The new method and reported findings address a growing interest in neuroscience for research tools that can reliably decompose brain activity at the mesoscopic scale into interpretable components.

Polysomnographical analysis in humans shows that temporal fluctuations of aperiodic neural activity reflect the cycling nature of sleep.

Changes in neural activity thought to reflect brain aging may be partly influenced by age-dependent signals ‘leaking’ from the heart.

The slope of the power spectrum of subthalamic local field potentials tracks pathological states in Parkinson’s disease and likely complements beta activity as a feedback marker for adaptive deep brain stimulation.

Applying decomposition of oscillatory and aperiodic signal components to developmental EEG data provides a solution for the problems in the investigation of alpha power during brain maturation.

Waschke and colleagues demonstrate that aperiodic EEG activity not only captures subtle attention-related changes in brain signals but also tracks 1/f-like sensory input.

Spontaneous fluctuations in pupil-indexed arousal shape human cortical dynamics in a frequency-dependent and spatially diverse manner.

Neural representations of stimulus-specific information increase in fidelity as the power of alpha/beta activity decreases, suggesting that alpha/beta power decreases reflect a domain-general mechanism that supports information representation.

Neural oscillations prior to a stimulus modulate the strength of early and late responses in opposite directions via distinct mechanisms.