title: Spontaneous brain activity as a source of ideal 1/f noise.
abstract: We study the electroencephalogram (EEG) of 30 closed-eye awake subjects with a technique of analysis recently proposed to detect punctual events signaling rapid transitions between different metastable states. After single-EEG-channel event detection, we study global properties of events simultaneously occurring among two or more electrodes termed coincidences. We convert the coincidences into a diffusion process with three distinct rules that can yield the same mu only in the case where the coincidences are driven by a renewal process. We establish that the time interval between two consecutive renewal events driving the coincidences has a waiting-time distribution with inverse power-law index mu approximately 2 corresponding to ideal 1/f noise. We argue that this discovery, shared by all subjects of our study, supports the conviction that 1/f noise is an optimal communication channel for complex networks as in art or language and may therefore be the channel through which the brain influences complex processes and is influenced by them.
authors.affiliation: Istituto di Fisiologia Clinica-CNR) Via Moruzzi 1, 56124 Pisa, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Allegrini
authors.forename: Danilo
authors.initials: D
authors.lastname: Menicucci
authors.forename: Remo
authors.initials: R
authors.lastname: Bedini
authors.forename: Leone
authors.initials: L
authors.lastname: Fronzoni
authors.forename: Angelo
authors.initials: A
authors.lastname: Gemignani
authors.forename: Paolo
authors.initials: P
authors.lastname: Grigolini
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: West
authors.forename: Paolo
authors.initials: P
authors.lastname: Paradisi
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*