Brain source localization using reduced EEG sensors

Brain source activation is caused due to certain mental or physical task, and such activation is localized by using various optimization techniques. This localization has vital application for diagnoses of various brain disorders such as epilepsy, schizophrenia, Alzheimer, depression, Parkinson and stress. Various neuroimaging techniques (such as EEG, fMRI, MEG) are used to record brain activity for inference and estimation of active source locations. EEG employs set of sensors which are placed on scalp to measure electric potentials. These sensors have significant role in overall system complexity, computational time and system cost. Hence, sensor reduction for EEG source localization has been a topic of interest for researchers to develop a system with improved localization precision, less system complexity and reduced cost. This research work discusses and implements the brain source localization for real-time and synthetically generated EEG dataset with reduced number of sensors. For this, various optimization algorithms are used which include Bayesian framework-based multiple sparse priors (MSP), classical low-resolution brain electromagnetic tomography (LORETA), beamformer and minimum norm estimation (MNE). The results obtained are then compared in terms of negative variational free energy, localization error and computational time measured in seconds. It is observed that multiple sparse priors (MSP) with increased number of patches performed best even with reduced number of sensors, i.e., 7 instead of 74. The results are shown valid for synthetic EEG data at low SNR level, i.e., 5 dB and real-time EEG data, respectively.     

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.     

Efficient localization of synchronous EEG source activities using a modified RAP-MUSIC algorithm

Synchronization across different brain regions is suggested to be a possible mechanism for functional integration. Noninvasive analysis of the synchronization among cortical areas is possible if the electrical sources can be estimated by solving the electroencephalography inverse problem. Among various inverse algorithms, spatio-temporal dipole fitting methods such as RAP-MUSIC and R-MUSIC have demonstrated superior ability in the localization of a restricted number of independent sources, and also have the ability to reliably reproduce temporal waveforms. However, these algorithms experience difficulty in reconstructing multiple correlated sources. Accurate reconstruction of correlated brain activities is critical in synchronization analysis. In this study, we modified the well-known inverse algorithm RAP-MUSIC to a multistage process which analyzes the correlation of candidate sources and searches for independent topographies (ITs) among precorrelated groups. Comparative studies were carried out on both simulated data and clinical seizure data. The results demonstrated superior performance with the modified algorithm compared to the original RAP-MUSIC in recovering synchronous sources and localizing the epileptiform activity. The modified RAP-MUSIC algorithm, thus, has potential in neurological applications involving significant synchronous brain activities.     

MEG and EEG source localization in beamspace

Beamspace methods are applied to EEG/MEG source localization problems in this paper. Beamspace processing involves passing the data through a linear transformation that reduces the data dimension prior to applying a desired statistical signal processing algorithm. This process generally reduces the data requirements of the subsequent algorithm. We present one approach for designing beamspace transformations that are optimized to preserve source activity located within a given region of interest and show that substantial reductions in dimension are obtained with negligible signal loss. Beamspace versions of maximum likelihood dipole fitting, MUSIC, and minimum variance beamforming source localization algorithms are presented. The performance improvement offered by the beamspace approach with limited data is demonstrated by bootstrapping somatosensory data to evaluate the variability of the source location estimates obtained with each algorithm. The quantitative benefits of beamspace processing depend on the algorithm, signal to noise ratio, and amount of data. Dramatic performance improvements are obtained in scenarios with low signal to noise ratio and a small number of independent data samples.     

Statistical maps for EEG dipolar source localization

We present a method that estimates three-dimensional statistical maps for electroencephalogram (EEG) source localization. The maps assess the likelihood that a point in the brain contains a dipolar source, under the hypothesis of one, two or three activated sources. This is achieved by examining all combinations of one to three dipoles on a coarse grid and attributing to each combination a score based on an F statistic. The probability density function of the statistic under the null hypothesis is estimated nonparametrically, using bootstrap resampling. A theoretical F distribution is then fitted to the empirical distribution in order to allow correction for multiple comparisons. The maps allow for the systematic exploration of the solution space for dipolar sources. They permit to test whether the data support a given solution. They do not rely on the assumption of uncorrelated source time courses. They can be compared to other statistical parametric maps such as those used in functional magnetic resonance imaging (fMRI). Results are presented for both simulated and real data. The maps were compared with LORETA and MUSIC results. For the real data consisting of an average of epileptic spikes, we observed good agreement between the EEG statistical maps, intracranial EEG recordings, and fMRI activations.     

