[Eeglablist] Critical pitfall of spectral power analysis?

Thu Aug 28 10:17:28 PDT 2025

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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