A new method for fMRI data processing: Neighborhood independent component correlation algorithm and its preliminary application

Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.     

基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究

脑电(Electroencephalography, EEG)与功能磁共振成像(Functional magnetic resonance imaging, fMRI)为脑科学研究提供了互补的时空信息. 为研究大脑在对情绪图片采取认知重评策略时的神经活动, 基于同步采集的EEG-fMRI数据, 应用典型相关分析、经验模态分解及k-均值聚类等算法对融合情绪数据进行交叉关联和盲源分离, 得到空间上的fMRI图像和与之对应的EEG时间演变信号. 结果表明: 时域上, CCA分离出的脑电成分在认知重评状态下有明显的晚期正电位(Late positive potential, LPP) (潜伏期200ms～900ms)出现, 而且认知重评策略诱发下的LPP 波幅明显小于观看负性诱发的LPP波幅(F(1, 224)= 28.72, P<0.01), 而大于观看中性诱发的LPP波幅(F(1, 224)= 63.32, P<0.01); 与之对应的空域上, 可以明显地看出和情绪调节相关的扣带回, 额叶、颞叶等区域有明显激活区, 采用情绪认知重评策略时的脑区激活强度明显小于观看负性状态, 而大于观看中性, 且观看中性状态下被激活的与情绪相关的区域相对较少. 研究表明, 这种融合数据分析技术通过计算两种模态数据之间潜在的线性相关性, 可以有效地分离出大脑在时空上神经活动情况, 达到了同时描绘出大脑神经活动的时间信息与空间信息的效果.     

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.     

Canonical correlation analysis using within-class coupling

Fisher’s linear discriminant analysis (LDA) is one of the most popular supervised linear dimensionality reduction methods. Unfortunately, LDA is not suitable for problems where the class labels are not available and only the spatial or temporal association of data samples is implicitly indicative of class membership. In this study, a new strategy for reducing LDA to Hotelling’s canonical correlation analysis (CCA) is proposed. CCA seeks prominently correlated projections between two views of data and it has been long known to be equivalent to LDA when the data features are used in one view and the class labels are used in the other view. The basic idea of the new equivalence between LDA and CCA, which we call within-class coupling CCA (WCCCA), is to apply CCA to pairs of data samples that are most likely to belong to the same class. We prove the equivalence between LDA and such an application of CCA. With such an implicit representation of the class labels, WCCCA is applicable both to regular LDA problems and to problems in which only spatial and/or temporal continuity provides clues to the class labels.     

自适应阈值化功能磁共振统计参数映射图

为了准确检测及定位功能激发信息,需选择一个客观、有效的阈值来阈值化功能磁共振统计参数映射图.为此提出了一种组合控制错误发现率及分析皮层血流动力学响应的空间相关性阈值化功能磁共振统计参数映射图的方法.该方法首先采用基于控制错误发现率的方法确定阈值,进行激发体素判别,然后分析已判别为激发的体素与其三维空间26-邻域体素的血流动力学响应的相关性,并进行空间相关检验.该方法不仅能够自适应地选取阈值,而且能够识别由于随机因素而导致的伪激发体素,具有更好脑功能激发信息检测及空间定位能力.     

Robust space–time adaptive processing with colored loading using iterative optimization

The performance of space–time adaptive processing (STAP) may degrade dramatically if some undesired mismatches exist in real scenarios, such as array calibration error, distorted antenna shape, direction of arrival (DOA) and Doppler frequency mismatches between the actual and presumed responses to the desired target signal, insufficient training data samples and so on. In this paper, we develop a new approach to STAP that is robust to different variations in real scenarios. This method is based on the iterative optimization for the spatial–temporal separate filter. It is confirmed that this method belongs to the class of colored loading algorithms. The loading factor can be efficiently calculated based on the known level of the uncertainty mismatch sets of spatial temporal steering vectors. Computer simulations demonstrate that the proposed robust two-dimensional (2-D) beamformer with colored loading has attained better performance as compared to the conventional STAP algorithm.     

Effect of the line scanning driving of active-matrix display on the measurement of display temporal characteristics

Temporal characteristics are crucial factors influencing display performance. For an active-matrix display, signals are applied sequentially line-by-line, and the temporal response of each horizontal line changes depending on different timings. When recording the temporal response of a display, the probe of the measuring device must have the finite size and shutter time. As the finite-size probe records the temporal responses of multiple lines with different timings, the number of lines covered by the finite-size probe affects the obtained result. And the effects due to the timing difference of the sequential driving can be misinterpreted as the temporal response of the display. In this study, the contribution of the various factors, such as the camera shutter time, probe size of the measuring device, temporal transition time, and display frame rate was evaluated to separate the temporal characteristics of the display from the effect caused by these factors. A procedure to estimate only the temporal response of a display without such effects is devised. The effectiveness of the proposed procedure was verified based on the spatial temporal record obtained by a high-speed camera of video frame rates of 9000 and 12,000 per second.     

