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.

通常通过使用脑电图和/或ECG来测量和估计精神压力的状态。由于这些信号的特征和精神状态随时间变化，因此我们必须将它们作为非平稳数据进行处理。因此，我们必须开发有效的非平稳数据分析方法。时频分析的理论被称为分析生物医学信号等非平稳信号的强大工具。在这个项目中，我们试图找到有效的参数，我们可以通过这些参数来提取随机过程的基本特征，并分析和解释非机构信号。在随机过程的理论中，已经在5月分支机构中经常提出并经常使用许多有效的带宽。几乎没有努力使他们团结起来。我们提出了一类无限的有效带宽，其中包括所有必需的传统带宽。该类由Renyi的α-阶熵定义，并给出带宽的统一表达。我们称它们为alpha-rorder带宽，w-alpha。 W-Alpha的概念很容易扩展到非平稳分析。然后，我们提出了基于时间频率分布的W-alpha的时间，并表明通过仿真分析有效的W-Alpha的时间有效。我们将它们应用于来自Uchida-kraepelin的任务的人，这些分析是由uchida-kraepelin的任务施加精神压力的。我们表明该方法对于跟踪EEG的α带宽很有用。仪式条件是影响人类精神状态的基本因素。在该项目的后期，我们开始开发分析气象数据的方法，即来自某些气象卫星，温度数据，降雨数据等的图像数据。我们尝试应用信息理论概念，例如熵和分歧对我们的方法。我们一直在展示这些信息理论概念对我们的目的非常有力。