Paired MEG data set source localization using recursively applied and projected (RAP) MUSIC

An important class of experiments in functional brain mapping involves collecting pairs of data corresponding to separate "Task" and "Control" conditions. The data are then analyzed to determine what activity occurs during the Task experiment but not in the Control. Here we describe a new method for processing paired magnetoencephalographic (MEG) data sets using our recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this method the signal subspace of the Task data is projected against the orthogonal complement of the Control data signal subspace to obtain a subspace which describes spatial activity unique to the Task. A RAP-MUSIC localization search is then performed on this projected data to localize the sources which are active in the Task but not in the Control data. In addition to dipolar sources, effective blocking of more complex sources, e.g., multiple synchronously activated dipoles or synchronously activated distributed source activity, is possible since these topographies are well-described by the Control data signal subspace. Unlike previously published methods, the proposed method is shown to be effective in situations where the time series associated with Control and Task activity possess significant cross correlation. The method also allows for straightforward determination of the estimated time series of the localized target sources. A multiepoch MEG simulation and a phantom experiment are presented to demonstrate the ability of this method to successfully identify sources and their time series in the Task data.     

Modified beamformers for coherent source region suppression

Many tomographic source localization algorithms used in biomagnetic imaging assume, explicitly or sometimes implicitly, that the source activity at different brain locations are either independent or that the correlation structure between sources is known. Among these algorithms is a class of adaptive spatial filters known as beamformers, which have superior spatiotemporal resolution abilities. The performance of beamformers is robust to weakly coherent sources. However, these algorithms are extremely sensitive to the presence of strongly coherent sources. A frequent mode of failure in beamformers occurs with reconstruction of auditory evoked fields (AEFs), in which bilateral auditory cortices are highly coherent in their activation. Here, we present a novel beamformer that suppresses activation from regions with interfering coherent sources. First, a volume containing the interfering sources is defined. The lead field matrix for this volume is computed and reduced into a few significant columns using singular value decomposition (SVD). A vector beamformer is then constructed by rejecting the contribution of sources in the suppression region while allowing for source reconstruction at other specified regions. Performance of this algorithm was first validated with simulated data. Subsequent tests of this modified beamformer were performed on bilateral AEF data. An unmodified vector beamformer using whole head coverage misplaces the source medially. After defining a suppression region containing the temporal cortex on one side, the described method consistently results in clear focal activations at expected regions of the contralateral superior temporal plane.     

Source localization using recursively applied and projected (RAP)MUSIC

A new method for source localization is described that is based on a modification of the well-known MUSIC algorithm. In classical MUSIC, the array manifold vector is projected onto an estimate of the signal subspace. Errors in the estimate of the signal subspace can make localization of multiple sources difficult. Recursively applied and projected (RAP) MUSIC uses each successively located source to form an intermediate array gain matrix and projects both the array manifold and the signal subspace estimate into its orthogonal complement. The MUSIC projection to find the next source is then performed in this reduced subspace. Special assumptions about the array manifold structure, such as Vandermonde or shift invariance, are not required. Using the metric of principal angles, we describe a general form of the RAP-MUSIC algorithm for the case of diversely polarized sources. Through a uniform linear array simulation with two highly correlated sources, we demonstrate the improved Monte Carlo error performance of RAP-MUSIC relative to MUSIC and two other sequential subspace methods: S and IES-MUSIC. We then demonstrate the more general utility of this algorithm for multidimensional array manifolds in a magnetoencephalography (MEG) source localization simulation     

Spatio-temporal EEG source localization using a three-dimensional subspace FINE approach in a realistic geometry inhomogeneous head model

The subspace source localization approach, i.e., first principle vectors (FINE), is able to enhance the spatial resolvability and localization accuracy for closely-spaced neural sources from EEG and MEG measurements. Computer simulations were conducted to evaluate the performance of the FINE algorithm in an inhomogeneous realistic geometry head model under a variety of conditions. The source localization abilities of FINE were examined at different cortical regions and at different depths. The present computer simulation results indicate that FINE has enhanced source localization capability, as compared with MUSIC and RAP-MUSIC, when sources are closely spaced, highly noise-contaminated, or inter-correlated. The source localization accuracy of FINE is better, for closely-spaced sources, than MUSIC at various noise levels, i.e., signal-to-noise ratio (SNR) from 6 dB to 16 dB, and RAP-MUSIC at relatively low noise levels, i.e., 6 dB to 12 dB. The FINE approach has been further applied to localize brain sources of motor potentials, obtained during the finger tapping tasks in a human subject. The experimental results suggest that the detailed neural activity distribution could be revealed by FINE. The present study suggests that FINE provides enhanced performance in localizing multiple closely spaced, and inter-correlated sources under low SNR, and may become an important alternative to brain source localization from EEG or MEG.     

