基于混沌优化算法的多信号分类脑磁图源定位方法

如何利用实验测得的脑磁图数据准确定位脑磁图源的真实活动位置是脑功能研究和临床应用中的一个关键问题.在脑磁活动源定位问题中,多信号分类算法是被广泛研究和采用的一类方法.为了克服多信号分类算法及其改进算法--递归多信号分类算法全局扫描时速度太慢的缺点,提出了一种基于混沌优化算法的脑磁图源定位新方法.该方法利用混沌运动遍历性的特点估计目标函数的全局最大值,进行初步的脑磁图源定位;然后,在小范围内结合网格的方法,进一步进行精确的定位.实验结果表明,此方法可实现多个脑磁图源的定位,并且定位速度大大加快,同时又能达到所要求的定位精度.     

基于粒子群优化算法的脑磁图源定位

脑磁图作为一种新型的脑探测技术,具有较高定位精度和毫秒级时间分辨率的特点。快速准确地利用脑磁图技术对三维空间中的脑神经活动源进行定位,对于脑功能研究和医学临床应用都具有重要的应用价值。可是,目前的脑磁图源定位广泛采用了多信号分类方法,它要求对三维大脑空间进行全局扫描,需要大量的计算,存在速度慢的缺点。针对这一问题,提出了一种基于粒子群优化算法的脑磁图源定位方法。先利用粒子群优化算法全局搜索能力强的特点寻找出目标函数的全局最优值,进行初步的脑磁图源定位;然后,再在小范围内进行小网格的搜索,进一步实现精确的定位。实验结果表明,基于粒子群优化算法的脑磁图源定位能够很好地解决上述问题,具有计算速度快、定位精度高的特点。     

基于独立元分析的脑磁图源定位

在脑磁图源定位问题中,通常感兴趣的是脑内众多神经活动源中的一个或几个,而传统源定位方法,如多信号分类方法,需要将所有源的位置都确定后,通过重组信号波形才能获得所感兴趣源的位置信息。为了提高定位效率,文章作者提出了一种结合独立元分析的脑磁图源定位方法。实验结果表明,该方法能加快定位的速度,同时能够在一定程度上克服噪声的影响,具有更强的抗噪声能力。     

脑磁图神经活动源数目的估计

在脑磁图信号的分析中,正确估计出脑磁图神经活动源的数目是进一步分析脑磁图信号的前提。目前广泛采用的信息论方法和主成分分析方法都是根据特征值来确定源的数目,这两种方法在源数目较多、噪声较强的情况下,会导致误判。该文提出了一种噪声调节自动阈值的脑磁图源数目判断方法,利用基于噪声调节的主成分分析并结合聂曼- 皮尔逊准则对脑磁图源数目进行估计。同时,该方法采用了基于小波的噪声方差估计,实现了脑磁图信号中噪声方差的精确估计。通过对基于信息论方法、主成分分析方法以及该文所提议方法的实验结果的比较,表明该文所提议方法能更准确地估计脑磁图源数目,特别是在源数目较多、信噪比较小的情况下,仍能准确地估计脑磁图源数目,具有较大的实际意义。     

脑磁源的定位研究

在脑电脑磁研究中,常将大脑视为一电磁系统,利用准静态近似下的麦克斯韦方程,可以发现代表脑神经元活动的原在电流密度与脑外磁场之间呈线性关系。在高斯哭喊杨存在的情况下,采用极大似然估计理论,讨论了一种基于脑磁场时域－罕注解磁源定位问题的一般方法。球对称导体模型下的模拟计算表明,这一方法是有效的。对于考虑真实头模型下的磁源定位问题求解磁源定位,提出了一种联合使用脑磁脑电数据的近似方法。     

脑磁图的临床应用研究

脑磁图 (magnetoencephalogragphy,MEG) ,是一种通过测量脑磁场信号 ,对脑功能区进行定位及评价其状态的新技术 ,具有对人体无侵袭、无损伤等特点 ,目前已在人脑的功能研究和临床上进行应用。脑磁图临床的应用研究1　MEG的基础研究MEG可用于听觉、视觉、语言、运动、脑细胞信息     