Multiple current dipole estimation using simulated annealing

A method for estimating electrical current distribution in the human brain using a multiple current dipole model is presented. A cost function for estimating multiple dipoles is proposed and a simulated annealing algorithm is used to obtain an acceptable solution. Computer simulation is used to evaluate the effectiveness of this method     

Fast antenna tuning using transputer based simulated annealing

A method of applying simulated annealing to the control of an antenna tuning unit (ATU) is presented. This includes implementation of the algorithm on a transputer architecture and a diagnosis procedure to calculate the load impedance in a timely fashion and to allow the system to adapt to changes in the environment     

Restoration of polarimetric SAR images using simulated annealing

Filtering synthetic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the images while preserving the structures of the nondistributed targets. However, these objectives are normally conflicting, often leading to a filtering approach favoring one of the objectives. An algorithm for estimating the radar cross-section (RCS) for intensity SAR images has previously been proposed in the literature based on Markov random fields and the stochastic optimization method simulated annealing. A new version of the algorithm is presented applicable to multilook polarimetric SAR images, resulting in an estimate of the mean covariance matrix rather than the RCS. Small windows are applied in the filtering, and due to the iterative nature of the approach, reasonable estimates of the polarimetric quantities characterizing the distributed targets are obtained while at the same time preserving most of the structures in the image. The algorithm is evaluated using multilook polarimetric L-band data from the Danish airborne EMISAR system, and the impact of the algorithm on the unsupervised H-α classification is demonstrated     

Channel assignment for cellular radio using simulated annealing

The channel assignment problem, i.e. the task of assigning the channels to the radio base stations in a spectrum-efficient way, is an NP-complete optimization problem occurring during design of cellular radio systems. Previously, this problem has been solved by graph coloring algorithms. An alternative approach is presented. The problem is solved using simulated annealing, which is a general approach to combinatorial optimization. The algorithm has been successfully applied to practical radio network planning situations. One major benefit of the approach consists in the enhanced flexibility it gives to the engineer     

Reliability optimization of communication networks using simulated annealing

Two concepts of communication network reliability are considered. The first one, the ‘s-t’ reliability, is relevant for communication between a source station and a terminal station as in the case of a two way telephone communication. The second one, the overall reliability, is a measure of simultaneous connectedness among all stations in the network. An algorthm is presented which selects the optimal set of links that maximizes the overall reliability of the network subject to a cost restriction, given the allowable node-link incidences, the link costs and the link reliabilities. The algorithm employs a variaton of the simulated annealing approach coupled with a hierarchical strategy to achieve the gobal optimum. For complex networks, the present algorithm is advantageous over the traditional heuristic procedures. The solutions of two representative example network optimization problems are presented to illustrate the present algorithm. The potential utilization of parallel computing strategies in the present algorithm is also identified.     

基于循环差集与模拟退火法的阵列综合

介绍了循环差集的基本原理,提出将循环差集与模拟退火法相结合的阵列综合方法.利用循环差集综合稀疏程度较高的非均匀线性阵列以及可双频工作的孔径共享线性阵列,得到的阵列方向图的旁瓣电平较低,且服从均匀分布.利用模拟退火法对由循环差集综合得到的稀疏阵列的电流幅度分布进行优化,可以使阵列方向图的旁瓣电平进一步降低,同时优化后的馈电电流幅值比较低.仿真结果表明,该方法是一种有效的阵列综合方法.     

Optimal design for broadband quasi-phase-matched second-harmonicgeneration using simulated annealing

We propose a modulation-optimization method for achieving broadband quasi-phase matching in second-harmonic generation devices with domain-inverted structures. Using simulated annealing, we optimize phase-matching curves tailored to practical purposes by appropriately choosing a cost function in the annealing process. Both the acceptance bandwidth and the conversion efficiency are significantly improved in comparison with conventional periodically domain-inverted devices     

Distributed source localization using ESPRIT algorithm

A new algorithm based on ESPRIT is proposed for the estimation of the central angle and angular extension of distributed sources. The central angles are estimated using TLS-ESPRIT for both incoherently distributed (ID) and coherently distributed (CD) sources. For CD sources, the extension width is estimated by constructing a one-dimensional (1-D) distributed source parameter estimator (DSPE) spectrum for each source. For ID sources, the extension widths are estimated using the central moments of the distribution. The algorithm can be used for sources with different angular distributions     