Performance evaluation of a new BPSK-OFDM timing estimation algorithm

Previously, the timing estimation in OFDM system employs cyclic prefix has been presented with the assumption of independent identified distributed OFDM symbol. The information used to find the symbol synchronization depends on the length of cyclic prefix. Actually, the data in the BPSK-OFDM modulated symbol, transferred by inverse fast Fourier transform, is with a complex symmetry character. With this character, more information from the whole OFDM symbol could be provided for the symbol timing. In this paper, the proposed algorithm uses the autocorrelation of the received signal to obtain the symbol timing. The simulation of Beek’s algorithm and the proposed scheme are presented. The performances of these algorithms are evaluated based on the missing symbol probability and the estimator mean square error. The simulation results show that the proposed algorithm is suitable to achieve a better symbol synchronization.     

Image restoration using a modified Hopfield network

A modified Hopfield neural network model for regularized image restoration is presented. The proposed network allows negative autoconnections for each neuron. A set of algorithms using the proposed neural network model is presented, with various updating modes: sequential updates; n-simultaneous updates; and partially asynchronous updates. The sequential algorithm is shown to converge to a local minimum of the energy function after a finite number of iterations. Since an algorithm which updates all n neurons simultaneously is not guaranteed to converge, a modified algorithm is presented, which is called a greedy algorithm. Although the greedy algorithm is not guaranteed to converge to a local minimum, the l (1) norm of the residual at a fixed point is bounded. A partially asynchronous algorithm is presented, which allows a neuron to have a bounded time delay to communicate with other neurons. Such an algorithm can eliminate the synchronization overhead of synchronous algorithms.     

一种改进的定时误差检测算法

定时恢复环路是数字接收机同步模块的一个重要组成部分,Gardner算法以实现结构简单,不敏感于载波相位,独立于调制方式等优点作为定时误差检测算法广泛应用于数字接收机的定时恢复环路中。鉴于Gardner算法在数字接收机定时同步中的广泛应用,并通过比较Gardner算法及其改进算法的定时抖动和捕获性能,研究了一种改进的Gardner定时误差检测算法。仿真结果表明,该算法对于MPSK恒包络的调制信号定时抖动性能有很明显的改善,并且在小滚降因子下改善仍然成立,但是对于16QAM非恒包络的调制信号改善不理想。     

Comparison of 3-D maximum a posteriori and filtered backprojection algorithms for high-resolution animal imaging with microPET

We have evaluated the performance of two three-dimensional (3-D) reconstruction algorithms with data acquired from microPET, a high resolution tomograph dedicated to small animal imaging. The first was a linear filtered-backprojection algorithm (FBP) with reprojection of the missing data, and the second was a statistical maximum a posteriori probability algorithm (MAP). The two algorithms were evaluated in terms of their resolution performance, both in phantoms and in vivo. Sixty independent realizations of a phantom simulating the brain of a baby monkey were acquired, each containing three million counts. Each of these realizations was reconstructed independently with both algorithms. The ensemble of the 60 reconstructed realizations was used to estimate the standard deviation as a measure of the noise for each reconstruction algorithm. More detail was recovered in the MAP reconstruction without an increase in noise relative to FBP. Studies in a simple cylindrical compartment phantom demonstrated improved recovery of known activity ratios with MAP. Finally, in vivo studies also demonstrated a clear improvement in spatial resolution using the MAP algorithm. The quantitative accuracy of the MAP reconstruction was also evaluated by comparison with autoradiography and direct well counting of tissue samples and was shown to be superior.     

分布式多通道同步采集系统设计

为实现分布式微震采集系统中各个采集终端之间和同一采集终端多个通道之间的同步,设计了一种分布式多通道同步微震信号采集系统。利用了ADS1278解决了同一采集终端多通道之间的同步采集问题,同时使用GPS时钟同步的方法解决了不同采集终端间的同步问题,在GPS设计中提出了时钟分相算法测量1PPS信号和本地秒脉冲时间误差,通过PID算法计算得到恒温晶振的频率偏差控制量来调整恒温晶振,最终使1PPS信号和本地秒脉冲信号高精度同步,从而实现不同终端之间的采样时钟同步。实验结果表明,不同数据采集终端之间的同步精度优于500ns,实现了分布式多通道同步信号采集。     

一种新的MC-CDMA系统同步算法

多载波CDMA(MC-CDMA)技术结合了正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)和CDMA技术的特点,在无线衰落信道中传输高速数据时具有突出的优势.整个MC-CDMA系统中同步性能起着非常重要的作用.本文在分析现有各种同步算法性能的基础上提出了一种新的MC-CDMA同步算法,理论分析和仿真表明这种新算法具有更好的同步性能且具有更低的系统复杂度.     

