基于改进小波变换的EEG分析

利用小波变换来进行EEG自动识别的研究,试图将EEG自动识别由当前的癫痫领域扩大到更多的病症中来.提出了一种改进小波基的方法.该方法是利用连续小波变换来对EEG信号进行分析,选定一个最合适的小波基.在自动识别的过程中,用自适应小波基的方法来进行多分辨分析,然后用6个参数进行特征提取.实验证明此方法有良好的可行性和有效性.     

碳纤维复合材料缺陷的小波包分析

利用超声相控阵检测系统对含有裂纹、夹杂、分层三种缺陷的碳纤维复合材料实验板进行检测。对采集到的缺陷信号,在计算机中利用小波包变换进行分析。提出基于距离可分性测度的小波基评估准则,以确定最优小波基。对三种缺陷信号用sym8小波进行小波包的分解和重构,用"频率-能量"的方法提取各类缺陷信号的能量特征;结果表明,该方法对碳纤维复合材料的缺陷类型的区分具有较好的效果,从而为缺陷自动识别奠定了基础。     

基于小波变换的数字耳蜗滤波器组设计与实现

分析了耳蜗滤波器与小波变换的相似性,以耳蜗频率响应为基础,设计出相应的小波变换基函数,并通过二进尺度变换得到一组小波作为耳蜗滤波器组。研究了利用该小波滤波器组进行多分辨率分析的数字实现方法,给出了相应的分析滤波器。理论和实验结果均表明,利用小波变换来实现耳蜗滤波器组是可行的。     

基于小波变换的载波相位恢复算法的研究

在相干光通信中,利用载波相位恢复算法来减小由于激光器线宽和ASE噪声所造成的相位偏移。对载波相位恢复算法进行了研究,在此基础上提出了利用小波变换中的小波分解和小波重构的方法来减少相位噪声,通过结果比对选取最佳分解次数,并对此方法进行迭代,以提高去噪性能。小波分析中用到的小波系数没有唯一性,仿真中选取了不同的基小波并对结果进行分析。结果表明通过小波变换算法能够有效地对激光器的初始相位偏差进行恢复,为载波相位恢复提供了一种新的解决办法。     

基于独立分量分析和最大熵模型的人脸自动识别方法

提出一种基于独立分量分析和最大熵模型的人脸自动识别方法。首先利用小波变换，提原始图像的低频子图，然后利用独立分量分析（ICA）进行特征提取，最后采用最大熵模型进行分类识别。实验表明该方法能更好地提高人脸的自动识别。     

树型小波变换在纹理分析中的应用

分析了小波变换的特点,将传统的塔型小波变换和树型小波变换进行了比较,给出了一种用树型小波来提取纹理特征进行纹理图象分割的方法。经过实验结果表明,用树型小波变换可以在纹理分析中获得较好的效果。     

基于小波变换的数字图像压缩编码方法研究

本文首先介绍了在利用小波变换进行图像压缩时,小波基选择一般需要考虑的几个方面.并通过实验数据说明了不同的小波基对BMP类图像压缩产生的影响.然后对基于小波变换的嵌入式图像编码方法中最具代表性的EZW和SPIHT编码算法的原理进行了分析、比较,并通过实验对EZW算法进行了性能分析及其改进,并给出了实验结果.     

基于自适应阈值的脑电信号去噪方法

脑电采集后得到的脑电信号(Electroencephalogram,EEG)中含有噪声信号,为了有效去除噪声并保留有用信息,本文在软阈值去噪的基础上,提出一种改进阈值去除EEG噪声的算法。利用小波变换对EEG信号分解,得到多层的高频系数和低频系数;根据分解层次不同,对小波系数进行自适应的阈值处理;将缩放后的小波系数重构,得到去噪后的EEG信号。以信噪比、均方根误差作为去噪效果的定量指标,将改进算法与硬阈值法、软阈值法、Garrote阈值法进行比较,结果表明,改进阈值法优于其他3种阈值法。     

