The Dynamics of EEG Entropy
M. Ignaccolo, M. Latka, W. Jernajczyk, P. Grigolini, B.J. West
TL;DR
This paper investigates EEG entropy dynamics using the diffusion entropy method, revealing key properties like short-time scaling and alpha-rhythm modulation, modeled through a Langevin equation within neural networks.
Contribution
It introduces a phenomenological Langevin equation model that captures EEG entropy properties, linking empirical observations to neural network dynamics.
Findings
EEG entropy shows short-time scaling and saturation.
Alpha-rhythm modulation affects entropy dynamics.
Langevin equation effectively models observed properties.
Abstract
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.
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Taxonomy
TopicsNeural dynamics and brain function · Neural Networks and Applications · Complex Systems and Time Series Analysis