改进的参考独立分量分析算法

针对参考独立分量分析收敛速度较慢的问题,提出了两种基于改进的快速收敛牛顿迭代方法的参考独立分量分析方法。该方法首先对观测信号进行白化预处理,避免观测信号矩阵求逆运算,减少了算法的计算量;然后采用修正的具有三阶收敛速度的牛顿迭代方法对参考独立分量分析的代价函数进行优化,推导出快速收敛的参考独立分量分析算法。仿真实验结果表明,改进后的算法是有效的,算法收敛速度相对原算法提高了1.7倍,相对现有算法提高了1倍,而且误差更小。     

Fast-convergence filtered regressor algorithms for blindequalisation

The authors present a simple extension of the standard Bussgang blind equalisation algorithms that significantly improves their convergence properties. The technique uses the inverse channel estimate to filter the regressor signal. The modified algorithms provide quasi-Newton convergence in the vicinity of a local minimum of the chosen cost function with only a modest increase in the overall computational complexity of the system. An example of the technique as applied to the constant-modulus algorithm indicates its superior convergence behaviour     

GPS软件接收机导航电文提取的研究

导航电文提取中最重要的步骤是位同步和帧同步。传统的方法是先进行位同步再进行帧同步,并且算法复杂。文中提出了利用自相关函数直接进行帧同步的算法,该算法在实现帧同步的同时也实现了位同步。利用实际采集的GPS中频信号,基于计算机MATLAB软件平台,分别使用传统的方法和自相关函数算法完成帧同步和位同步。仿真结果表明两种方法均可以有效可靠地获得导航电文,自相关函数的算法更简单快捷。     

长持续时间数据流的并行检测算法

针对现有长持续时间数据流检测算法的实时性差、检测精度与估计精度低的问题,提出长持续时间数据流的并行检测算法。基于共享数据结构的长持续时间数据流的并行检测算法中不同线程访问共享数据结构,线程之间的同步开销过大。在此基础上,基于独立数据结构的长持续时间数据流的并行检测算法中不同线程具有本地数据结构,线程之间不需要同步,产生较少的开销。理论分析与实验结果表明,基于独立数据结构的长持续时间数据流的并行检测算法具有良好的时间效率、较高的检测精度和流持续时间估计精度。     

Adaptive nonlinear PCA algorithms for blind source separation without prewhitening

Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals.     

Nonparametric estimation of ultrasound pulses

An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori knowledge about the structure of RF line echo data and can employ a number of adjacent RF lines from an image. The prime application of the algorithm is to yield a pulse suitable for deconvolution algorithms. This will enable these algorithms to properly take into account the frequency dependence of the attenuation and its variation within a patient and among patients. It is also possible to use the estimated pulse for attenuation estimation, and the consistency of the assumptions underlying the proposed technique is demonstrated by its ability to recover low variance attenuation estimates in the normal liver from in vivo pulse-echo data. Estimates are given for 8 different patients     

X射线管道焊缝探伤中的视觉同步技术

研究了一种基于机器视觉的同步技术。实现同步的关键在于检测管内外的转角差，通过对X射线图像进行特征提取获取管内外转角差。在特征提取时，先对图像进行形态学滤波，然后用遗传算法（GA）进行图像分割，最后用Hough变换从图像中提取特征量。试验结果表明，该算法从X射线图像中准确检测出管内外的转角差，且检测速度超过了25fps的实时性要求。该算法再结合一定的控制规律控制伺服电机，即可实现管内外同步旋转。     

修正MUSIC算法对相关信号源的DOA估计性能

本文介绍了用修正ＭＵＳＩＣ算法提高相关信号源ＤＯＡ估计性能的方法。理论分析表明,采用该方法后,可将相关信号源的相关系数平均降低为原来的６３％,因而可改善ＭＵＳＩＣ算法对相关信号源的ＤＯＡ估计性能。且该方法不影响对非相关信号源ＤＯＡ的估计,计算量也无明显增加。计算机仿真实验结果验证了上述理论分析的正确性。     

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.     

Self-Orthogonalizing Adaptive Equalization in the Discrete Frequency Domain

A self-orthogonalizing discrete adaptive equalizer for synchronous data transmission is presented, based on the overlapsave filtering technique. Self-orthogonalization in the discrete frequency domain is adaptively performed by premultiplying by a diagonal matrix the MSE gradient estimates before projecting by means of Rosen's gradient projection method. The diagonal of the matrix is the inverse of the power spectrum of the received sequence taken at equally spaced frequencies, and estimates are obtained by using Bartlett's procedure of periodograms averaging for spectrum estimation. Projection is accomplished by means of an off-line derived projection matrix. Confidence of gradient estimates is improved by means of a block correlated estimation technique using available DFT's of blocks of data. This equalizer is compared to time-domain self-orthogonalizing algorithms as regards speed of convergence and ease of implementation. During the short startup phase, convergence is competitive with that of Godard's algorithm, which is the fastest algorithm known, and in the decision-directed mode, fast convolution performed by blocks results in a considerable reduction of the number of multiplications with respect to the time domain algorithms. A computationally simpler, unconstrained startup algorithm is also examined, obtained by removing the projection while retaining gradient block estimates in the DFD.