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Scientific Reports volume 11, Article number: 2041 (2021) DOI 10.1038/s41598-021-81230-7 (Open Access)

Róbert Bódizs1,2, Orsolya Szalárdy1,3, Csenge Horváth1, Péter P. Ujma1,2, Ferenc Gombos4,5, Péter Simor1,6,7, Adrián Pótári8,9, Marcel Zeising10,11, Axel Steiger10 &Martin Dresler12

1 Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
2 Epilepsy Center, National Institute of Clinical Neurosciences, Budapest, Hungary
3 Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
4 Department of General Psychology, Pázmány Péter Catholic University, Budapest, Hungary
5 MTA‐PPKE Adolescent Development Research Group, Budapest, Hungary
6 Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary
7 UR2NF, Neuropsychology and Functional Neuroimaging Research Unit At CRCN – Center for Research in Cognition and Neurosciences and UNI – ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium
8 MTA‐PPKE Adolescent Development Research Group, Budapest, Hungary
9 Doctoral School of Psychology (Cognitive Science), Budapest University of Technology and Economics, Budapest, Hungary
10 Max Planck Institute of Psychiatry, Research Group Sleep Endocrinology, Munich, Germany
11 Centre of Mental Health, Klinikum Ingolstadt, Ingolstadt, Germany
12 Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands


Features of sleep were shown to reflect aging, typical sex differences and cognitive abilities of humans. However, these measures are characterized by redundancy and arbitrariness. Our present approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is based on the power law scaling of the Fourier spectra with the additional consideration of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles. Measures derived are the spectral intercept and slope, as well as the maximal spectral peak amplitude and frequency in the sleep spindle range, effectively reducing 191 spectral measures to 4, which were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

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