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.     

改进小波多分辨力分析的数据融合算法

在小波分析中,多分辨力分析是一重要的方法。但该方法只对低频段的近似信号进行逐级细分,导致低频段频率分辨力越来越高,而高频段的细节信号保持不变,频率分辨力较低。为了克服小波多分辨力分析在高频段频率分辨力低的缺点,采用改进的小波多分辨力分析方法,由于该方法对高频段进行逐级细分,改善了小波变换在高频段的时频局部化性能,提高了小波变换高频段频率的分辨力。同时,将改进的小波多分辨力分析方法应用到多传感器数据融合中,经过仿真计算,其结果表明:该算法是有效的。     

SARS时空分析系统的设计与实现

介绍了SARS时空分析系统的系统结构、数据流程和功能模块,功能模块包括数据库查询和显示、时间态势分析、空间态势分析、空间风险动态区划、时空过程预测、疫情空间传播机理和参数。通过MapObjects控件把这些模块集成为独立运行的软件,系统实现了SARS时空数据的查询和显示,并采用多个时空分析模型从不同角度对SARS时空传播过程进行分析、模拟和预测。     

fMRI动态功能网络构建及其在脑部疾病识别中的应用

如何从复杂的fMRI数据中提取 丰富的大脑信息是提高脑部疾病识别精度的关键。传统的静息态功能磁共振成像分析中,功能连接网络被认为是稳定不变的。提出一种基于成组独立成分分析的构建动态功能连接网络的方法,并通过该网络来获取功能网络本身的动态特性。首先,利用成组独立成分分析法提取fMRI数据的空间独立成分作为网络节点,并通过滑动时间窗的方法获取窗口时间序列,构建动态功能连接网络。以动态功能网络作为特征,对精神分裂症患者和正常被试数据进行分类识别。实验结果表明,该方法能够获取fMRI数据的时间维度信息,提高识别效果,在一定程度上能为临床诊断提供客观参照。     

fMRI analysis on the GPU-possibilities and challenges

Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64 × 64 × 22 voxels), all the preprocessing takes about 0.5 s on the GPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10,000 permutations, with smoothing in each permutation, takes about 50 s if three GPUs are used, compared to 0.5-2.5 h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.     

Constructing an exactly periodic subspace for enhancing SSVEP based BCI

A novel exactly periodic spatial filtering (EPSD) approach, that provides a robust detection performance, is introduced and evaluated in this study. The proposed method exploits the temporal properties of the steady-state visual evoked potential (SSVEP) response to construct an orthogonal and exactly periodic mapping that enhances the signal to noise ratio (SNR) of the SSVEP embedded in the electroencephalogram (EEG) data. The subspace of interest is constructed via the elimination of the signals spaces that does not constitute the exact period of the target frequency. The EPSD is evaluated on a 35 subject benchmark dataset collected using a 40 target SSVEP BCI system. The results reveal that the proposed EPSD spatial filter significantly enhances the performance of target detection. Further statistical tests also confirm that the EPSD is a potential alternative to the existing SSVEP spatial filters for realizing an efficient BCI system.     

基于加速度传感器的高频振动信号检测分析

介绍了一种基于普通压电加速度传感器的高频振动信号检测技术的实现方法,并论述了高频振动信号的采集、时域分析、频域分析及功率谱分析程序的实现。检测系统硬件部分主要包括压电加速度传感器、信号调理器、高速采集卡。用这套检测系统对各频段的信号进行检测对比实验,实验结果表明此系统能有效地检测到高频振动信号的变化。     

基于动态功能连接的运动任务大脑状态表达

针对传统静态功能连接分析技术不能准确反映大脑动态功能状态的问题,提出了一种基于全脑动态功能连接(DFC)分析对大脑的状态变化进行表达的方法。首先,利用个体的弥散张量成像(DTI)数据构建高精确度全脑网络,将运动任务下功能磁共振成像(fMRI)数据映射到相应DTI空间后,提取各节点fMRI信号;然后,采用滑动时间窗口方法计算随时间变化的全脑功能连接强度矩阵,并提取动态功能连接向量(DFCV)样本;最后,将所有个体的DFCV样本通过基于Fisher准则的字典学习(FDDL)算法进行稀疏表达和分类。共得到8个该运动任务下全脑功能连接状态模式,各模式的功能连接强度空间分布具有明显差异,模式1、模式2和模式3占据了大部分样本分布(77.6%),且与平均静态功能连接强度矩阵之间的相似度明显高于其他5个模式。此外,大脑在各模式之间的状态迁移遵循一定的规律。实验结果表明,采用全脑DFC和FDDL学习相结合的方法,能够有效地对任务态下大脑的功能状态变化进行表达,为研究脑动态信息处理机制提供基础。     