基于ELM运动想象脑电信号的分类

针对运动想象脑电信号的分类识别,提出一种基于小波变换和共空间模式滤波的方法进行特征提取。对EEG进行3层小波分解,提取相关层数小波系数的特征量;同时利用共空间模式对EEG进行空间滤波,提取其转换后信号的方差作为特征量,并将这两类特征量进行组合。该方法结合了时频域和空间域的特征信息,可提高分类识别的效果。最后选取BCI2003中Data setⅢ数据作为样本,分别用极限学习机和基于粒子群算法的支持向量机进行分类识别。实验结果表明极限学习机分类学习时间较快,最优识别率为94.2857%,证明了该方法更适用于脑机接口系统。     

双树复小波包变换语音增强新算法

实小波包变换是语音增强中效果较好的一种算法,利用阈值的方法对小波包系数进行压缩进而重构语音信号.分析了实小波包变换的平移敏感性,以及其对语音进行增强时的缺陷.提出采用双树复小波包变换方法进行语音增强,当低通滤波器和高通滤波器对应的小波基近似为希尔伯特变换对时,该变换能大大减小实小波包变换中的平移敏感性.同时考虑小波包系数之间的相互关系,提出了重叠块复阈值算法.结果表明,算法优于传统实小波包变换及点阈值算法,尤其对含周期噪声的语音信号,双树复小波包变换算法的优势更为明显.     

Automatic classification of sleep stages based on the time-frequency image of EEG signals

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner–Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.     

基于小波系数特征融合的小鼠癫痫脑电分类

采集癫痫小鼠模型在常态与致癫状态下的脑电信号以研究其癫痫脑电的自动分类。对经过噪声和伪迹消除预处理的脑电信号进行小波变换,获得不同频率子带的小波系数,对脑电信号及与癫痫特征波相关的小波系数提取相应的线性特征（标准差）和非线性特征（样本熵）;基于这些特征及其组合使用支持向量机分类器实现分类。实验发现基于小鼠脑电本身的标准差和样本熵的分类正确率分别为59.10%和58.00%;而融合各相关小波系数的标准差或样本熵,分类正确率分别达到86.60%和88.60%;融合全部相关小波系数的线性和非线性特征后分类正确率为99.80%。这些结果说明基于小波系数特征融合的分类算法性能有显著提升,能有效实现小鼠癫痫脑电的自动分类。     

脑电信号在情感识别中的应用

针对如何在情感识别中有效处理脑电信号和提取有用信息的问题,对实验采集的脑电信号进行小波包分解,通过对相关频段信号的重构,提取出脑电信号中能用于情感状态识别的β波节律,对其在不同情感状态下进行谱分析。仿真实验结果表明,将脑电信号中的β波节律用于情感状态识别是可行的。     

基于小波包分解的时变脑电节律提取

研究从时变非平稳脑电信号中提取脑电动态节律的新方法。首先用小波包分解构造不同频率特性的时变滤波器以提取各种时变的脑电节律，研究临床脑电信号瞬时变化。在此基础上测试并分析两种不同功能状态下的脑电信号，并由此构造各种节律的时变脑电地形图。实验结果表明，小波包分解可以有效提取脑电不同节律的动态特性，此方法也适用于分析其他生物医学信号。     

Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform

For the brain-computer interface system (BCI), pre-processing has an important role to ensure system performance. However, the speech recognition system using electroencephalogram (EEG) is weak against temporal effects. Therefore, in general cases, wavelet transform has been used to cope with the temporal effects and non-stationary characteristic of EEG. The discrete version of wavelet transform, called DWT, requires a filter of the system for use in downsampling the signal. In other words, it is important to determine the suitable type of filter. In many cases, it is difficult to find an adequate filter for DWT because of differences in the characteristics of the input signal. In this paper, we proposed a heuristic approach to finding the optimal filter of the system for EEG signals. The harmony search algorithm (HSA) was used for finding of the optimal filter. In the learning process with the EEG system, the optimal wavelet filter could be found, which is automatically designed for subject personality.     

