[Eeglablist] Critical pitfall of spectral power analysis?

Fri Aug 29 10:50:39 PDT 2025

Hi Karlton and Mate,

Thank you Karlton for sharing the paper. I agree that a 1/f distribution
can result from multiple mechanisms and commonly found in various natural
phenomena.

Mammalian neocortex can be expected to low pass filter its mesosources (and
by implication macroscopic EEG signals) below the high-frequency range f >
1/T, consistent with EEG recordings in a wide variety of mammalian species.
That is, nearly all EEG power observed in mammal cortex lies below about
100 Hz. By contrast, invertebrate EEG spectra typically exhibit substantial
relative power above 100 Hz (Bullock 1977).

In Bullock and Basar (1988), you can see PSDs of electrophysiological
recordings from an aplysia up to 300 Hz, which appears very flat compared
with those of a rabbit and a cat. This may be explained by the fact that
the 'sum-of-distributed-time-constants model' is more suitable for
human/mammalian cortex than neural ganglia (or whatever) in aplysia.

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 it only yields a trivial effect size, we do not need to bother. You can
see the numerical simulation plot in EFB p. 192 (Figure 4-18). The Section
4-15 is concluded with the following sentence.

*We emphasize that this predicted low-pass filtering is due to
high-frequency reductions in mesoscopic dipole moment, not bulk tissue
properties.*

To be clear, the definition of he 'mesoscopic dipole moment' is an
equivalent current dipole generated along with a dendrite of a pyramidal
cell. 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. So, at least the authors of EFB conclude that the cable
equation is the mechanism of the low-pass effect of scalp-recorded EEG. In
addition, scalp EEG recording has a broad spatial averaging, which favors
low-frequency signal sources than high-frequency signal sources. This
serves another factor of the low-pass filter effect.

I see what you mean by trivial and non-trivial causes of 1/f-ness in the
evoked potentials.
I want to repeat that there is no such a thing as 'general low-pass
filtering properties of the tissue'. In mesoscopic and macroscopic
electrophysiology, the rules of physics are simply resistive, not
capacitive.

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.

Makoto

On Fri, Aug 29, 2025 at 4:31 AM Gyurkovics, Mate <mategy at illinois.edu>
wrote:

> 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!HIAOZOQRk1voMqPWjS4Z3OGI9uYhmSMZN7WFcvXz_SDbM8uYJIdFKWCdu9UHLxrs-SMYR0O-uZmeAEncqY4S6FzVp2Q$ 
> <https://urldefense.com/v3/__https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(14)00085-0__;!!Mih3wA!DNhA6f3b41i6W1CrEVCQzzFEo8_-PS1F5kGb6JK4idm3qMd2s3Eaeo8FP_hdObCNqsw52qAEHzL9aip-CpV9$> -
> 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
>
>
> ------------------------------
> *Feladó:* Wirsing, Karlton <kwirsing at vt.edu>
> *Elküldve:* 2025. augusztus 29., péntek 3:20
> *Címzett:* Cedric Cannard <ccannard at protonmail.com>; 장진원 <
> jinwon06292 at gmail.com>; Gyurkovics, Mate <mategy at illinois.edu>
> *Másolatot kap:* EEGLAB List <eeglablist at sccn.ucsd.edu>
> *Tárgy:* Re: [Eeglablist] Critical pitfall of spectral power analysis?
>
> 1/f noise occurs a lot in nature. I was using this reference in my
> Master's thesis. M. S. Keshner, "1/f noise," Proceedings of the IEEE, vol.
> 70, pp. 212-218, 1982.
>
> •The voltages or currents of: vacuum tubes, diodes, and transistors
> •The resistance of: carbon microphones, semiconductors, metallic
> thin-films, and aqueousionic solutions
> •The frequency of quartz crystal oscillators
> •Average seasonal temperature
> •Annual amount of rainfall
> •Rate of traffic flow
> •The voltage across nerve membranes and synthetic membranes
> •The rate of insulin uptake by diabetics
> •Economic data
> •The loudness and pitch of music
>
> I've also seen it in pulsar data, so EEG data is yet another aspect of it.
>
> ------------------------------
> *From:* eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of
> Gyurkovics, Mate via eeglablist <eeglablist at sccn.ucsd.edu>
> *Sent:* Wednesday, August 13, 2025 4:34 AM
> *To:* Cedric Cannard <ccannard at protonmail.com>; 장진원 <jinwon06292 at gmail.com
> >
> *Cc:* EEGLAB List <eeglablist at sccn.ucsd.edu>
> *Subject:* Re: [Eeglablist] Critical pitfall of spectral power analysis?
>
> 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://nam04.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fsccn.ucsd.edu*2Fwiki*2FMakoto*27s_preprocessing_pipeline*Umbra_Corticalis_.28For_330.2C000_page_views.2C_Added_on_01.2F01.2F2025.3B_corrected_on_06.2F25.2F2025.29__*3BJSM!!DZ3fjg!8XeyW453IOLKzDv0rqUyEegED2RNW6JwnujP93W2KN_0LAnN3FNeNHTCgDdTMP6fyrTyqyp0SdPGo5LHLL4GrC_82Wc*24&data=05*7C02*7Ckwirsing*40vt.edu*7C4ce6fe589e47480a8af308ddda758498*7C6095688410ad40fa863d4f32c1e3a37a*7C0*7C0*7C638906920554451943*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=ZSv143NO8wwoPMeoW5Uy43uMtQQBtuGHhrKaym5mNNE*3D&reserved=0__;JSUlJSUlJSUlJSoqJSUlJSUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!HIAOZOQRk1voMqPWjS4Z3OGI9uYhmSMZN7WFcvXz_SDbM8uYJIdFKWCdu9UHLxrs-SMYR0O-uZmeAEncqY4SkA3sfVk$ 
> <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://nam04.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fgithub.com*2Famisepa*2Feeg_glm_aperiodic_covariate__*3B!!Mih3wA!Ce_9hnOFHUAB7C-ZZf4C4Hxmygb-iSFGuxv4cE7TEEml8V0EcFqc-4JQ9slmdryJSv0Ti88G0V1kM_Rb-3jPEJF9pA*24&data=05*7C02*7Ckwirsing*40vt.edu*7C4ce6fe589e47480a8af308ddda758498*7C6095688410ad40fa863d4f32c1e3a37a*7C0*7C0*7C638906920554488413*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=FsJ*2BzZheQ9i9raIX120swYl3AzIRgnKdkQsGBBlNPwY*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!Mih3wA!HIAOZOQRk1voMqPWjS4Z3OGI9uYhmSMZN7WFcvXz_SDbM8uYJIdFKWCdu9UHLxrs-SMYR0O-uZmeAEncqY4SramN-Og$ 
> <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://nam04.safelinks.protection.outlook.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fonlinelibrary.wiley.com*2Fdoi*2Fabs*2F10.1002*2Fsim.3132__*3B!!Mih3wA!DDPZ_YphIcjaVH3DU8jtzW_d9dhPFRD0nt95TF-cXHZrIJX7BX9VE_PO2zN4gVthY5GGOkVw0MwTB1uR7e3aMMJ-bQs*24&data=05*7C02*7Ckwirsing*40vt.edu*7C4ce6fe589e47480a8af308ddda758498*7C6095688410ad40fa863d4f32c1e3a37a*7C0*7C0*7C638906920554505997*7CUnknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=J*2B2uQHVj9XzGJuWNbnT*2Fg*2FBEaDgUL9rmi4AW4qE3N5w*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!HIAOZOQRk1voMqPWjS4Z3OGI9uYhmSMZN7WFcvXz_SDbM8uYJIdFKWCdu9UHLxrs-SMYR0O-uZmeAEncqY4SMFNi810$ 
> <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.
> > > >
> > > >
> >
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> > > >
> > > > Best regards,
> > > > Jinwon Chang
> > > > _______________________________________________
> > > > To unsubscribe, send an empty email to
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