一种基于能量选择的改进TQWT海杂波抑制算法

针对雷达对海探测中海杂波抑制问题,研究了基于能量选择的改进可调Q小波变换(TQWT)海杂波抑制算法。根据目标和海杂波振荡属性差异,通过TQWT将回波信号分解到不同子带的小波系数中,并利用基追踪去噪(BPD)对小波系数稀疏优化后进行目标重构。为更好的重构目标信号,文中基于子带能量最优分布准则,提出了能量选择法,提取目标能量占优层级的小波系数进行重构,实现目标与海杂波的有效分离。论文分析了雷达观测时间对算法性能的影响,为该方法的工程应用提供指导。通过在CSIR公开数据集上进行实验,结果验证了该算法的性能,改善了输出信杂比。     

在线脑机接口中脑电信号的特征提取与分类方法

在脑机接口研究中,针对运动想象脑电信号的特征抽取,提出了一种基于离散小波变换和AR模型的方法.利用Daubechies类小波函数对脑电信号进行3层分解,抽取小波变换系数的统计特征;利用Burg算法提取脑电信号6阶AR模型系数.将这两类特征进行组合后使用神经网络、支持向量机、马氏距离线性判别进行分类并比较分析.采用BCI...     

基于相关性和稀疏表示的运动想象脑电通道选择方法

在基于运动想象(MI)的脑机接口(BCI)中,通常采用较多通道的脑电信号(EEG)来提高分类精度,但其中会有包含与MI任务无关或冗余信息的通道,从而影响BCI的性能提升。该文针对运动想象脑电分类中的通道选择问题,提出一种采用相关性和稀疏表示对通道进行选择的方法(CSR-CS)。首先计算训练样本每个通道的皮尔逊相关系数来选择显著通道,然后提取显著通道所在区域的滤波器组共空间模式特征拼接成字典,利用由字典所得到的非零稀疏系数的个数表征每个区域的分类能力,选出显著区域所包含的显著通道作为最优通道,最后采用共空间模式和支持向量机分别进行特征提取与分类。在对BCI第3次竞赛数据集IVa和BCI第4次竞赛数据集I两个二分类MI任务的分类实验中,平均分类精度达到了88.61%和83.9%,表明所提通道选择方法的有效性和鲁棒性。     

基于条件经验模式分解和串并行CNN的脑电信号识别

针对运动想象脑电信号(EEG)的非线性、非平稳特点,该文提出一种结合条件经验模式分解(CEMD)和串并行卷积神经网络(SPCNN)的脑电信号识别方法。在CEMD过程中,采用各阶固有模式分量(IMF)与原始信号的相关性系数作为第1个IMF筛选条件,在此基础上,提出各阶IMF之间的相对能量占有率作为第2个IMF筛选条件。此外,为了考虑脑电信号各个通道之间的特征和突出每个通道内的特征,该文提出SPCNN网络模型对进行CEMD过程后的脑电信号进行分类。实验结果表明,在自行采集的脑电数据集上平均识别率达到94.58%。在公开数据集BCI competition IV 2b上平均识别率达到82.13%,比卷积神经网络提高了3.85%。最后,在自行设计的智能轮椅脑电控制平台上进行了轮椅前进、左转和右转在线控制实验,验证了该文算法对脑电信号识别的有效性。     

基于小波能量系数和神经网络的管道缺陷识别

利用基于小波能量系数的BP神经网络方法对管道焊缝和管道凹槽进行分类识别。建立了导波检测系统,采集了管道凹槽缺陷和焊缝的多组检测信号样本,从信号样本中提取出小波能量系数,并将小波能量系数应用于BP神经网络的训练与识别。结果表明,该方法对管道缺陷的识别准确率较高,且识别效果稳定,在随机抽取信号样本进行的5次试验中,对焊缝和凹槽的最低识别准确率分别为92%和98%,最高识别准确率均为100%。     

色彩感知中的脑电信号多域特征选择算法研究

针对目前在不同色彩感知中的脑电信号识别方面的研究还不多见,本文提出采用随机森林算法对信号的时域特征和频域特征进行最优组合的方法对不同色彩感知中的脑电信号进行识别。首先采用小波变换,对脑电信号进行7层分解,提取脑电信号在delta、theta、alpha和beta节律频带上的小波能量,并结合脑电信号在时域上的统计量偏度和峰度组成特征向量。然后通过基于随机森林的特征选择算法提取最优的特征组合方案,删除冗余的特征量。使用自适应增强算法进行分类识别,识别的平均正确率可达到85.07%。该结果表明使用本文所提出的特征提取与选择方法用于不同色彩感知中的脑电信号识别上是可行的,并且能够取得较好的识别率。      

