论文标题
通过单组分非平稳信号对脑电图的参数建模
Parametric Modeling of EEG by Mono-Component Non-Stationary Signal
论文作者
论文摘要
在本文中，我们提出了一种新型的脑电图（EEG）信号参数建模的方法。已经证明，脑电图是一个单组分的非平稳信号，其幅度和相位（频率）可以表示为时间函数。我们提出了详细的策略，以高精度估算所提出模型的参数。仿真研究说明了模型拟合的过程。描述了模型特征的某些解释。
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.
