesting points!
> >
> > Cedric wrote:
> >
> > "Broadband 1/f slopes can emerge from periodic sources via amplitude
> > modulation, membrane RC filtering, and spatial summation constraints
> > (Buzsáki et al., 2012; Nunez & Srinivasan, 2006). Scalp electrodes
> > integrate over large, structured cortical regions, with slope changes
> > depending on the extent and synchrony of oscillatory sources. In the
> > spectral fitting tradition (e.g., FOOOF), the aperiodic component is
> > usually treated as a broadband process partially independent from
> > narrowband rhythms."
> >
> > I agree with both of these assertions (when I mentioned the low-pass
> > filtering characteristics of surface recordings I was referring to the
> > issues around spatial summation and their consequences). It still seems
> to
> > me that although theoretical possible, yes, I know of little evidence to
> > suggest that the 1/f-shape in typical M/EEG recording comes from periodic
> > sources. Sometimes it's unclear if even the peaks that rise above the 1/f
> > scaling are oscillatory in nature at all!
> >
> > But I fully agree with your second point that there is a spectral fitting
> > school of thought where broadband and narrowband activities are
> considered
> > to be separable, and that baseline/ongoing activity contains additive
> > components. As long as we accept the second point here, the general
> > observation in the paper stands.
> >
> > I like your GLM idea, although I will have to think about how it
> > accommodates multiplicative, as well as additive contributions too. In
> > general, it does reaffirm our larger point - accounting for (or
> controlling
> > for) 1/f parameters is important when one is interested in narrowband
> > activity, and this partialling out needs to be done carefully. I will
> note
> > that oscillatory estimates from fooof in its current form are also the
> > outcome of a divisive process, so they could suffer from the same problem
> > (not the aperiodic estimates though), but this is a known issue that a)
> we
> > will soon publish a paper about, and b) the new release of
> fooof/specparam
> > will take care of by allowing users to set whether separation should
> happen
> > in linear or log space.
> >
> > As for Jinwon's question:
> >
> > "In sleep EEG, it's common to extract slow oscillation and sleep
> > spindles by simply calculating relative PSD across channels. Is this
> > calculation also affected by baseline correction? "
> >
> > Sorry, I am not a sleep person so I am not sure what exactly happens
> here.
> > To extract slow oscillations, do you take the PSD over a long period of
> > time (to allow for multiple cycles of a slow oscillation to resolve),
> then
> > compare this against resting-state, non-sleep PSD based on data of the
> same
> > duration? So it's basically sleep-minus-rest? Although the word
> "relative"
> > suggests it may be sleep/rest? I think if the goal is to simply say that
> > for a given participant, there is an increase in low-freq activity
> compared
> > to rest, then it does not matter if it's sleep-minus-rest or sleep/rest.
> If
> > you then want to compare the magnitude of low-freq activity (let's say
> > these are slow oscillations) across participants, it will matter whether
> > it's sleep-minus-rest or sleep/rest, because people might differ a lot in
> > their "rest" activity already.
> >
> > If you simply quantify power at low freqs in the PSD, and track how that
> > changes during sleep, you run the risk of conflating aperiodic and
> periodic
> > changes across time (i.e., something that you call a slow oscillation
> based
> > on the PSD could non-oscillatory in origin). You run this risk by
> comparing
> > it to rest too, by the way, so it might be a good idea to check the time
> > courses themselves whether it looks like there is any rhythmicity there.
> >
> > Thanks,
> > Mate
> >
> >
> > ________________________________
> > Feladó: eeglablist <eeglablist-bounces at sccn.ucsd.edu>, meghatalmazó: 장진원
> > via eeglablist <eeglablist at sccn.ucsd.edu>
> > Elküldve: 2025. augusztus 13., szerda 0:55
> > Címzett: Cedric Cannard <ccannard at protonmail.com>
> > Másolatot kap: EEGLAB List <eeglablist at sccn.ucsd.edu>
> > Tárgy: Re: [Eeglablist] Critical pitfall of spectral power analysis?