脑源定位技术的精度评估及其在实际中的应用

脑源定位技术旨在通过头皮表面的脑电、脑磁信号来识别大脑内的神经活动源，是研究大脑皮层神经活动、认知过程和病理功能的基础。其毫秒级的时间分辨率可以有效弥补功能核磁共振在低时间分辨率方面的不足。然而，理论分析层面中逆问题的不适定性，以及实践操作层面上不同的记录方式、电极数量和头模型构建等过程带来的误差，给脑源定位的准确性带来极大挑战，也在一定程度上限制了脑源定位方法在神经科学和心理学研究以及临床诊断治疗中的实际应用。因此，理论分析和实践操作层面中的精度评估在脑源定位方法的实际使用中至关重要。针对以上问题，本文在对现有脑源定位方法介绍的基础上，着重分析了脑源定位技术的精度评估方法以及其在基础研究和临床诊断治疗中的实际应用。具体地，本文在理论分析中总结了基于空间分辨率、基于点扩散以及串扰函数的评估方法对于不同脑源定位方法中源的重叠程度和其他源对目标源的影响；在实践操作中介绍了记录方式、电极数量和密度、头部容积传导模型等因素对源定位精度的影响；进一步介绍了脑源定位技术在时频分析、连通性分析中的应用，以及其在临床中的应用，包括癫痫、注意缺陷与多动障碍等脑部疾病。     

脑磁图的临床应用研究

脑磁图(magnetoencephalogragphy,MEG),是一种通过测量脑磁场信号,对脑功能区进行定位及评价其状态的新技术,具有对人体无侵袭、无损伤等特点,目前已在人脑的功能研究和临床上进行应用。     

基于模拟退火法由脑磁图推测电流偶极子参数

利用模拟退火（Ｓｉｍｕｌａｔｅｄ Ａｎｎｅａｌｉｎｇ） 算法,由脑磁图（ ＭＥＧ） 数据反演脑内作为磁源的单电流偶极子参数,可以得到理想的结果。在上述工作的基础上,对脑内多电流偶极子参数的反演,则呈现如下状况：即以少于实际源数目的偶极子为源假设反演,目标函数得不到极小优化。反之,目标函数可以得到极小优化, 但出现多余的伪偶极子, 且这些伪偶极子在多次不同条件的反演结果中,处于不稳定状态。若将多次反演结果中处于不稳定状态的偶极子作为伪偶极子的判据而将其排除,则可以得到一种判断磁源偶极子数目的方法     

小波和主分量分析方法研究思维脑电

研究自发脑电和思维活动的关系．利用小波和主分量分析结合的WPCA算法对不同思维任务记录的六导脑电进行处理,并对思维特征的频谱能量和变化率等多指标进行综合分析和计算。结果表明WPCA算法不仅可以实现噪声的去除,而且能提高主分量的贡献率,降低输入矢量的维数。对脑电主分量的分析揭示了脑电与思维个体、思维种类、复杂度以及注意力的联系,思维任务的神经网络分类结果验证了WPCA方法研究脑电和思维的有效性,为进一步理解认知和思维过程,实现对思维的定位和分类提供了依据。     

Application of decorrelation-independent component analysis to biomagnetic multi-channel measurements.

Biomagnetic multi-channel recordings are typically a superposition of signals from several biological sources of interest and from biological and technical noise sources. Besides averaging, source localization, and spectral analysis to name only a few methods, independent component analysis is an established tool to resolve the superposition present in raw biomagnetic data on a purely statistical basis. Here the time-delayed decorrelation-independent component analysis algorithm is applied to exemplary magnetocardiographic and magnetoencephalographic data and the successful signal separation is demonstrated.     

Seizure source localization using a hybrid second order blind identification and extended rival penalized competitive learning algorithm

Localization of seizure sources prior to neurosurgery is crucial. In this paper, a new method is proposed to localize the seizure sources from multi-channel electroencephalogram (EEG) signals. Blind source separation based on second order blind identification (SOBI) is primarily applied to estimate the brain source signals in each window of the EEG signals. A new clustering method based on rival penalized competitive learning (RPCL) is then developed to cluster the rows of the estimated unmixing matrices in all the windows. The algorithm also includes pre and post-processing stages. By multiplying each cluster center to the EEG signals, the brain signal sources are approximated. According to a complexity value measure, the main seizure source signal is separated from the others. This signal is projected back to the electrodes’ space and is subjected to the dipole source localization using a single dipole model. The simulation results verify the accuracy of the system. In addition, correct localization of the seizure source is consistent with the clinical tests derived using the simultaneous intracranial recordings.     

