In this paper, we propose a novel approach for parametric modeling of electroencephalographic (EEG) signals. It is demonstrated that the EEG signal is a mono-component non-stationary signal whose amplitude and phase (frequency) can be expressed as functions of time. We present detailed strategy for estimation of the parameters of the proposed model with high accuracy. Simulation study illustrates the procedure of model fitting. Some interpretation of the characteristic features of the model is described.

中文翻译：

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单分量非平稳信号的脑电参数化建模

在本文中，我们提出了一种新的脑电图 (EEG) 信号参数建模方法。证明了 EEG 信号是单分量非平稳信号，其幅度和相位（频率）可以表示为时间的函数。我们提出了高精度估计所提出模型参数的详细策略。仿真研究说明了模型拟合的过程。描述了模型特征的一些解释。