> >
> > Hi all,
> >
> > It's very interesting for me to see many different solutions for this
> > issue. As a clinical scientist who studies PSG of patients, It would be
> > great if I could get an answer on one specific topic of spectral power
> > analysis. In sleep EEG, it's common to extract slow oscillation and sleep
> > spindles by simply calculating relative PSD across channels. Is this
> > calculation also affected by baseline correction? All this calculation is
> > based on only resting-state, which means it directly handles
> > baseline activity, so I wonder whether this approach also requires
> > additional setup using GLM or specific additive models. For me, it does
> not
> > seem like that baseline correction is a problem.
> >
> > Best regards,
> > Jinwon Chang
> >
> > 2025년 8월 12일 (화) 오후 7:11, Cedric Cannard via eeglablist <
> > eeglablist at sccn.ucsd.edu>님이 작성:
> >
> > > Hi Makoto,
> > >
> > > I’m really glad this topic came up. I think refining these concepts is
> > > critical for the long-term progress of neuroscience, and I hope I can
> > > contribute something useful. Here’s my current understanding of the
> issue
> > > and I propose a potential solution.
> > >
> > > As Makoto described to me a while ago and here:
> > >
> >
> https://urldefense.com/v3/__https://sccn.ucsd.edu/wiki/Makoto*27s_preprocessing_pipeline*Umbra_Corticalis_.28For_330.2C000_page_views.2C_Added_on_01.2F01.2F2025.3B_corrected_on_06.2F25.2F2025.29__;JSM!!DZ3fjg!8XeyW453IOLKzDv0rqUyEegED2RNW6JwnujP93W2KN_0LAnN3FNeNHTCgDdTMP6fyrTyqyp0SdPGo5LHLL4GrC_82Wc$
> <https://urldefense.com/v3/__https:/sccn.ucsd.edu/wiki/Makoto*27s_preprocessing_pipeline*Umbra_Corticalis_.28For_330.2C000_page_views.2C_Added_on_01.2F01.2F2025.3B_corrected_on_06.2F25.2F2025.29__;JSM!!DZ3fjg!8XeyW453IOLKzDv0rqUyEegED2RNW6JwnujP93W2KN_0LAnN3FNeNHTCgDdTMP6fyrTyqyp0SdPGo5LHLL4GrC_82Wc$>
> > > Broadband 1/f slopes can emerge from periodic sources via amplitude
> > > modulation, membrane RC filtering, and spatial summation constraints
> > > (Buzsáki et al., 2012; Nunez & Srinivasan, 2006). Scalp electrodes
> > > integrate over large, structured cortical regions, with slope changes
> > > depending on the extent and synchrony of oscillatory sources. In the
> > > spectral fitting tradition (e.g., FOOOF), the aperiodic component is
> > > usually treated as a broadband process partially independent from
> > > narrowband rhythms.
> > >
> > > As Gyurkovics et al. (2021) and Makoto's example point out, if the
> > > baseline contains additive broadband components (as is likely), the
> same
> > > absolute oscillatory change can appear larger with a low baseline and
> > > smaller with a high baseline.
> > >
> > > One way forward I suggest is to model baseline explicitly rather than
> > > remove it by ratio. For oscillatory targets, work in linear power units
> > > instead of dB, estimate baseline aperiodic parameters (e.g., offset,
> > > exponent via FOOOF or IRASA), and include them (along with baseline
> band
> > > power) as covariates in a GLM or mixed model. This accommodates both
> > > additive and multiplicative contributions, yielding a condition effect
> > that
> > > reflects rhythmic changes net of broadband shifts (Donoghue et al.,
> 2020;
> > > Wen & Liu, 2016; Alday, 2019).
> > >
> > > If one still wanted to focus on broadband state effects rather than
> > > oscillations, the same GLM framework could be applied directly to the
> > > aperiodic parameters themselves (e.g., baseline offset, exponent). This
> > > would allow testing whether these parameters change systematically
> across
> > > conditions, with physiological interpretations such as global power
> > shifts
> > > (offset) or potential changes in excitation–inhibition balance
> (exponent;
> > > Gao et al., 2017).
