基于小波包分解的不同状态下脑电信号分析

本文针对脑电信号的非平稳性,引入小波包分解理论处理临床脑电.根据脑电信号的不同节律特性,提出应用小波包分解构造不同频率特性的时变滤波器,提取脑电信号不同节律的动态特性,并由此构造各种节律的动态脑电地形图.为了研究不同脑功能状态下脑电信号各种节律的动态特性,文中对两组不同的临床脑电数据进行分析,比较两种状态下各种节律的动态特性.实验结果表明,利用小波包分解对脑电信号进行滤波,能够有效提取临床脑电不同节律的动态特性,为分析脑电信号提供一条新的途径.

Analysis of Different Functional States EEG Signal Based on Wavelet Packet Decomposition

In this paper, the theory of wavelet packet decomposition is employed to investigate the clinical EEG signals according to its nonstationarity. On the basis of the property of different EEG rhythms, wavelet packet decomposition is used for designing filters with different frequency characteristics to detect the dynamic characteristics of different EEG rhythms, which are used to form the dynamic electrical brain activity mapping (DBEAM). In order to examine the dynamic characteristics of all sorts of rhythms under different functional states of brain, two kinds of clinical EEG data with different brain function states are analyzed and compared. The experimental results indicate that the dynamic characteristics of clinical brain electrical activities can be demonstrated by using wavelet packet decomposition. The method proposes a new way for the analysis of EEG signals.