A nonlinear perspective in understanding the neurodynamics of EEG.
Computers in biology and medicine November 1993 DOI: 10.1016/0010-4825(93)90091-e (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Review Peer reviewed |
|---|---|
| Key findings | Argues that nonlinear dynamics and chaos theory provide a framework for understanding EEG that surpasses descriptive stochastic statistics, proposing that chaotic dynamical parameters and trajectory behavior can serve as feature-detection techniques and that globally coupled dynamical systems can model transitional states of brain activity underlying consciousness and behavior. |
Abstract
The developments in nonlinear dynamics and the theory of chaos have considerably altered our perception and analysis of many complex systems, including the brain. This paper reviews the physical and dynamical aspect of brain's electrical activity from this new perspective and indicates possible future directions. The importance of emerging trends of nonlinear dynamics and chaos to neurobiology has been discussed in the context of various states of consciousness and behaviour. In the past, EEG analysis has been confined to descriptive stochastic statistics and any understanding of the transitional process of brain activities was either nonexistent or not amenable for investigation. With the developments in nonlinear dynamics, the chaotic dynamical parameters and trajectory behaviour will find their use as feature detection techniques in EEG. Furthermore, nonlinear dynamics provides a model for EEG generation and temporal prediction which will help in determining the nature of neuronal processes governing various states of brain activity. The formalism of globally coupled dynamic systems will find applications in modelling the transitional states of EEG.