Recursive MUSIC: A framework for EEG and MEG source localization

The multiple signal classification (MUSIC) algorithm can be used to locate multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetocncephalography (MEG) data. The algorithm scans a single-dipole model through a three-dimensional (3-D) head volume and computes projections onto an estimated signal subspace. To locate the sources, the user must search the head volume for multiple local peaks in the projection metric. This task is time consuming and subjective. Here, the authors describe an extension of this approach which they refer to as recursive MUSIC (R-MUSIC). This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections. The new method is also able to locate synchronous sources through the use of a spatio-temporal independent topographies (IT) model. This model defines a source as one or more nonrotating dipoles with a single time course. Within this framework, the authors are able to locate fixed, rotating, and synchronous dipoles. The recursive subspace projection procedure that they introduce here uses the metric of canonical or subspace correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace, by recursively computing subspace correlations, the authors build up a model for the sources which account for a given set of data. They demonstrate here how R-MUSIC can easily extract multiple asynchronous dipolar sources that are difficult to find using the original MUSIC scan. The authors then demonstrate R-MUSIC applied to the more general IT model and show results for combinations of fixed, rotating, and synchronous dipoles     

EEG localization accuracy improvements using realistically shapedhead models

A model of the head must be used in making estimates of the locations of electrical sources in the brain using electroencephalograms (EEGs) measured on the scalp. In part, the accuracy of these estimates is dependent on how accurately the model represents the actual head. In most work performed to date, spherical models of the head have been used. This paper presents results in which the estimates of source location are made in realistically shaped head models. Techniques for accurately and conveniently developing realistically shaped head models from CTs, MRIs, X-rays, and/or physical measurements are also presented. Realistically shaped head models are developed for three subjects with electrical sources implanted at known locations in the brain. Localization accuracy is found to be significantly better in the realistically shaped bead models than in spherical models if EEGs with good signal-to-noise ratio are used     

应用模拟退火算法建立共面波导结构的等效电路

对中心导带和地渐变的共面波导结构提出了一种简单通用的方法,建立了其等效电路,通过模拟退火算法优化提取了电路参数.将这种方法应用于分析两种周期性共面波导结构,比较了由等效电路模型与实验得出的散射参数,得到了很好的一致性.     

Teaching network connectivity using simulated annealing on amassively parallel processor

A simulated annealing technique for automatically training a machine vision system to recognize and locate complex objects is described. In this method, the training is used to find an optimum connectivity pattern of a fixed number of inputs that have fixed weights, rather than the usual technique of finding the optimum weights for a fixed connectivity. The recognition model uses a two-layer artificial neural network, where the first layer consists of image edge vectors in four directions. Each neuron in the second layer has a fixed number of connections that connect only to those first layer edges that are best for distinguishing the object from a confusing background. Simulated annealing is used to find the best parameters for defining edges in the first layer, as well as the pattern of connections from the first to the second layer. Weights of the connections are either plus or minus one, so that multiplications are avoided, and the system speed is considerably enhanced. In industrial applications on a low-cost parallel SIMD (single instruction multiple data) architecture, objects can be trained by an unskilled user in less than 1 min, and after training, parts can be located in about 100 ms. This method has been found to work very well on integrated circuit patterns     

Image reconstruction using interval simulated annealing in electrical impedance tomography

Electrical impedance tomography (EIT) is an imaging technique that attempts to reconstruct the impedance distribution inside an object from the impedance between electrodes placed on the object surface. The EIT reconstruction problem can be approached as a nonlinear nonconvex optimization problem in which one tries to maximize the matching between a simulated impedance problem and the observed data. This nonlinear optimization problem is often ill-posed, and not very suited to methods that evaluate derivatives of the objective function. It may be approached by simulated annealing (SA), but at a large computational cost due to the expensive evaluation process of the objective function, which involves a full simulation of the impedance problem at each iteration. A variation of SA is proposed in which the objective function is evaluated only partially, while ensuring boundaries on the behavior of the modified algorithm.     

Efficient wideband source localization using beamforming invariancetechnique

A novel scheme for wideband direction-of-arrival (DOA) estimation is proposed. The technique performs coherent signal subspace transformation by a set of judiciously constructed beamforming matrices. The beamformers are chosen to transform each of the narrowband array manifold vectors into the one corresponding to the reference frequency, regardless of the actual spatial distribution of the sources. The focused data correlation matrix can thus be obtained without any preliminary DOA estimation or iteration. A simplified version of the beamspace Root-MUSIC algorithm is developed and used in conjunction with the proposed method to efficiently localize multiple wideband sources with a linear, equally spaced array. Numerical simulations are conducted to demonstrate the efficacy of the new scheme     

利用遗传模拟退火算法解决分布式OS-CFAR检测的优化问题

利用遗传模拟退火算法(GSAAs)对分布式有序统计恒虚警检测(OS-CFAR)系统的k,T参数与融合规则进行了优化设计,给出了典型的3传感器在一致与非一致检测条件下的一组准最优搜索结果。分析表明 GSAAs对于该问题的优化具有良好的适应性。