[Eeglablist] Invitation to collaborate on an open paper about EEG’s 1/f power distribution

Wed Jan 21 17:10:26 PST 2026

Hi everyone,

My quick update: Eugen, Mate, and I have been making progress looking at periodic/aperiodic separation using a simple GLM-based approach in a few toy simulations. In the setups where the analysis space matches the generative model (additive handled in linear space, multiplicative handled in log/dB space), FOOOF-style iterative fitting seems to behave the best overall compared to a handful of other exclusion/robust-style approaches.

Eugen also proposed a really simple parameter-free “median” slope estimator that looks surprisingly stable in these tests, and promising. 

That said, it looks like we are still facing the same main and unavoidable problem: the separation still depends on whether the underlying phenomenon is additive vs multiplicative. If we analyze an additive process in log space (or a multiplicative process in linear space), we can easily introduce a background-dependent bias, even if the mean recovery looks OK.

This work is slowly happening here if anyone wants to take a look / comment / try extending it: https://urldefense.com/v3/__https://github.com/sccn/OneOverF/discussions/12__;!!Mih3wA!HN6zXpAScehZWYTtFIVvhqRPUa5Asb_78uypLJyTPXZfDtsje8iFk70jLELa9JhRiXdtlv36XEc3cpPNuy1SV0_few$ 

So my current takeaway is that the next big step is less about finding the perfect estimator and more about better understanding the generative mechanisms Makoto outlined first, whether we should separate aperiodic and periodic components or not, and if so, when should scalp EEG 1/f-ness be treated as additive vs multiplicative, so we can apply the right separation strategy.

Cedric

On Wednesday, January 21st, 2026 at 10:29 AM, Makoto Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu> wrote:

