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.     

基于混沌优化算法的MUSIC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.     

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。     

新型粒子群优化算法在多序列联配中的应用

将粒子群优化算法应用于序列联配,提出了一种改进的粒子群优化算法,该算法在粒子群的进化过程中根据粒子的适应值动态地调整粒子群的惯性权重与粒子群飞行速度范围,提高了算法的收敛速度和收敛精度;针对PSO算法可能出现的早熟现象,引入重新初始化机制,增强了算法的搜索能力,实验表明该算法是有效的。     

基于混合遗传算法的偶极子源定位的仿真计算

首先提出一种新的混合遗传算法。在基于实数编码的基础上,通过嵌入一个最速下降算子,结合遗传算法和最速下降法两者的优点,并引入模拟退火的思想,即可改善原算法的局部搜索能力,又能进一步提高优化效率。为了验证算法的可行性,通过对脑电偶极子源定位的仿真计算,证明所提出的新算法与其它优化算法相比,在达到最优解的效率上有了明显的提高。     

基于混合适传算法的偶极子源定位的仿真计算’

首先提出一种新的混合遗传算法。在基于实数编码的基础上,通过嵌入一个最速下降算子,结合遗传算法和最速下降法两者的优点,并引入模拟退火的思想,即可改善原算法的局部搜索能力,又能进一步提高优化效率。为了验证算法的可行性,通过对脑电偶极子源定位的仿真计算,证明所提出的新算法与其它优化算法相比,在达到最优解的效率上有了明显的提高。     

肿瘤亚型识别研究中智能算法的应用

目的:为解决肿瘤亚型识别过程中易出现的维数灾难和过拟合问题,提出了一种改进的粒子群BP神经网络集成算法。方法:算法采用欧式距离和互信息来初步过滤冗余基因,之后用Relief算法进一步处理,得到候选特征基因集合。采用BP神经网络作为基分类器,将特征基因提取与分类器训练相结合,改进的粒子群对其权值和阈值进行全局搜索优化。结果:当隐含层神经元个数为5时,候选特征基因个数为110时,QPSO/BP算法全局优化和搜索,此时的分类准确率最高。结论:该算法不但提高了肿瘤分型识别的准确率,而且降低了学习的复杂度。     

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     

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。     

MEG Source Localization Using Invariance of Noise Space

We propose INvariance of Noise (INN) space as a novel method for source localization of magnetoencephalography (MEG) data. The method is based on the fact that modulations of source strengths across time change the energy in signal subspace but leave the noise subspace invariant. We compare INN with classical MUSIC, RAP-MUSIC, and beamformer approaches using simulated data while varying signal-to-noise ratios as well as distance and temporal correlation between two sources. We also demonstrate the utility of INN with actual auditory evoked MEG responses in eight subjects. In all cases, INN performed well, especially when the sources were closely spaced, highly correlated, or one source was considerably stronger than the other.     

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.     

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.     

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.     

Assessment of Subcortical Source Localization Using Deep Brain Activity Imaging Model with Minimum Norm Operators: A MEG Study

Subcortical structures are involved in many healthy and pathological brain processes. It is crucial for many studies to use magnetoencephalography (MEG) to assess the ability to detect subcortical generators. This study aims to assess the source localization accuracy and to compare the characteristics of three inverse operators in the specific case of subcortical generators. MEG has a low sensitivity to subcortical sources mainly because of their distance from sensors and their complex cyto-architecture. However, we show that using a realistic anatomical and electrophysiological model of deep brain activity (DBA), the sources make measurable contributions to MEG sensors signals. Furthermore, we study the point-spread and cross-talk functions of the wMNE, sLORETA and dSPM inverse operators to characterize distortions in cortical and subcortical regions and to study how noise-normalization methods can improve or bias accuracy. We then run Monte Carlo simulations with neocortical and subcortical activations. In the case of single hippocampus patch activations, the results indicate that MEG can indeed localize the generators in the head and the body of the hippocampus with good accuracy. We then tackle the question of simultaneous cortical and subcortical activations. wMNE can detect hippocampal activations that are embedded in cortical activations that have less than double their amplitude, but it does not completely correct the bias to more superficial sources. dSPM and sLORETA can still detect hippocampal activity above this threshold, but such detection might include the creation of ghost deeper sources. Finally, using the DBA model, we showed that the detection of weak thalamic modulations of ongoing brain activity is possible.     

High-resolution magnetoencephalography--studies with a small-volume phantom]

To investigate the spatiotemporal organisation of neuronal processes in an animal model using magnetoencephalography (MEG), a high temporal resolution (ms) and an appropriate spatial resolution of about 1 mm is necessary. With the aim of determining the localization error and the resolution power of high-resolution MEG systems, we developed a phantom capable of simulating the characteristics of animal models. The phantom enables us to variably position at least two magnetic field sources to within 0.1 mm. For source localization on the basis of the magnetic field data, a spatial filtering algorithm was used. The investigation of a 16-channel micro SQUID-MEG system with a current dipole orientated tangentially to the phantom surface produced the following localization data (min ... max, x, y--horizontal plane, z--depth); systematic localization error e(x) = 1.16 ... 1.67 mm, e(y) = -1.01 ... -1.28 mm, e(z) = -5.22 ... -7.64 mm, standard deviation of the individual measurements perpendicular to the dipole axis s(perp) = 0.05 ... 0.22 mm, along this axis s(long) = 0.20 ... 1.73 mm, in the depths sz = 0.17 ... 3.17 mm. The "goodness of fit" was > 95%. Separation of two dipoles was still possible for parallel dipoles at a distance apart of d(parallel) = 0.03 mm and for those oriented perpendicularly to each other at a distance apart of d(perp) = 0.10 mm. On the basis of these results we conclude that the MEG system can achieve a resolution sufficient to permit the investigation of neuronal microstructures. The spatial errors detected were related to sensor position in the cryostatic vessel as well as to external low-frequency noise.     

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。     

Harmony: EEG/MEG Linear Inverse Source Reconstruction in the Anatomical Basis of Spherical Harmonics

EEG/MEG source localization based on a “distributed solution” is severely underdetermined, because the number of sources is much larger than the number of measurements. In particular, this makes the solution strongly affected by sensor noise. A new way to constrain the problem is presented. By using the anatomical basis of spherical harmonics (or spherical splines) instead of single dipoles the dimensionality of the inverse solution is greatly reduced without sacrificing the quality of the data fit. The smoothness of the resulting solution reduces the surface bias and scatter of the sources (incoherency) compared to the popular minimum-norm algorithms where single-dipole basis is used (MNE, depth-weighted MNE, dSPM, sLORETA, LORETA, IBF) and allows to efficiently reduce the effect of sensor noise. This approach, termed Harmony, performed well when applied to experimental data (two exemplars of early evoked potentials) and showed better localization precision and solution coherence than the other tested algorithms when applied to realistically simulated data.