Spectral density

Quantum mechanics

Sleep Stages

Power law

Neuroscience

Statistics

Entropy (arrow of time)

Polysomnography

Computer science

Acoustics

Scaling

Statistical physics

Physics

Nonlinear system

Psychology

Mathematics

Artificial intelligence

Geometry

Rhythm

Electroencephalography

Slow-wave sleep

Human Brain Mapping

We explored changes in multiscale brain signal complexity and power-law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate-based tests of nonlinearity we also examined whether any of the sleep stage-dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum. Our results indicate that changes of brain signal entropy throughout the sleep cycle are strongly time-scale dependent. Slow wave sleep was characterized by reduced entropy at short time scales and increased entropy at long time scales. Temporal signal complexity (at short time scales) and the slope of EEG power spectra appear, to a large extent, to capture a common phenomenon of neuronal noise, putatively reflecting cortical balance between excitation and inhibition. Nonlinear dynamical properties of brain signals accounted for a smaller portion of entropy changes, especially in stage 2 sleep.

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle