基于脑电信号的在线疲劳监测算法研究

申请上海交通大学硕士学位论文

基于脑电信号的在线疲劳监测算法研究

论文作者孙珲

学号1100339028

指导教师吕宝粮教授

专业计算机软件与理论

答辩日期2013 年 1 月7 日

Submitted in total fulfilment of the requirements for the degree of Masterin Computer Software and TheoryA Study on EEG based On-line FatigueMonitoring

Algorithms

H S

Supervisor

Prof. B -L L

D C S , S E

E E

S J T U S , P.R.C

Jan. 7th, 2013

大学硕士学位论文ABSTRACT

A Study on EEG based On-line Fatigue Monitoring

Algorithms

ABSTRACT

Because traffic accidents caused by fatigue driving occur frequently in recen-

t years, fatigue monitoring has become an important research topic. In the past re- searches often use the facial video signal, blood pressure, body temperature or other

physiologicalsignals. Comparedtothesesignals,theelectroencephalogram(EEG)can reflect the brain’s activities more directly and objectively, has a higher temporal reso- lution, and can not be artificially controlled and faked, therefore, we use EEG signal

to study fatigue monitoring in this article. In the first half of the article, we mainly introduce the common EEG processing processes, and in the second half we introduce the methods used in our research. Firstly, subjects are asked to complete task which will induce subject’s fatigue, and at the same time we record subject’s EEG signal and performance. Then we use fast Fourier transform (FFT) to obtain the power spectral density (PSD) features of the original EEG signal in the respective frequency bands.

In order to remove the fatigue-unrelated noise, we use linear dynamic system (LDS) to smooth features. Then we use principal component analysis (PCA) to reduce fea- tures’ dimension and discard those features which have bad correlations with fatigue labels. Finally, we extend the remaining features to dynamic feature groups, use par- allel hidden Markov model (PHMM) and fuzzy integral to train and fuse classifiers. Experimental results indicate that the accuracy of classification obtained by using our new method are 88.85 % for classifying 3 states and 83.09 % for classifying 4 states, respectively.

KEY WORDS: EEG, Fatigue Monitoring, LDS, PHMM, Fuzzy In-

tegral

—iii —线疲劳监测算法研究上海交通大学硕士学位论文

2.5 基于脑电信号疲劳监测的主要步骤和相关技术. . . . . . . . . . . 13

2.5.1 降噪去伪迹. . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5.2 特征提取. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.5.3 特征过滤. . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5.4 特征降维与选择. . . . . . . . . . . . . . . . . . . . . . . . 17

2.5.5 疲劳监测算法. . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5.6 疲劳标记. . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.6 本章小结. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

第三章基于脑电信号的疲劳监测算法研究19

3.1 实验设计及数据采集. . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1.1 实验设备. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1.2 实验流程. . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1.3 实验数据. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2 数据处理流程. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3 预处理及脑区选择. . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3.1 去噪预处理. . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.2 脑区选择. . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4 特征提取. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4.1 基于傅里叶变换的特征提取方法. . . . . . . . . . . . . . 26

3.4.2 对数功率谱密度与微分熵. . . . . . . . . . . . . . . . . . 27

3.4.3 倍数化特征. . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.5 特征平滑过滤. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.5.1 滑动平均方法. . . . . . . . . . . . . . . . . . . . . . . . . 29

3.5.2 线性动力系统. . . . . . . . . . . . . . . . . . . . . . . . . 29