ArticleNeuroinformatics2022
Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations.
Article in Neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 1 of them a synthesis that pooled it.
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Who cites it
130 citing papers in PubMed, 1 synthesis or guideline pooled it, 236 citations in OpenAlex.
- Aperiodic and Hurst EEG exponents across early human brain development: A systematic review.Developmental cognitive neuroscience · 2024Pooled it
- Sleep deprivation and sleep intensity exert distinct effects on cerebral vasomotion and brain pulsations driven by the respiratory and cardiac cycles.PLoS biology · 2025Trial
- Altered Evening Aperiodic Activity and Microstate Dynamics in Insomnia Disorder: An OPM MEG Study.CNS neuroscience & therapeutics · 2026Article
- The mMolecular psychiatry · 2026Article
- Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power.Communications biology · 2026Article
- Signatures of Covert Neuron Loss in the Local Field Potential of Motor Cortex.Journal of the American Heart Association · 2026Article
- Association between aperiodic activity and impulsivity in individuals with methamphetamine use disorder.Psychopharmacology · 2026Article
- Characterization of aperiodic and theta activity in preterm infants using EEG: Insights into cerebral maturation and inter-individual variability.Developmental cognitive neuroscience · 2026Article
- Aperiodic Brain Activity Modulates Corticospinal Excitability.Neuroscience bulletin · 2026Article
- Aperiodic and oscillatory activity of the human brain during induced emotional states.medRxiv : the preprint server for health sciences · 2026Article
- A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.PLOS digital health · 2026Article
- Abnormal periodic and aperiodic resting-state electroencephalographic markers in Lewy body and Alzheimer's diseases with cognitive decline.GeroScience · 2026Article
- Pallidal alpha activity is an electrophysiological biomarker of symptom severity in obsessive-compulsive disorder.Research square · 2026Article
- Cortical and basal ganglia beta oscillations and frequency-dependent DBS effects in the A53T Parkinson's disease rat model.NPJ Parkinson's disease · 2026Article
- Normative modeling of MEG brain oscillations across the human lifespan.Communications biology · 2026Article
- Scale-Free Neurodynamics as Functional Fingerprint of Brain Regions.Bioengineering (Basel, Switzerland) · 2026Article
- How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison.Psychophysiology · 2026Article
- Aperiodicity in Mouse CA1 and DG Power Spectra.eNeuro · 2026Article
- Reduced cortical excitability is associated with head impact severity and cognitive symptoms in adolescent football players.iScience · 2026Article
- In vivo acoustoelectric neural recording in mice enabled by ultrasound-induced frequency mixing.Communications engineering · 2026Article
70 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.