ArticleFrontiers in neuroinformatics2022
Scale-free and oscillatory spectral measures of sleep stages in humans.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Baseline Dynamics and Sleep Loss-Driven Changes of Electrocorticographic Aperiodic Properties Are Altered in Mice Lacking Neuroligin-2.The European journal of neuroscience · 2026Article
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- Altered rhythmic and arrhythmic electroencephalographic activity during non-rapid eye movement sleep in amnestic mild cognitive impairment.Brain communications · 2026Article
- Temporally resolved analyses of aperiodic features track neural dynamics during sleep.Communications psychology · 2025Article
- Hemispherotomy leads to persistent sleep-like slow waves in the isolated cortex of awake humans.PLoS biology · 2025Article
- Wake EEG oscillation dynamics reflect both sleep pressure and brain maturation across childhood and adolescence.bioRxiv : the preprint server for biology · 2025Article
- Aperiodic neural activity distinguishes between phasic and tonic REM sleep.Journal of sleep research · 2025Article
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- Mind Wandering during Implicit Learning Is Associated with Increased Periodic EEG Activity and Improved Extraction of Hidden Probabilistic Patterns.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
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- Medication-invariant resting aperiodic and periodic neural activity in Parkinson's disease.Psychophysiology · 2024Article
- Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data.Frontiers in computational neuroscience · 2024Article
- The spectral slope as a marker of excitation/inhibition ratio and cognitive functioning in multiple sclerosis.Human brain mapping · 2023Article
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8 authors.
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Abstract
Power spectra of sleep electroencephalograms (EEG) comprise two main components: a decaying power-law corresponding to the aperiodic neural background activity, and spectral peaks present due to neural oscillations. "Traditional" band-based spectral methods ignore this fundamental structure of the EEG spectra and thus are susceptible to misrepresenting the underlying phenomena. A fitting method that attempts to separate and parameterize the aperiodic and periodic spectral components called "fitting oscillations and one over f" (FOOOF) was applied to a set of annotated whole-night sleep EEG recordings of 251 subjects from a wide age range (4-69 years). Most of the extracted parameters exhibited sleep stage sensitivity; significant main effects and interactions of sleep stage, age, sex, and brain region were found. The spectral slope (describing the steepness of the aperiodic component) showed especially large and consistent variability between sleep stages (and low variability between subjects), making it a candidate indicator of sleep states. The limitations and arisen problems of the FOOOF method are also discussed, possible solutions for some of them are suggested.
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