Combined MEG and EEG source imaging by minimization of mutual information

Though very frequently assumed, the necessity to operate a joint processing of simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) recordings for functional brain imaging has never been clearly demonstrated. However, the very last generation of MEG instruments allows the simultaneous recording of brain magnetic fields and electrical potentials on the scalp. But the general fear regarding the fusion between MEG and EEG data is that the drawbacks from one modality will systematically spoil the performances of the other one without any consequent improvement. This is the case for instance for the estimation of deeper or radial sources with MEG. In this paper, we propose a method for a cooperative processing of MEG and EEG in a distributed source model. First, the evaluation of the respective performances of each modality for the estimation of every dipole in the source pattern is made using a conditional entropy criterion. Then, the algorithm operates a preprocessing of the MEG and EEG gain matrices which minimizes the mutual information between these two transfer functions, by a selective weighting of the MEG and EEG lead fields. This new combined EEG/MEG modality brings major improvements to the localization of active sources, together with reduced sensitivity to perturbations on data.     

Electrophysiological imaging of brain activity and connectivity-challenges and opportunities

Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging.     

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.     

Three-Dimensional Imaging of Complex Neural Activation in Humans From EEG

Electro- or magnetoencephalography (EEG/MEG) are of utmost advantage in studying transient neuronal activity and its timing with respect to behavior in the working human brain. Direct localization of the neural substrates underlying EEG/MEG is commonly achieved by modeling neuronal activity as dipoles. However, the success of neural source localization with the dipole model has only been demonstrated in relatively simple localization tasks owing to the simplified model and its insufficiency in differentiating cortical sources with different extents. It would be of great interest to image complex neural activation with multiple sources of different cortical extensions directly from EEG/MEG. We have investigated this crucial issue by adding additional parameters to the dipole model, leading to the multipole model to better represent the extended sources confined to the convoluted cortical surface. The localization of multiple cortical sources is achieved by using the subspace source localization method with the multipole model. Its performance is evaluated with simulated data as compared with the dipole model, and further illustrated with the real data obtained during visual stimulations in human subjects. The interpretation of the localization results is fully supported by our knowledge about their anatomic locations and functional magnetic resonance imaging data in the same experimental setting. Methods for estimating multiple neuronal sources at cortical areas will facilitate our ability to characterize the cortical electrical activity from simple, early sensory components to more complex networks, such as in visual, motor, and cognitive tasks.     

Spatiotemporal forward solution of the EEG and MEG using network modeling

Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixed-current dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.     

Integrated MEG/EEG and fMRI model based on neural masses

We introduce a bottom-up model for integrating electroencephalography (EEG) or magnetoencephalography (MEG) with functional magnetic resonance imaging (fMRI). An extended neural mass model is proposed based on the physiological principles of cortical minicolumns and their connections. The fMRI signal is extracted from the proposed neural mass model by introducing a relationship between the stimulus and the neural activity and using the resultant neural activity as input of the extended Balloon model. The proposed model, validated using simulations, is instrumental in evaluating the upcoming combined methods for simultaneous analysis of MEG/EEG and fMRI.     

A Bayesian approach to introducing anatomo-functional priors in theEEG/MEG inverse problem

We present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA)     

基于偏度和时序结构的声音信号盲抽取算法研究

信号盲抽取是盲信号处理领域的热点研究方向,它仅抽取感兴趣的信号,能有效减小运算量,解决盲分离中信号顺序不确定性的难题,因而在生物医学信号分析（如EEG、MEG、fMRI等）、语音和图像识别领域得到广泛应用。针对传统的基于时序结构的盲抽取算法存在较弱的抗噪性和对时延估计误差比较敏感的不足,论文提出了将偏度和时序结构相结合的信号盲抽取算法。该算法首先利用偏度的非对称性来度量分离信号的非高斯性,以减弱噪声,同时减小了传统的利用峭度度量非高斯性方法的运算量;其次利用基音周期作为声音信号的最佳时延估计,以实现对感兴趣信号的盲抽取,将两者结合后使得算法对时延估计误差不敏感,且对噪声更具鲁棒性。仿真实验部分选取了标准TIMIT语料库中一男、两女分别单独朗读同一语句的语音信号,盲抽取的实验结果表明:本文算法与文献3中算法相比具有较好的分离效果且抽取速度快,与文献4中算法相比分离效果相当但大大地提高了抽取速度,从而验证了本文算法的有效性。      