一种基于FPGA的脑电分类算法实现

通过对脑电信号特征的分析,利用小波变换的多尺度分析技术对脑电信号进行特征提取,进而使用主成分分析算法对特征进行降维,并对降维后的信号使用Fisher线性判别方法进行分类。最后,利用VerilogHDL硬件编程语言设计实现了Mallat分解算法、PCA算法和LDA算法模块,并在FPGA应用板上实现了脑电分类功能。系统对2008年BCI大赛的数据进行了测试,分类准确率达到92.31%,表明该方法对开发便携式脑机接口系统具有良好的应用价值。     

运动想象脑信号的深度置信网络分类优化

关于脑机接口（BCI）系统中的运动想象（MI）脑信号的特征提取一直是一个难题。相较于SSVEP、AEP和P300等其他BCI模式，MI的分类准确率相对较低，缺乏有效的识别方案。本文提出了一种结合深度置信网络（Deep Belief Network,DBN）和麻雀搜索算法（Sparrow search algorithm,SSA）的特征提取和分类识别算法SSA-DBN。融合SSA与DBN的优势，可以在保持较低计算复杂度的同时，提高特征提取和分类识别的准确率。本研究首先利用完全自适应噪声集合经验模态分解（CEEMDAN）方法提取信号的固有模态函数（IMF）特征。然后，将筛选出的适合分类识别的IMF分量与希尔伯特黄变换（HHT）方法相结合，提取出不同导联时频信号的特征空间向量，并进行叠加平均。最后，将特征向量输入到SSA-DBN算法进行分类处理。为确保公平性，在验证算法性能时，选取了具有代表性的BCI Competition IV Dataset 2a数据集，同时对比了其他算法的表现，并详细说明了调参方法。为了避免过拟合问题，可以考虑使用更大规模的数据集进行测试，如PhysioNet或BCI...     

单通道脑电信号中眼电干扰的自动分离方法

当前主流的眼电(EOG)去除方法需要利用多通道脑电的相关性,难以在单通道的便携式脑机接口(BCI)中应用。该文提出一种基于长时差分振幅包络与小波变换的眼电干扰自动分离方法。首先在原脑电信号的长时差分振幅包络上实施双门限法来精确检测眼电的起止点,然后利用sym5小波对脑电进行分解并引进Birg_Massart策略来自适应地确定小波重构系数阈值,最后通过小波重构精确地估计眼电,实现单通道上眼电与脑电的自动分离。大量实验证明,该方法与主流的平均伪迹回归分析和基于独立成分分析(ICA)的方法相比,能够获得更好的估计眼电与原眼电的相关性,保证更高的校正信噪比和较强的实时性,能够满足脑机接口多方面的需要。     

基于半分积分投影的车牌字符快速识别算法

提出了一种用于车牌识别的快速字符识别算法。首先利用半积分投影把大小为W×H的二值化字符图像转化为长度为2(W×H)的一维信号,保留了用于图像识别的关键特征,降低了后续算法的复杂度。然后将投影得到的数据进行离散Harr小波变换,抽取第二层小波变换后的低频系数并送入支持向量机进行训练识别。实验结果表明,所提算法可以使车牌字符的总识别率达到97.60%,平均识别时间为16.2ms,有效地提高了识别速度和精度。     

基于信号投影能量特征的脑电意识动态分类

针对脑电意识任务动态分类问题,本文提出了一种基于投影能量的特征提取方法来提取反映不同思维状态的脑电特征,并结合信息累积后验贝叶斯方法进行分类以提高脑-机接口系统的分类正确率。该方法通过使两类信号在投影基上的平均投影能量比达到极值,从而达到提高脑电信号分类准确度的作用。实验结果表明两个运动想象数据集上的最大正确率都达到90%左右,最大分类准确率、kappa系数和最大互信息等评价指标的比较也表明该方法能够有效提高BCI系统的性能,具有较好的实用性。     

Oscillatory-Plus-Transient Signal Decomposition Using TQWT and MCA

This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT; similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.     

Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection.

A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.     

Optimizing the channel selection and classification accuracy in EEG-based BCI

Multichannel EEG is generally used in brain-computer interfaces (BCIs), whereby performing EEG channel selection 1) improves BCI performance by removing irrelevant or noisy channels and 2) enhances user convenience from the use of lesser channels. This paper proposes a novel sparse common spatial pattern (SCSP) algorithm for EEG channel selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and outperformed existing channel selection methods based on Fisher criterion, mutual information, support vector machine, common spatial pattern, and regularized common spatial pattern in classification accuracy. The proposed SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three channels (C3, C4, and Cz).     

基于脑电和眼电的运动想象多尺度识别方法研究

基于脑电信号对同一肢体不同动作想象模式进行识别的正确率低,已成为基于脑机接口对肢体瘫痪患者进行运动想象训练监控的方法,获得临床应用前必须解决的瓶颈问题.针对该问题,本文提出一种利用运动想象时眼睛的活动状态与所想象肢体动作之间存在的耦合关系,进行运动想象多尺度识别的新方法.该方法首先在大尺度上,利用脑电信号对运动想象是否发生进行识别,再结合同一运动想象过程眼电信号协同变化模式的识别结果,基于决策融合在更精细的尺度上,对同一肢体不同动作的想象模式进行识别.实验结果表明,仅基于脑电进行右臂三种动作想象模式识别的平均正确率为63.0%,而应用所提出方法可以将其提高到91.4%.所提出方法可望有临床应用前景.     

Multiclass common spatial patterns and information theoretic feature extraction

We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain--computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4% in comparison to multiclass CSP.     

Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming

A novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on a novel space-time-frequency (STF) model of EEGs and robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, namely, an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, the vector corresponding to the spatial distribution of the EB factor, is identified using the STF model of EEGs, provided by the parallel factor analysis (PARAFAC) method. In order to reduce the computational complexity present in the estimation of the STF model using the three-way PARAFAC, the time domain is subdivided into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC. The correct number of the factors of the STF model is effectively estimated by using a novel core consistency diagnostic- (CORCONDIA-) based measure. Subsequently, the STF-TS model is shown to closely approximate the classic STF model, with significantly lower computational cost. The results confirm that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements.     

基于互信息的脑网络及测谎研究

互信息分析方法是基于信息论提出的一种描述两信号间信息交互情况的算法,其在脑电信号领域的有效性已得到了充分证实.针对当前测谎方法中脑电信号特征提取困难以及大脑整体认知功能分析在脑认知科学研究中越来越被重视的情况,本文首次将互信息分析方法应用到脑电测谎领域中,使用互信息量化大脑各节点之间的相关性,对计算结果进行统计分析,选取出在两类人群中具有显著性差异的电极对的互信息作为分类特征,进行模式识别,得到了99.67%的准确率.这一结果表明,互信息分析方法是一种有效的脑功能连接分析方法,为基于脑电信号连接分析的测谎研究提供了一种新的途径.另外,对说谎与诚实两类受试者的大脑功能网络的分析结果表明：处于说谎状态时,大脑的额叶、顶叶、颞叶及枕叶之间协同实现谎言功能,并在躯体行为所对应的脑区与其他脑区的连接上也表现出相对诚实组的显著性差异,以上结果均有助于进一步揭示谎言的神经活动机制.     

Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection

A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands. Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: (1) unsupervised k-means clustering; (2) linear and quadratic discriminant analysis; (3) radial basis function neural network; (4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different combinations of mixed-band feature spaces consisting of promising parameters from phase one of the research were investigated. It is concluded that all three key components of the wavelet-chaos-neural network methodology are important for improving the EEG classification accuracy. Judicious combinations of parameters and classifiers are needed to accurately discriminate between the three types of EEGs. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy, a high value of 96.7%.     

Adaptive wavelet transform-based method for recognizing characteristic oscillatory patterns

The problem concerning the automatic recognition of characteristic oscillatory patterns in multicomponent signals is investigated using the brain’s electric activity records, electroencephalograms (EEGs), as an example. It has been ascertained that recognition errors can be decreased by optimally selecting continuous wavelet transform (CWT) parameters to obtain characteristics describing the most important information on analyzed patterns. The adaptive CWT-based method for identifying the characteristic types of EEG rhythmic activity is proposed.