1、广西师范大学 硕士学位论文 脑电信号的特征分析与研究 姓名：周建芳 申请学位级别：硕士 专业：电路与系统 指导教师：罗晓曙 20080401 I 2005 1.5 (1) (2) (3) 16 (4) 1.5 1.5 1.5 II The Study and Analysis of EEG Features Graduate student: Zhou Jian-Fang Advisor: Prof. Luo Xiao-Shu Major: Circuit and System Grade: 2005 Abstract A plentiful of information about phys
2、iology and pathology is contained in Electroencephalogram (EEG) signals. Some evidences of clinical diagnosis and an effective measure of adjuvant therapy of certain brain diseases are provided for doctors by processing the EEG signals. The studies about the extraction and analysis of EEG features h
3、ave made some important progress and achievements at home and abroad. By using modern signal processing methods, such as sample entropy, bispectrum and 1.5-dimensional spectrum, we study the wealthy information hidden in EEG after reading a number of literatures about EEG at home and abroad. Our res
4、ults can provide theoretical reference worthiness for clinical diagnose of the EEG. The main contents in this paper can be summarized as follows: (1) The basic knowledge of EEG signals is summarized, and the development, date collection, classification and other knowledge of EEG are reviewed. Moreov
5、er, some modern methods of EEG, such as time-frequency analysis, Higher-Order spectral analysis, non-linear analysis and artificial neural network analysis are introduced. Especially, some applications of wavelet analysis, bispectrum analysis, complex analysis and neural network analysis to the EEG
6、signals processing are reviewed. (2) For shortcomings of approximate entropy algorithm, sample entropy is introduced, which is a modified algorithm based on approximate entropy, and is used to analyze EEG signals of epileptic patients and normal people. The results indicate that, on the whole, the v
7、alues of sample entropy of epileptic patients are lower than those of normal people; the value of epileptic patient being in the attack period declines obviously than that of epileptic patient being in pre- attack period, and the value returns to previous level after seizure. These results are basic
8、ally consistent with the symptoms of epileptic patients, which can provide reference value for the clinical diagnosis of epilepsy. (3) The kurtosis and skewness are computed to study the characteristics of non-linear and non-Gaussian of EEG signals under the different states. By using direct method
9、of bispectrum estimate to analyze the EEG signals under three different states, there are some differences of bispectrum structures of the EEG signals under the three states. This verifies that the bispectrum analysis is an effective way of nonlinear analyses to extract the wealthy high-order inform
10、ation. And it contributes to the automatic classification of the EEG signals and provides the more useful information for clinical EEG studies. III (4) For deficiency of traditional bispectrum analysis, a new method1.5-dimensional spectrum analysis is adopted to analyze EEG and numerically verify th
11、e algorithm. Our results show that, the analysis of the 1.5-dimensional spectrum can so effectively inhibit the additive Gaussian noise in the signals that can easily extract useful non-Gaussian signal. Moreover, this method can reveals the quadratic phase coupling characteristic of the EEG and can
12、greatly reduce computational capacity and complexity, and also can effectively extract useful information, which cannot be acquired by using conventional spectral analysis. Key words: Electroencephalogram (EEG) signals; feature analysis; sample entropy; bispectrum; 1.5-dimensional spectrum 1 1.1 200
13、420 1.2 “ ” 30 20 1.3 1932 Dietch 2 (1) 1-3 Lyapunov 4-6 7-8 (2) 9-11 (3) 12-13 (4) 14 (5) 15-16 1.4 1020 16 (FP1 FP2 F3 F4 C3 C4 P3 P4 O1 O2 F7 F8 T3 T4 T5 T6) (A1A2) 3 100HZ 1-1 1-1 1.5 (1) (2) matlab (3) ( ) 16 4 (4) 1.5 Matlab 1.5 , 1.5 1.6 1.5 5 2.1 19 1875 Richard Caton Beck 1902 Hans Berger 1929 ON the Electroencephalogram of Man” 17 1934 Adrian Matthews 1935 Gibbs Davis Lennox 3Hz Gibbs 1936 Lommis Harvery Hobart 1939 18 2.2 51000V 17