> > >
> > > I’ve set up a small MATLAB simulation repo illustrating how this bias
> > > arises and how a GLM-based adjustment can remove it:
> > >
> >
> https://urldefense.com/v3/__https://github.com/amisepa/eeg_glm_aperiodic_covariate__;!!Mih3wA!Ce_9hnOFHUAB7C-ZZf4C4Hxmygb-iSFGuxv4cE7TEEml8V0EcFqc-4JQ9slmdryJSv0Ti88G0V1kM_Rb-3jPEJF9pA$
> <https://urldefense.com/v3/__https:/github.com/amisepa/eeg_glm_aperiodic_covariate__;!!Mih3wA!Ce_9hnOFHUAB7C-ZZf4C4Hxmygb-iSFGuxv4cE7TEEml8V0EcFqc-4JQ9slmdryJSv0Ti88G0V1kM_Rb-3jPEJF9pA$>
> > >  could be a useful starting point for collaborative exploration by the
> > > community.
> > >
> > >
> > > Cedric Cannard
> > >
> > >
> > >
> > > On Monday, August 11th, 2025 at 11:39 AM, Makoto Miyakoshi via
> > eeglablist <
> > > eeglablist at sccn.ucsd.edu> wrote:
> > >
> > > > Hi Jinwon and Daniele,
> > > >
> > > > I've checked that paper recently but haven't read it. Let me guess
> what
> > > the
> > > > main problem is, and let me use a simple example below to share
> > > > understanding of it.
> > > >
> > > > Subject 1: Baseline-period alpha power magnitude 10 microV^2/Hz, and
> a
> > > task
> > > > increased the power by 10 microV^2/Hz. Thus, the power change is 10
> > > > microV^2/Hz -> 20 microV^2/Hz, which is 3dB.
> > > >
> > > > Subject 2: Baseline-period alpha power magnitude 100 microV^2/Hz,
> and a
> > > > task increased the power by 10 microV^2/Hz. Thus, the change is 100
> > > > microV^2/Hz -> 110 microV^2/Hz, which is 0.41dB.
> > > >
> > > >
> > > > Thus, even though both subjects showed the same 10 microV^2/Hz power
> > > > increase evoked by the task, dB-conversion showed one is +3dB while
> the
> > > > other is +0.41dB.
> > > > I guess this is the main point of the problem? I still do not see how
> > the
> > > > source independence issue can relate here, but at least this is a
> part
> > of
> > > > the problem and is legitimate, right?
> > > >
> > > > This kind of '1/f slope + peak' conceptualization, together with
> > concepts
> > > > such as 'oscillatory', 'non-oscillatory', reminds me of FOOOF
> (Donoghue
> > > et
> > > > al., 2020; Gao et al., 2017).
> > > >
> > > > Here is my take:
> > > > If someone makes an assertion that that dB-converted calculation is
> the
> > > > ONLY VALID way of quantifying it, s/he is wrong.
> > > > Otherwise it is ok to use the dB-converted calculation. It just has
> > > > insensitivity in certain aspects. The calculation itself is valid.
> > > >
> > > > A practical merit of using dB-conversion is that cross-frequency
> > > > normalization is automatically taken care of.
> > > > For example, if you observe 10 microV^2/Hz power increase in theta
> and
> > > > gamma bands, which is more prominent? The latter, right? It's because
> > the
> > > > variance of the 'baseline signals' follows 1/f.
> > > > So, if you want, we can publish another paper saying that using
> > > microV^2/Hz
> > > > cannot show the significance of the same 10 microV^2/Hz power
> increases
> > > in
> > > > theta and gamma.
> > > >
> > > > These are just thought experiments. My point is that there are
> usually
> > > > trade-offs in these approaches, and it is rare to find that one
> > approach
> > > > turned out to be completely wrong. It's usually a matter of
> > distribution
> > > of
> > > > sensitivities. Also, it should not be too difficult to use multiple
> > > > calculations and show the results in parallel. However, I do not know
> > > what
> > > > EEGLAB developers will do for this issue (will they ever recognize it
> > as
> > > an
> > > > issue?)