Electro/magnetoencephalography beamforming with spatial restrictions based on sparsity

We present a source localization method for electroencephalographic (EEG) and magnetoencephalographic (MEG) data which is based on an estimate of the sparsity obtained through the eigencanceler (EIG), which is a spatial filter whose weights are constrained to lie in the noise subspace. The EIG provides rejection of directional interferences while minimizing noise contributions and maintaining specified beam pattern constraints. In our case, the EIG is used to estimate the sparsity of the signal as a function of the position, then we use this information to spatially restrict the neural sources to locations out of the sparsity maxima. As proof of the concept, we incorporate this restriction in the “classical” linearly constrained minimum variance (LCMV) source localization approach in order to enhance its performance. We present numerical examples to evaluate the proposed method using realistically simulated EEG/MEG data for different signal-to-noise (SNR) conditions and various levels of correlation between sources, as well as real EEG/MEG measurements of median nerve stimulation. Our results show that the proposed method has the potential of reducing the bias on the search of neural sources in the classical approach, as well as making it more effective in localizing correlated sources.     

Real-Time Localization of Moving Dipole Sources for Tracking Multiple Free-Swimming Weakly Electric Fish

In order to survive, animals must quickly and accurately locate prey, predators, and conspecifics using the signals they generate. The signal source location can be estimated using multiple detectors and the inverse relationship between the received signal intensity (RSI) and the distance, but difficulty of the source localization increases if there is an additional dependence on the orientation of a signal source. In such cases, the signal source could be approximated as an ideal dipole for simplification. Based on a theoretical model, the RSI can be directly predicted from a known dipole location; but estimating a dipole location from RSIs has no direct analytical solution. Here, we propose an efficient solution to the dipole localization problem by using a lookup table (LUT) to store RSIs predicted by our theoretically derived dipole model at many possible dipole positions and orientations. For a given set of RSIs measured at multiple detectors, our algorithm found a dipole location having the closest matching normalized RSIs from the LUT, and further refined the location at higher resolution. Studying the natural behavior of weakly electric fish (WEF) requires efficiently computing their location and the temporal pattern of their electric signals over extended periods. Our dipole localization method was successfully applied to track single or multiple freely swimming WEF in shallow water in real-time, as each fish could be closely approximated by an ideal current dipole in two dimensions. Our optimized search algorithm found the animal’s positions, orientations, and tail-bending angles quickly and accurately under various conditions, without the need for calibrating individual-specific parameters. Our dipole localization method is directly applicable to studying the role of active sensing during spatial navigation, or social interactions between multiple WEF. Furthermore, our method could be extended to other application areas involving dipole source localization.     

Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.     

Quality assurance procedure for a gamma guided stereotactic breast biopsy system

A quality assurance procedure has been developed for a prototype gamma-ray guided stereotactic biopsy system. The system consists of a compact small-field-of-view gamma-ray camera mounted to the rotational arm of a Lorad stereotactic biopsy system. The small-field-of-view gamma-ray camera has been developed for clinical applications where mammographic X-ray localization is not possible. Marker sources that can be imaged with the gamma-camera have been designed and built for quality assurance testing and to provide a fiducial reference mark. An algorithm for determining the three dimensional location of a region of interest, such as a lesion, relative to the fiducial mark has been implemented into the software control of the camera. This system can be used to determine the three-dimensional location of a region of interest from a stereo pair of images and that information can be used to guide a biopsy needle to that site. Point source phantom tests performed with the system have demonstrated that the camera can be used to localize a point of interest to within 1 mm, which is satisfactory for its use in needle localization.     

Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

Background

Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized.

Methods

EEG data were generated by simulating multiple cortical sources (2–4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated.

Results

While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms.