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  Separating neural oscillations from aperiodic 1/f activity: Challenges and recommendations

Gerster, M., Waterstraat, G., Litvak, V., Lehnertz, K., Schnitzler, A., Florin, E., et al. (2022). Separating neural oscillations from aperiodic 1/f activity: Challenges and recommendations. Neuroinformatics. doi:10.1007/s12021-022-09581-8.

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 Creators:
Gerster, Moritz1, 2, 3, Author              
Waterstraat, Gunnar2, Author
Litvak, Vladimir4, Author
Lehnertz, Klaus5, 6, 7, Author
Schnitzler, Alfons8, Author
Florin, Esther8, Author
Curio, Gabriel3, Author
Nikulin, Vadim V.1, 2, 3, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Neurophysics Group, Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
3Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
4Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom, ou_persistent22              
5Department of Epileptology, University Hospital Bonn, Germany, ou_persistent22              
6Helmholtz Institute for Radiation and Nuclear Physics, University Bonn, Germany, ou_persistent22              
7Interdisciplinary Center for Complex Systems (IZKS), University Bonn, Germany, ou_persistent22              
8Institute for Clinical Neuroscience and Medical Psychology, Heinrich Heine University Düsseldorf, Germany, ou_persistent22              

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Free keywords: 1/f exponent; FOOOF; IRASA; Neural oscillations; Spectra; EEG/MEG
 Abstract: Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

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Language(s): eng - English
 Dates: 2022-02-252022-04-07
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/s12021-022-09581-8
Other: online ahead of print
PMID: 35389160
 Degree: -

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Project name : -
Grant ID : 203147/Z/16/Z
Funding program : -
Funding organization : Wellcome Centre for Human Neuroimaging

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Title: Neuroinformatics
Source Genre: Journal
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Publ. Info: Humana Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 1539-2791
CoNE: https://pure.mpg.de/cone/journals/resource/1539-2791