基于深度小波去噪自动编码器的轴承智能故障诊断方法

针对原始振动数据无监督特征学习问题,提出一种深度小波去噪自动编码器与鲁棒极限学习机相结合的滚动轴承的智能故障诊断方法。利用小波函数作为非线性激活函数设计小波去噪自动编码器,从而有效地捕获信号特征;利用多个小波去噪自动编码器构造一个深度小波去噪自动编码器来增强无监督特征学习能力;采用鲁棒极限学习机作为分类器,对不同的轴承故障进行分类识别。对实验所得的轴承振动信号进行对比分析,结果验证了该方法在原始振动数据无监督特征学习的条件下优于传统方法和标准深度学习方法。     

A comparative study of wavelet families for EEG signal classification

Over the past two decades, wavelet theory has been used for the processing of biomedical signals for feature extraction, compression and de-noising applications. However the question as to which wavelet family is the most suitable for analysis of non-stationary bio-signals is still prevalent among researchers. This paper attempts to find the most useful wavelet function among the existing members of the wavelet families for electroencephalogram signal (EEG) analysis. The EEGs considered for this study belong to both normal as well as abnormal signals like epileptic EEG. Important features such as energy, entropy and standard deviation at different sub-bands were computed using the wavelet functions—Haar, Daubechies (orders 2-10), Coiflets (orders 1-10), and Biorthogonal (orders 1.1, 2.4, 3.5, and 4.4). Feature vectors were used to model and train the Probabilistic Neural Network (PNN) and the classification accuracies were evaluated for each case. The results obtained from PNN classifier were compared with Support Vector Machine (SVM) classifier. From the statistical analysis, it was found that Coiflets 1 is the most suitable candidate among the wavelet families considered in this study for accurate classification of the EEG signals. In this work, we have attempted to improve the computing efficiency as it selects the most suitable wavelet function that can be used for EEG signal processing efficiently and accurately with lesser computational time.     

基于小波变换的动态脑电节律提取

针对脑电信号和其他医学信号的非平稳性,引入小波变换处理临床脑电信号的动态特性。根据脑电信号的不同节律特性,提出应用小波包变换构造不同频率特性的滤波器,提取脑电信号的４种节律,并由各种节律对应的小波系数构造动态脑电地形图。为了研究不同脑功能状态下脑电信号４种节律的动态特性,文中对两组不同临床脑电数据进行分析与比较,给出了有关的实际分析结果。实验结果表明,利用小波包分析的滤波特性,能够有效地反映临床脑电不同节律的动态特性,也为分析其他生物医学信号提供了一条新的途径。     

基于能量特征的脑电信号特征提取与分类

为了快速、有效地提取脑电特征,提高分类正确率,采用带通滤波和小波包分析的方法提取Mu、Beta节律对应的脑电信号,在时域范围内,将信号幅度的平方作为能量特征值;在频域范围内,采用AR模型功率谱估计法所得的功率谱密度作为能量特征值.根据运动想象脑电信号特点,构造左右通道信号能量差值的符号特性作为分类判别依据,进行分类测试,方法简单.初步实验结果表明,所利用的两种方法的分类正确率达87.857%.     

Examination of scale-invariant characteristics of epileptic electroencephalograms using wavelet-based analysis

There is evidence that biological and physiological systems including the brain exhibit can exhibit fractal characteristics that can be used to identify the state of the system. In this study, wavelet-based fractal analysis is used to examine self-similar or scale-invariant characteristics of intracranial EEG data in terms of the spectral exponent. The intracranial EEG data were recorded from subjects with epilepsy during non-seizure period and during epileptic seizure activity. From the computational results, it is observed that the self-similar or scale-invariant characteristics of the intracranial EEG data obtained during these two periods are significantly different. The actual value of the estimated spectral exponent depends on the wavelet bases used for the computations.