Spatio-temporal analysis for obstacle detection in agricultural videos

Autonomous mobile vehicles are becoming commoner in outdoor scenarios for agricultural applications. They must be equipped with a robot navigation system for sensing, mapping, localization, path planning, and obstacle avoidance. In autonomous vehicles, safety becomes a major challenge where unexpected obstacles in the working area must be conveniently addressed. Of particular interest are, people or animals crossing in front of the vehicle or fixed/moving uncatalogued elements in specific positions. Detection of unexpected obstacles or elements on video sequences acquired with a machine vision system on-board a tractor moving in cornfields makes the main contribution to this research. We propose a new strategy for automatic video analysis to detect static/dynamic obstacles in agricultural environments via spatial-temporal analysis. At a first stage obstacles are detected by using spatial information based on spectral colour analysis and texture data. At a second stage temporal information is used to detect moving objects/obstacles at the scene, which is of particular interest in camouflaged elements within the environment. A main feature of our method is that it does not require any training process. Another feature of our approach consists in the spatial analysis to obtain an initial segmentation of interesting objects; afterwards, temporal information is used for discriminating between moving and static objects. To the best of our knowledge in the field of agricultural image analysis, classical approaches make use of either spatial or temporal information, but not both at the same time, making an important contribution. Our method shows favourable results when tested in different outdoor scenarios in agricultural environments, which are really complex, mainly due to the high variability in the illumination conditions, causing undesired effects such as shadows and alternating lighted and dark areas. Dynamic background, camera vibrations and static and dynamic objects are also factors complicating the situation. The results are comparable to those obtained with other state-of-art techniques reported in literature.     

基于相关性分析的多模式多视点视频编码

多视点视频编码方法除需具有较高编码效率外,还必须支持视点或时间的随机访问、低延时编解码、视点可分级等性能.多视点视频信号的时间、视点间相关性随相机密度、光照、对象运动等因素不同而变化.文中提出基于多视点视频信号相关性分析的多模式多视点视频编码方法,改变传统单一预测模式的多视点编码结构,将多种性能优良的预测编码模式有机结合,根据多视点视频相关性分析灵活选择合适的预测编码模式,以获得优异的编码综合性能.实验结果表明,所提出的多模式多视点视频编码方法在保证高压缩效率的前提下,可进一步降低复杂度,提高随机访问性能.     

Effective connectivity analysis of fMRI data based on network motifs

Exploring effective connectivity between neuronal assemblies at different temporal and spatial scales is an important issue in human brain research from the perspective of pervasive computing. At the same time, network motifs play roles in network classification and analysis of structural network properties. This paper develops a method of analyzing the effective connectivity of functional magnetic resonance imaging (fMRI) data by using network motifs. Firstly, the directed interactions between fMRI time-series are analyzed based on Granger causality analysis (GCA), by which the complex network is built up to reveal the causal relationships among different brain regions. Then the effective connectivity in complex network is described with a variety of network motifs, and the statistical properties of fMRI data are characterized according to the network motifs topological parameters. Finally, the experimental results demonstrate that the proposed method is feasible in testing and measuring the effective connectivity of fMRI data.     

An algorithmic strategy for in-network distributed spatial analysis in wireless sensor networks

A wireless sensor network (WSN) can be construed as an intelligent, largely autonomous, instrument for scientific observation at fine temporal and spatial granularities and over large areas. The ability to perform spatial analyses over sensor data has often been highlighted as desirable in areas such as environmental monitoring. Whilst there exists research on computing topological changes of dynamic phenomena, existing proposals do not allow for more expressive in-network spatial analysis. This paper addresses the challenges involved in using WSNs to identify, track and report topological relationships between dynamic, transient spatial phenomena and permanent application-specific geometries focusing on cases where the geometries involved can be characterized by sets of nodes embedded in a finite 2-dimensional space. The approach taken is algebraic, i.e., analyses are expressed as algebraic expressions that compose primitive operations (such as Adjacent, or AreaInside). The main contributions are distributed algorithms for the operations in the proposed algebra and an empirical evaluation of their performance in terms of bit complexity, response time, and energy consumption.     

An overview of air quality analysis by big data techniques: Monitoring,forecasting, and traceability

With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas.