独立分量分析及其应用

独立分量分析是信号处理领域的一个研究热点,它能从混合信号中分离出既具有统计独立性又具有非高斯性的源信号。介绍了独立分量分析的数学模型及其假设条件、求解方法,在此基础上分析了其在语音信号处理、特征提取、生物医学信号处理等领域的应用,最后指出了其发展趋势与进一步的研究方向。     

基于fMRI瞬时功率的独立成分分析

独立成分分析(independent component analysis,ICA)采用一种统计隐变量模型,假设信号是由各信源线性叠加构成.为了解决功能磁共振数据(functional magnetic resonance imaging,fMRI)中由于信源非线性叠加造成的ICA检测误差,提出了基于瞬时功率的ICA方法.首先,由电流能量形式将fMRI数据推广为fMRI能量信号;然后,由血氧水平依赖(blood oxygenation level dependent,BOLD)信号与T2*信号的关系,给出了两种反映BOLD能量变化的瞬时功率fMRI信号;最后,采用空间ICA分析fMRI瞬时功率信号,得到与各脑部活跃区域能量相关的独立成分.从理论和仿真试验两个方面阐明了新方法的合理性和优越性,同时应用于实际癫痫fMRI数据,经与传统ICA方法比较,该方法能够在静息态下鲁棒地检测脑部能量异常区域.     

基于小波变换的功能磁共振图像时间序列分步去噪

功能磁共振图像（fMRI）数据中反映大脑神经活动的感兴趣信号常受到结构噪声和随机噪声的影响。为消除上述噪声对分析激活体素的影响,对经过SPM标准预处理的体素时间序列进行Activelets小波变换,并在得到尺度系数及细节系数后,针对两类噪声的不同特点进行分步去噪。第一步,在受结构噪声影响的尺度系数上,选用独立成分分（ICA）析去识别并消除结构噪声源;第二步,提出一种改进的空域相关去噪算法在细节系数上对信号进行处理。值得注意的是,该算法利用邻域体素之间的相似性,判定所处位置的细节系数反映噪声还是神经活动。实验结果表明,经过这两步处理的数据可有效消除噪声的影响,其中框架位移减少了1.5mm,尖峰百分比减少了2%,此外由去噪后的信号获得的脑激活图中一些明显的伪激活区得到抑制。     

A simplified approach to independent component analysis

Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an array of sensors. Various statistical techniques based on information theoretic and algebraic approaches exist for performing ICA. In this paper, we have used an objective function based on independence criterion of the signals. Optimisation of this objective function yields a neural algorithm along with a non-linear function for signal separation. Performance of the algorithm for artificially generated signals as well as audio signals has been evaluated.     

独立分量分析及其在图像处理中的应用现状

独立分量分析是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,近年来作为信号处理和图像处理领域的强有力的分析处理工具得到广泛的关注和研究。在介绍了独立分量分析的基本概念和各种实现算法及其性能的基础上,综述了独立分量分析在图像处理上的应用,最后结合作者的研究探索,总结了独立分量分析的研究新进展和发展趋势。     

基于语音信号稀疏性的FDICA初始化和后处理方法

目前解决语音信号盲源分离(Blind source separation,BSS)的两大类方法分别为频域独立成分分析(Frequency domain independent component analysis,FDICA)和基于稀疏性的时频掩蔽(Time frequency masking,TF masking).为此将两类方法优点相结合,利用TF masking方法的结果,对FDICA做初始化,在加快FDICA收敛速度的同时也避免了次序不确定性问题.此外还提出了一种新的基于语音稀疏性FDICA的BSS后处理方法:基于局部最小比例控制(Local minimum ratio controlled,LMRC)谱减法,比常规的TF masking、维纳滤波等后处理方法,能够更有效地控制音乐噪声,提高分离性能.合成数据和实际采集数据的实验结果验证了所提方法的有效性.     

ICA算法在fMRI中的应用

独立分量分析（ICA）是信号处理领域新近发展起来的一种很有应用前景的方法,而脑功能磁共振（fMRI）信号的有效分离与识别是一个正在研究和实验之中的技术领域。近年来,ICA已被成功地应用于fMRI数据的处理,成为分析IMRI数据的一种很有效的方法。本文介绍了ICA在分析fMRI数据方面的应用,以及多种ICA算法在fMRI信号盲源分离中 的应用,分析了三种算法的问题,给出了本人对此研究的展望。     

一种结合信噪比的独立成分分析算法

针对传统独立成分分析算法存在的不足,在简要介绍独立成分分析的基本原理和相关算法的基础上,提出一种结合负熵与信噪比的独立成分分析法.推导了算法的关键公式,给出了实现算法,并进行了计算机仿真实验,分别使用传统算法和改进算法对模拟产生的合成数据进行分离.通过对实验结果进行的计算分析表明了所提出的改进算法比基于负熵的传统算法具有更佳的信号分离能力,能更好地从混合信号中估计出源信号.     

A comparative analysis of principal component and independent component techniques for electrocardiograms

Principal component analysis (PCA) is used for ECG data compression, denoising and decorrelation of noisy and useful ECG components or signals. In this study, a comparative analysis of independent component analysis (ICA) and PCA for correction of ECG signals is carried out by removing noise and artifacts from various raw ECG data sets. PCA and ICA scatter plots of various chest and augmented ECG leads and their combinations are plotted to examine the varying orientations of the heart signal. In order to qualitatively illustrate the recovery of the shape of the ECG signals with high fidelity using ICA, corrected source signals and extracted independent components are plotted. In this analysis, it is also investigated if difference between the two kurtosis coefficients is positive than on each of the respective channels and if we get a super-Gaussian signal, or a sub-Gaussian signal. The efficacy of the combined PCA–ICA algorithm is verified on six channels V1, V3, V6, AF, AR and AL of 12-channel ECG data. ICA has been utilized for identifying and for removing noise and artifacts from the ECG signals. ECG signals are further corrected by using statistical measures after ICA processing. PCA scatter plots of various ECG leads give different orientations of the same heart information when considered for different combinations of leads by quadrant analysis. The PCA results have been also obtained for different combinations of ECG leads to find correlations between them and demonstrate that there is significant improvement in signal quality, i.e., signal-to-noise ratio is improved. In this paper, the noise sensitivity, specificity and accuracy of the PCA method is evaluated by examining the effect of noise, base-line wander and their combinations on the characteristics of ECG for classification of true and false peaks.     

Single-channel source separation of multi-component radar signal based on EVD and ICA

This study explored a novel method based on eigenvalue decomposition (EVD) and independent component analysis (ICA) to separate the multi-component radar signal in the single channel. By exploiting the generalized periodicity of the radar signal, the proposed method structures the multi-dimensional matrix from the observed signal in single-channel through EVD, then applies ICA to the matrix to determine the basic waveform of each component, and finally reconstructs the component signals. Simulation results confirmed the effectiveness of the proposed method and compared it with other methods, although the performance of proposed approach is a bit worse than some other method when processing radar signals, the most outstanding advantage of the proposed approach is that it does not require any other known conditions, and it can recover the component signals with a satisfactory level, so it can yet be regarded as an effective method.     

应用小波变换和ICA方法的肌电信号分解

基于单通道、短时真实肌电（EMG）记录和模拟EMG信号，提出一种改进的肌电信号分解方法。首先应用小波滤波、硬阈值估计等方法去除背景噪声和白噪声，并将独立成分分析（ICA）方法和小波滤波方法相结合去除工频干扰信号，然后再进行幅度滤波，从而提高了系统的速度和强健性。在运动单元动作电位（MUAP）聚类以及从原始信号中去除已识别的MUAP波形等方面也进行了改进。与已有的EMG分解方法相比，本文方法更快速、稳定。     

基于互累积量的有噪独立分量分析方法

针对传统独立分量分析(ICA)方法无噪假设的局限性,提出基于互累积量的有噪ICA方法。考虑含高斯噪声的瞬时混合模型,以观测信号的互累积量组成一系列对称矩阵,以对称矩阵的联合对角化程度为目标函数,采用粒子群优化算法对混合矩阵进行全局寻优。通过寻优得到混合矩阵,将有噪ICA转化为一维欠定ICA,基于奇异值分解法得到源信号的估计。仿真结果表明,与传统ICA方法相比,该方法对混合矩阵的估计精度较高,可以明显提高分离信号的信噪比。     

结合ICA和PCA方法的胎儿心电提取

在生物医学信号处理领域,独立分量分析(PCA)和主分量分析(ICA)是两种广泛应用的方法。但是,这两种方法各有其优缺点。提出了一种新颖的方法,将ICA和PCA相结合,通过求相关的技术,分别取ICA和PCA方法的优点。将该方法应用于从母体腹部测得的多通道信号中提取胎儿心电信号的实验,得到令人满意的结果。研究结果表明,这种结合ICA和PCA的方法能够比较准确地分离出所需要的胎儿心电信号,进而可以对胎儿心电进行监护,因此在临床上具有一定的实用价值。     

