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How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison.

Overview

Authors: Patrycja Kałamała1, Grace M Clements2, Mate Gyurkovics3, Tao Chen4,5, Kathy A Low4, Monica Fabiani4,5, Gabriele Gratton4,5
ORCID iDs: Patrycja Kałamała, Grace M Clements, Mate Gyurkovics, Monica Fabiani, Gabriele Gratton
  1. Centre for Cognitive Science, Jagiellonian University, Kraków, Poland
  2. Air Force Research Laboratory, Wright‐Patterson AFB, Dayton, Ohio, USA
  3. School of Psychology, University of East Anglia, Norwich, UK
  4. Beckman Institute, University of Illinois Urbana‐Champaign, Urbana, Illinois, USA
  5. Psychology Department, University of Illinois Urbana‐Champaign, Urbana, Illinois, USA
Institutions: Jagiellonian University (Poland); United States Air Force Research Laboratory (United States); Wright-Patterson Air Force Base (United States); University of East Anglia (United Kingdom); University of Illinois Urbana-Champaign (United States)
Journal: Psychophysiology, volume 63, issue 3, article e70272
Dates: received 30 June 2025; accepted 20 February 2026; published online 19 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70272 · PMID 41853983 · PMCID PMC13000880 · OpenAlex W7138926350
Open access: hybrid, a free copy (OpenAlex)
Status: code found, not verified yet
Categories: EEG (modality), MEG (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics
Keywords: 1/f, aperiodic activity, censored regression, EEG, fooof, psychometrics, reliability
MeSH: Brain*, Electroencephalography*, Magnetoencephalography*, Signal Processing, Computer-Assisted*, Humans, Reproducibility of Results (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute on Aging (RF1AG062666); NIA NIH HHS (RF1 AG062666, RF1AG062666)
Citations: cited by 5 papers (Europe PMC); 63 references in the paper

Abstract

Interest in broadband aperiodic brain activity (1/f phenomenon) has increased exponentially over recent years, partly fueled by the development of tools to parameterize it (i.e., estimate its offset/intercept and exponent/slope) using the M/EEG power spectrum. Broadband aperiodic activity needs to be separated from narrowband periodic activity before its parameters are computed. A popular method, the fooof toolbox, is based on the data‐driven detection of narrowband‐periodic peaks, whose maximum number is set by the user. While increasing analytic flexibility, variability in the number of detected peaks may increase sensitivity to noise and reduce the reliability of aperiodic parameter estimates and the power of analytic pipelines. Here, we present an investigation of the effects of analytic choices (e.g., number of peaks, spectral estimation method) on metrics indicating the adequacy of spectral parametrization. These include the internal consistency (odd‐even reliability) of aperiodic estimates, the number of outliers generated, and their ability to detect effects. Across two different data sets (resting state and task‐based), we found a decrease in the reliability of intercept and slope estimates as more peaks were allowed to be extracted. To ameliorate this problem, we propose a theory‐driven modification of fooof labeled censored regression, whereby a theory‐driven range of frequencies expected to contain periodic activity is removed from all spectra, and the remaining power values are regressed on the remaining frequencies to obtain parameter estimates. This method shows more reliable and robust estimates compared to fooof, while avoiding overfitting.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

OSF z3t5m

License: none: the authors keep all their rights
State: unreachable at the last attempt, verified on 30 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: the text, “Spectral Parameterization”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: unreachable at the last attempt (HTTP 401)
  • 30 September 2026: unreachable at the last attempt (HTTP 401)
At the source: osf.io/z3t5m/

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Data

No dataset and no data link were found in the paper.

Data Availability Statement

The data that support the findings of this study are openly available in How to Improve the Reliability of Aperiodic Parameter Estimates at https://osf.io/z3t5m/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 keywords, 6 MeSH terms, 2 funders, 62 references.

Cite

This paper

Kałamała, P., Clements, G. M., Gyurkovics, M., Chen, T., Low, K. A., Fabiani, M., & Gratton, G. (2026). How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison. Psychophysiology, 63(3), e70272. https://doi.org/10.1111/psyp.70272

BibTeX

@article{kaamaa2026how,
author = {Kałamała, Patrycja and Clements, Grace M and Gyurkovics, Mate and Chen, Tao and Low, Kathy A and Fabiani, Monica and Gratton, Gabriele},
title = {{How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison}},
journal = {Psychophysiology},
year = {2026},
month = mar,
volume = {63},
number = {3},
pages = {e70272},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70272},
url = {https://doi.org/10.1111/psyp.70272},
pmid = {41853983},
pmcid = {PMC13000880}
}

RIS

TY - JOUR
AU - Kałamała, Patrycja
AU - Clements, Grace M
AU - Gyurkovics, Mate
AU - Chen, Tao
AU - Low, Kathy A
AU - Fabiani, Monica
AU - Gratton, Gabriele
TI - How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/03/01
VL - 63
IS - 3
SP - e70272
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70272
UR - https://doi.org/10.1111/psyp.70272
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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