  EEG time series are analyzed using the diffusion entropy method. The
resulting EEG entropy manifests short-time scaling, asymptotic saturation and
an attenuated alpha-rhythm modulation. These properties are faithfully modeled
by a phenomenological Langevin equation interpreted within a neural network
context. Detrended fluctuation analysis of the EEG data is compared with
diffusion entropy analysis and is found to suppress certain important
properties of the EEG time series.