脑图像数据中的独立分量分析方法

针对脑功能磁共振成像在处理数据时空间维数较大的问题,提出一种空间独立分量分析(ICA)方法。研究空间ICA方法的基本模型结构和空间ICA的3种常见算法,即Infomax算法、Fixed-Point算法和Orth-Infomax算法。设计中文词义辨别实验,并使用线性相关方法进行算法比较。实验结果表明,与Infomax算法、Fixed-Point算法相比,Orth-Infomax算法任务相关分量的时间序列与参考函数的平均相关系数最大,具有较高的求解质量和求解效率,能够有效处理脑功能磁共振成像系统中存在的大量数据。     

A novel method for spatio-temporal pattern analysis of brain fMRI data

1 Introduction Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique which has been utilized in brain function researches since the early 1990s[1]. However, it is often difficult to do analysis in fMRI data because of the low signal to noise ratio (SNR) (about 2%—4% with 1.5T magnetic field strength) and the delay within the true neural activity and the stimuli-induced signal dynamic responses. The prevalent methods applied to fMRI data could be divided i…     

ICA,kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data

In the last decades, functional magnetic resonance imaging (fMRI) has been introduced into clinical practice. As a consequence of this advanced noninvasive medical imaging technique, the analysis and visualization of medical image time-series data poses a new challenge to both research and medical application. But often, the model data for a regression or generalized linear model-based analysis are not available. Hence exploratory data-driven techniques, i.e. blind source separation (BSS) methods are very popular in functional nuclear magnetic resonance imaging (fMRI) data analysis since they are neither based on explicit signal models nor on a priori knowledge of the underlying physiological process. The independent component analysis (ICA) represents a main BSS method which searches for stochastically independent signals from the multivariate observations. In this paper, we introduce a new kernel-based nonlinear ICA method and compare it to standard BSS techniques. This kernel nonlinear ICA (kICA) overcomes the restrictions of linearity of the mixing process usually encountered with ICA. Dimension reduction is an important preprocessing step for this nonlinear technique and is performed in a novel way: a genetic algorithm is designed which determines the optimal number of basis vectors for a reduced-order feature space representation as an optimization problem of the condition number of the resulting basis. For the fMRI data, a comparative quantitative evaluation is performed between kICA with different kernels, nonnegative matrix factorization (NMF) and other BSS algorithms. The comparative results are evaluated by task-related activation maps, associated time courses and ROC study. The comparison is performed on fMRI data from experiments with 10 subjects. The external stimulus was a visual pattern presentation in a block design. The most important obtained results in this paper represent that kICA and sparse NMF (sNMF) are able to identify signal components with high correlation to the fMRI stimulus, and kICA with a Gaussian kernel is comparable to standard ICA algorithms and even more, it yields spatially focused results.     

多通道表面肌电信号降噪与去混迭研究

通过数据采集装置同时采集多路表面肌电信号(sEMG)时,信号之间往往存在相互混迭的现象。为了得到有效的sEMG,提出了一种基于二代小波变换和独立分量分析(ICA)相结合的降噪与去混迭方法。先利用二代小波变换对sEMG降噪再利用改进的FastICA算法对降噪后的信号进行ICA分离,最后通过互相关系数验证去混迭效果。实验结果表明,所提方法能够有效降低噪声并去除相邻通道间产生的混迭。     

基于独立分量分析的单通道语音增强算法

传统的独立分量分析要求观测信号的个数不能小于源信号的个数，无法直接对单路信号进行独立分量分析。为了能够利用独立分量分析分离加性噪声，须构造一路观测信号。基于语音信号的短时平稳的特性，该文提出一种构造噪声信号的算法，实现了信号与噪声的分离。仿真结果表明，利用该算法可得到很好的消噪结果，提高信号的信噪比。     

Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT??= 670 ms reverberant environment.     

一种基于FastICA的波达方向估计新方法

独立分量分析是一种新颖的盲源分离技术，该方法作为目前信号处理领域的一项新技术，具有非常重要的理论意义和实用价值，已广泛应用于通讯、雷达信号处理、生物医学图像处理、模式识别等众多领域。简要介绍了独立分量分析的基本原理和算法，并提出将快速独立分量分析（FastICA）方法应用于波达方向估计（DOA），通过仿真实验和分析，可以得到DOA的一种简单估计，实验结果亦表明该算法在波达方向估计应用中的可行性和有效性。