Jansen B H
Department of Electrical and Computer Engineering, University of Houston, TX 77204-4793, USA.
Electroencephalogr Clin Neurophysiol Suppl. 1996;45:39-56.
Quantitative, computerized electroencephalogram (EEG) analysis appears to be based on a phenomenological approach to EEG interpretation, and is primarily rooted in linear systems theory. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of nonlinear dynamics and chaos theory, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing nonlinearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. A review of the fundamentals of chaos theory is provided. Evidence supporting the nonlinear dynamics paradigm to EEG interpretation is presented, and the kind of new information that can be extracted from the EEG is discussed. A case is made that a nonlinear dynamic systems viewpoint to EEG generation will profoundly affect the way EEG interpretation is currently done.
定量计算机化脑电图(EEG)分析似乎基于一种现象学方法来解释脑电图,并且主要植根于线性系统理论。然而,一种根本不同的计算机化脑电图分析方法正在进入实验室。其基本思想受到非线性动力学和混沌理论领域近期进展的启发,即将脑电图视为一个相对简单复杂度但包含非线性的确定性系统的输出。这表明,研究脑电图的几何动力学以及开发神经生理学上逼真的脑电图生成模型,可能会产生比传统随机方法更成功的自动脑电图分析技术。本文提供了混沌理论基础的综述。展示了支持将非线性动力学范式用于脑电图解释的证据,并讨论了可从脑电图中提取的新信息类型。有人认为,从非线性动态系统角度看待脑电图生成将深刻影响当前脑电图解释的方式。