> Hi Karlton,
> 
> Thank you for checking in. In fact, I am also badly behind the
> ongoing discussion.
> I can give you a summary of updates on my end. I'm sure other people have
> their own progress they can share. However, because I've been focusing on
> my topic too deeply in the past several weeks to pay attention to what's
> going on on the other side. I'll catch up soon.
> 
> 1. We moved the main discussion place to Github.
> https://urldefense.com/v3/__https://github.com/sccn/OneOverF/discussions__;!!Mih3wA!DuA-X4Y3fIvUzcvdYRSc27MK2cSHlRNJXJR0wSimkAvW0GGJV_YNYsiKo4rljUiGwlWs9m01RoXU7iSpPJSJPBb2IiA$
> 2. (From what I learned so far) there seems to be at least 4 independent
> mechanisms that can contribute to 1/f-ness. Below is the list of them in
> order of simplicity and popularity.
> 
> 
> 1. Spatial scales for averaging: low-frequency activities involve
> spatially broad cortex, while high-frequency activities involves local
> cortical areas. When scalp EEG's spatial averaging scale is applied, the
> latter tend to cancel each other within the fixed spatial constant, hence
> results in a low-pass effect. This is described in the 'Electric Fields of
> the Brain' (EFB).
> 2. AMPA/GABA_A receptor-evoked potential (aka E/I balance): This is
> the core dogma of the FOOOF papers (Gao et al., 2017; Donoghue et al.,
> 2020). AMPA-receptor-generated spikes are narrower than those generated by
> GABA_A receptors, hence the former is flatter than the latter in the
> frequency domain. This is not explained in EFB.
> 3. Cable-theory-based low-pass filter effect: See
> https://urldefense.com/v3/__https://github.com/sccn/OneOverF/discussions/8__;!!Mih3wA!DuA-X4Y3fIvUzcvdYRSc27MK2cSHlRNJXJR0wSimkAvW0GGJV_YNYsiKo4rljUiGwlWs9m01RoXU7iSpPJSJkyZ_Hn8$ for Figure 2 of Rall et
> al. (1967). The more distant a synaptic input location is, the more
> low-pass filter effect it applies. This is explained in EFB in detail. In
> fact, other than the spatial averaging, EFB treats it as the only source of
> the low-pass filter effect.
> 4. Electrodiffusive potential: In my opinion, this is for now the most
> esoteric one in which I've been strongly interested personally for the past
> weeks. It only impacts < 1 Hz and possibly up to the delta range. Its
> origin is not a synaptic activity. It does not involve current. Halnes et
> al. (2024) reported that local field potential (LFP) can measure up to 35
> microV under moderate cognitive load.
> 
> Status (as far as I know, and I'm probably more than 1 month behind):
> 
> - I think many of the 174 'registered' participants to this open project
> are interested in 2. I think Mate Gyurkovic will work on it.
> -
> 
> Cedric
> 
> 
> Cannard has been working on a linear mixed-effect (LME)
> regression to estimate the contribution of the 'slope'.
> - Makoto Miyakoshi has analyzed one EEG study that may be able to
> demonstrate the effect of electrodiffusive potential in scalp EEG. I've
> been consulting specialists for the validity of my view.
> 
> I welcome other people to add/correct my summary above.
> 
> My project, targeting electrodiffusive potential, seems to be generalized
> to other projects as long as experimental design is suitable. I will
> announce potential collaborators to gather datasets, verify the hypothesis,
> and write a paper together in this context. My project requires continuous
> stimuli, and hopefully two different groups of subjects, such as healthy
> adults vs patients, or young vs old, etc..
> 
> Makoto
> 
> On Mon, Jan 19, 2026 at 10:49 PM Wirsing, Karlton kwirsing at vt.edu wrote:
> 
> > I was very busy back in November. What is the status of the paper? I could
> > collaborate if it is still going on.
> > Karlton Wirsing
> > 
> > ------------------------------
> > From: eeglablist eeglablist-bounces at sccn.ucsd.edu on behalf of 王广军
> > via eeglablist eeglablist at sccn.ucsd.edu
> > Sent: Wednesday, November 12, 2025 8:59 PM
> > To: eeglablist at sccn.ucsd.edu eeglablist at sccn.ucsd.edu;
> > mmiyakoshi at ucsd.edu mmiyakoshi at ucsd.edu
> > Cc: eeglablist at sccn.ucsd.edu eeglablist at sccn.ucsd.edu
> > Subject: Re: [Eeglablist] Invitation to collaborate on an open paper
> > about EEG’s 1/f power distribution
> > 
> > Hi everyone!
> > I am interested too!
> > 
> > Best regards,
> > Wang Guangjun
> > 
> > --
> > 
> > Wang GJ, MD
> > Tell: +86 10 6408 9384
> > E-mail: cacms_guangjun at 163.com // tjuwgj at gmail.com
> > Institute of Acupuncture & Moxibustion, CACMS
> > 
> > _______________________________________________
> > To unsubscribe, send an empty email to
> > eeglablist-unsubscribe at sccn.ucsd.edu or visit
> > https://urldefense.com/v3/__https://nam04.safelinks.protection.outlook.com/?url=https*3A*2F*2Fsccn.ucsd.edu*2Fmailman*2Flistinfo*2Feeglablist&data=05*7C02*7Ckwirsing*40vt.edu*7C77a684b7630a426252a608de22731170*7C6095688410ad40fa863d4f32c1e3a37a*7C0*7C0*7C638986074775978222*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=puUZVFKyy14imRwKFNJIplGtX786xXQj3LHVsw0InmM*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!Mih3wA!DuA-X4Y3fIvUzcvdYRSc27MK2cSHlRNJXJR0wSimkAvW0GGJV_YNYsiKo4rljUiGwlWs9m01RoXU7iSpPJSJinPBRPU$
> > <https://sccn.ucsd.edu/mailman/listinfo/eeglablist  >.
> 
> _______________________________________________
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu or visit https://sccn.ucsd.edu/mailman/listinfo/eeglablist .