基于小波变换的特征提取脑诱发电位
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大 连 民 族 学 院 本 科 毕 业 设 计（论 文）
快速提取诱发脑电算法的研究
学 院（系）：信息与通信工程学院
专 业： 通信工程专业
学 生 姓 名： 陆万安
学 号： 2009081412
指 导 教 师： 李婷
评 阅 教 师： 姜明新
完 成 日 期： 2013年6月7日
大连民族学院
快速提取诱发脑电算法的研究
I 摘 要
从头皮采集的脑电信号中通常夹杂着不同种类的伪迹，主要如：眼电、心电、肌电、工频干扰，它们主要来自一些生理源和噪声源的影响。这些干扰很大程度上淹没了微弱的诱发电位(EP)，给临床应用和科研分析带来了极大的不便。如何从原始脑电中获取大脑活动的诱发信息成为脑电分析中有待解决的问题。
本文介绍了两种快速提取诱发脑电的新算法—小波分析和独立分量分析。小波分析理论是一种新兴的信号处理理论，它在时间上和频率上都有很好的局部性，这使得小波分析非常适合于时—频分析，借助时—频局部分析特性。利用小波方法去噪，是小波分析应用于实际的重要方面。ICA的根本原理是通过分析多维观测数据间的高阶统计相关性，找出相互独立的隐含信息成分，完成分量间高阶冗余的去除及独立信源的提取。
本文首先详细介绍小波分析去噪和独立分量分析的原理的算法,同时阐述这两种方法在信号处理中的运用；其次利用小波阈值去噪法和独立分量分析中的FastICA算法对一个纯净的EP信号加入噪声之后进行去噪处理，实例验证理论的实际效果，同时证实了理论的可靠性。
关键词：诱发电位；小波分析；去噪；独立分量分析快速提取诱发脑电算法的研究
II Abstract
There are many kinds of artifacts in the raw brain signals from scalp, such as eyes blinks,
Electrocardiograph, electromyography and other mechanical noises, which could degenerate
the real evoked potentials(EP). How to extract the underlying evoked potentials from noisy
acquired data has became an important and urgent problem to be resolved.
This paper describes two kinds of new rapid extraction algorithm Evoked Potentials –
wavelet analysis and independent component analysis. Wavelet analysis theory is a new
theory of signal process and it has good localization in both frequency and time do-mains. It
makes the wavelet analysis suitable for time-frequency analysis. Using wavelet methods in
de-noising, is an important aspect in the application of wavelet analysis. The fundamental
principles of Independent Component Analysis (ICA) is through analysis of high-ranking
statistical correlation between multidimensional observation data, find mutually independent
implicit message content, complete removal of a high redundancy and independent sources
extraction letter.
This paper first introduced the principle of wavelet analysis and independent component
analysis algorithm in detail, also explained the two methods applied in signal processing.
second, use the wavelet threshold method and the independent component analysis algorithm
FastICA EP on a pure noise signal added after denoising, the example verify the theoretical
practical effect, also confirmed the reliability of the theoretical.
Key Words：EP；wavelet analysis；noise rejection；Independent Component Analysis
快速提取诱发脑电算法的研究
III 目 录
摘 要 ..................................................................................................................................... I
Abstract ..................................................................................................................................... II
1 绪论 ........................................................................................................................................ 1
1.1 课题的背景与研究意义 ............................................................................................. 1
1.2本课题的发展状况 ...................................................................................................... 1
1.3本课题的主要内容 ...................................................................................................... 2
2 基于小波变换去噪研究 ........................................................................................................ 3
2.1 小波变换 ..................................................................................................................... 3
2.1.1 连续小波变换 .................................................................................................. 4
2.1.2 离散小波变换 .................................................................................................. 5
2.2 小波阈值去噪概述 ..................................................................................................... 5
2.3 小波阈值去噪方法 ..................................................................................................... 6
2.4小波阈值去噪仿真 ...................................................................................................... 7
2.5小结 .............................................................................................................................. 8
3 基于独立分量分析的去噪研究 .......................................................................................... 10
3.1 独立分量分析 ........................................................................................................... 10
3.2 ICA定点算法的实现 ................................................................................................ 10
3.2.1 数据的预处理 ................................................................................................ 11
3.2.2 ICA固定点算法 ............................................................................................. 12
