[Eeglablist] Critical pitfall of spectral power analysis?

[Makoto Miyakoshi]

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$ 

%%%%%%%%%%%%%%%%%%%%%%%%
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$
>
> Best regards,
> Jinwon Chang
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