Theory of Local parameters in Time-Frequency Analysis and its Applications to Tracking States of Mental Stress

时频分析中的局部参数理论及其在跟踪精神压力状态中的应用

基本信息

批准号：
11650437
负责人：
KIKKAWA Sho
金额：
$ 1.98万
依托单位：
Kinki University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2002
项目状态：
已结题

项目摘要

The state of mental stress is often measured and estimated by using EEG and/or ECG. Since feature of these signals and the mental state change with time, we have to deal them as non-stationary data. Therefore we have to develop effective methods for non-stationary data analysis.The theory of time-frequency analysis is known as a powerful tool to analyze non-stationary signals like biomedical signals.In this project, we tried to find effective parameters by which we can extract essential characteristics of a random process and analyze and interpret the non-stationary signals. In the theory of random processes, many kind of effective bandwidths have been proposed and used very often in may branches. How ever there have been few effort to unity them. We proposed a general class of infinite effective bandwidths which includes all the essential traditional bandwidths. The class is defined by Renyi's alpha-order entropy and gives unified expression of the bandwidths. We call them alpha-order bandwidths, W-alpha. The concept of W-alpha is easily expand to non-stationary analysis. Then we proposed time varying W-alpha based of time-frequency distributions and showed that the time varying W-alpha is effective by simulation analysis. We applied them to EEG analysis measured from persons who are put mental stress by been a task of Uchida-Kraepelin. We showed the method is useful to track alpha-bandwidth of EEG.Meteorological conditions are essential factors which affect human mental state. In the late stage of this project, we began to develop methods to analyze meteorological data, that is, image data from some meteorological satellites, temperature data, rainfall data, etc. We tried apply information theoretical concepts, like entropy and divergence to our methods. We have been showing these information theoretical concepts are very powerful to our purpose.

精神压力状态通常通过使用脑电图和/或心电图来测量和估计。由于这些信号的特征和心理状态会随着时间的推移而变化，因此我们必须将它们视为非平稳数据。因此，我们必须开发有效的非平稳数据分析方法。时频分析理论被称为分析生物医学信号等非平稳信号的有力工具。在这个项目中，我们试图找到有效的参数来分析非平稳数据。可以提取随机过程的基本特征并分析和解释非平稳信号。在随机过程理论中，已经提出了多种有效带宽，并在许多分支中经常使用。然而，团结他们的努力却很少。我们提出了无限有效带宽的一般类别，其中包括所有基本的传统带宽。该类由Renyi的α阶熵定义，并给出了带宽的统一表达。我们称它们为 alpha 阶带宽，W-alpha。 W-alpha 的概念很容易扩展到非平稳分析。然后我们提出了基于时频分布的时变W-alpha，并通过仿真分析表明时变W-alpha是有效的。我们将它们应用于对因内田-克雷佩林的任务而受到精神压力的人进行脑电图分析。我们证明该方法可用于跟踪脑电图的α带宽。气象条件是影响人类心理状态的重要因素。在这个项目的后期，我们开始开发分析气象数据的方法，即一些气象卫星的图像数据、温度数据、降雨数据等。我们尝试将熵和散度等信息论概念应用到我们的方法中。我们已经证明这些信息理论概念对于我们的目的非常强大。