> > > >
> > > > I've also seen a criticism that our inter-trial phase coherence is
> > > biased.
> > > >
> > >
> >
> https://urldefense.com/v3/__https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.3132__;!!Mih3wA!DDPZ_YphIcjaVH3DU8jtzW_d9dhPFRD0nt95TF-cXHZrIJX7BX9VE_PO2zN4gVthY5GGOkVw0MwTB1uR7e3aMMJ-bQs$
> <https://urldefense.com/v3/__https:/onlinelibrary.wiley.com/doi/abs/10.1002/sim.3132__;!!Mih3wA!DDPZ_YphIcjaVH3DU8jtzW_d9dhPFRD0nt95TF-cXHZrIJX7BX9VE_PO2zN4gVthY5GGOkVw0MwTB1uR7e3aMMJ-bQs$>
> > > >
> > > > %%%%%%%%%%%%%%%%%%%%%%%%
> > > > While writing this response, I saw Daniele's post. I'm curious to
> hear
> > > what
> > > > the 'substantial flaw' is in more detail. Looks like my quick and
> lazy
> > > > problem explained above is different from what he means.
> > > >
> > > > By the way, I was happy to find that he wrote '1/f can be generated
> by
> > > > "pure oscillations" with nonuniform amplitude, among other things.'
> > > because
> > > > I once said exactly the same thing to express my dissatisfaction to
> > hear
> > > > how the concept of 'aperiodic' had been misused in some communities!
> > > >
> > > > Makoto
> > > >
> > > > On Sat, Aug 9, 2025 at 1:25 PM 장진원 via eeglablist
> > > eeglablist at sccn.ucsd.edu
> > > >
> > > > wrote:
> > > >
> > > > > Hi all,
> > > > >
> > > > > Recently I found one interesting article that addresses the pitfall
> > of
> > > > > baseline correction that many scientists have used to transform EEG
> > to
> > > > > time-frequency domain. According to this article, power spectrum
> > > formation
> > > > > is highly exposed to subject-dependent noise that independently
> > affects
> > > > > power spectrum regardless of signal. Because I am not an engineer
> who
> > > > > majors signal transformation, I wonder how eeglab could handle this
> > > issue
> > > > > in spectral power analysis because this article implies that using
> > > alpha
> > > > > (8-13Hz) or theta (4-8)Hz is totally unacceptable in clinical
> > studies.
> > > > >
> > > > > Reference: Gyurkovics, M., Clements, G. M., Low, K. A., Fabiani,
> M.,
> > &
> > > > > Gratton, G. (2021). The impact of 1/f activity and baseline
> > correction
> > > on
> > > > > the results and interpretation of time-frequency analyses of
> EEG/MEG
> > > data:
> > > > > A cautionary tale. NeuroImage, 237, 118192.
> > > > >
> > > > >
> > >
> >
> https://urldefense.com/v3/__https://doi.org/10.1016/j.neuroimage.2021.118192__;!!Mih3wA!FUy2N9N5bZQJF1IM06-OIaXtDG8YvPWzfrSGxmJE6N_4DPqW9Irqgr9P4PajtadaJV9Jzo1Z9QWJsE2RPNZmbe-Mkw$
> <https://urldefense.com/v3/__https:/doi.org/10.1016/j.neuroimage.2021.118192__;!!Mih3wA!FUy2N9N5bZQJF1IM06-OIaXtDG8YvPWzfrSGxmJE6N_4DPqW9Irqgr9P4PajtadaJV9Jzo1Z9QWJsE2RPNZmbe-Mkw$>
> > > > >
> > > > > Best regards,
> > > > > Jinwon Chang
> > > > > _______________________________________________
> > > > > To unsubscribe, send an empty email to
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> > > > >
> >
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> >   .
> > > >
> > > > _______________________________________________
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> > >
> >
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> >  .
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