基于非线性分析方法的脑电信号特征提取及其模式识别应用研究毕业论文+文献综述+分析源码及数据+初稿+检测报告

ABSTRACT

The main content of this graduation project is to apply empirical pattern decomposition (EMD) and its improved algorithm to analyze EEG signals. EMD is an adaptive data processing and mining algorithm for analyzing nonlinear and non-stationary time series. The method decomposes the signal into a number of eigenmode functions IMF, each IMF component contains the local characteristics of the original signal on different scales through the screening process. Secondly, combined with the machine learning algorithm, the signals processed by the EMD algorithm are classified and recognized.

In this paper, the concept of instantaneous frequency is introduced, the basic concept of IMF and the principle of EMD decomposition algorithm are discussed, and the characteristics of EMD algorithm are analyzed. Combining with IMF, the physical meaning of marginal spectrum and Hilbert spectrum is given. The whole theory of EMD time-frequency analysis is discussed in detail. The empirical mode decomposition and its extended algorithms were investigated, familiar with the conventional processing methods of brain waves, and the translation of important foreign literature was completed, and the weekly records were written according to the nodes during the period. The nonlinear decomposition method of EEG signal is studied to complete the design of feature extraction flow of EEG signal, and the design scheme can consider the society comprehensively. Social, health, safety, legal, cultural and environmental factors.

Key words: empirical mode decomposition; time-frequency analysis; HHT spectrum

目 录

第1章 绪论 1

1.1 课题的研究背景和意义 1

1.2 EMD 方法的提出 3

1.3 EMD方法的研究发展概况 4

1.4 论文结构与安排 5

第2章 EMD时频分析的基本理论 7

2.1 EMD方法的基本概念 7

2.2 EMD方法的基本原理 8

2.3 基于EMD的HHT时频分析 12

2.4 EMD算法存在的问题及改进 13

2.5 本章小结 15

第3章 EMD 时频分析方法的应用 16

3.1 EMD 时频分析仿真及性能比较 16

3.2 EMD在脑电信号趋势提取中的应用 19

3.3 EMD 时频分析在奇异脑电信号检测中的应用 21

3.4 本章小结 24

第4章 基于EMD的脑电信号特征提取及其模式识别设计及仿真 25

4.1 传统的脑电信号特征和EMD脑电特征 25

4.2 EMD 的二进脑电特征特性及仿真 26

4.3 基于EMD的脑电特征方法及仿真 28

4.4本章总结 30

结 论 31

参考文献 32

致 谢 34

附 录 35