基于fMRI功能网络和贝叶斯矩阵分解的脑电源成像方法

脑电(EEG)是一种重要的脑功能成像技术,根据头皮记录的EEG信号重构皮层脑活动称为EEG源成像。然而脑源活动位置和尺寸的准确重构依然是一个挑战。为充分利用EEG和功能磁共振(fMRI)信号在时空分辨率上的互补信息,该文提出一个新的源成像方法——基于fMRI脑网络和时空约束的EEG源重构算法(FN-STCSI)。该方法在参数贝叶斯框架下,基于矩阵分解思想将源信号分解为若干时间基函数的线性组合。此外,为融合fMRI的高空间分辨率信息,FN-STCSI利用独立成分分析提取fMRI信号的功能网络,构建EEG源成像的空间协方差基,通过变分贝叶斯推断技术确定每个空间协方差基的相对贡献,实现EEG-fMRI融合。通过蒙特卡罗数值仿真和实验数据分析比较了FN-STCSI与现有算法在不同信噪比和不同先验条件下的性能,结果表明FN-STCSI能有效融合EEG-fMRI在时空上的互补信息,提高EEG弥散源成像的性能。     

Estimation of number of independent brain electric sources from the scalp EEGs

In electromagnetic source analysis, many source localization strategies require the number of sources as an input parameter (e.g., spatio-temporal dipole fitting and the multiple signal classification). In the present study, an information criterion method, in which the penalty functions are selected based on the spatio-temporal source model, has been developed to estimate the number of independent dipole sources from electromagnetic measurements such as the electroencephalogram (EEG). Computer simulations were conducted to evaluate the effects of various parameters on the estimation of the source number. A three-concentric-spheres head model was used to approximate the head volume conductor. Three kinds of typical signal sources, i.e., the damped sinusoid sources, sinusoid sources with one frequency band and sinusoid sources with two separated frequency bands, were used to simulate the oscillation characteristics of brain electric sources. The simulation results suggest that the present method can provide a good estimate of the number of independent dipole sources from the EEG measurements. In addition, the present simulation results suggest that choosing the optimal penalty function can successfully reduce the effect of noise on the estimation of number of independent sources. The present study suggests that the information criterion method may provide a useful means in estimating the number of independent brain electrical sources from EEG/MEG measurements.     

EEG/MEC error bounds for a static dipole source with a realistichead model

We derive Cramer-Rao bounds (CRBs) on the errors of estimating the parameters (location and moment) of a static current dipole source using data from electro-encephalography (EEG), magneto-encephalography (MEG), or the combined EEG/MEG modality. We use a realistic head model based on knowledge of surfaces separating tissues of different conductivities obtained from magnetic resonance (MR) or computer tomography (CT) imaging systems. The electric potentials and magnetic field components at the respective sensors are functions of the source parameters through integral equations. These potentials and field are formulated for solving them by the boundary or the finite element method (BEM or FEM) with a weighted residuals technique. We present a unified framework for the measurements computed by these methods that enables the derivation of the bounds. The resulting bounds may be used, for instance, to choose the best configuration of the sensors for a given patient and region of expected source location. Numerical results are used to demonstrate an application for showing expected accuracies in estimating the source parameters as a function of its position in the brain, based on real EEG/MEG system and MR or CT images     

Analysis of the Electromagnetic Signals of the Human Brain: Milestones, Obstacles, and Goals

Measuring the functioning of the human brain is one of the most formidable scientific/engineering endeavors ever undertaken. It is difficult to extract information about any particular processing function from brain electromagnetic signals (BEMS) since, at any instant, only a small fraction of the brain's hundreds of simultaneously active major systems might be performing processing related to the function being studied. With recent developments, a new era of research is dawning based on an interdisciplinary approach in which advanced signal processing methods are focused on increasingly more specific neuroanatomical, neurophysiological, and neuropsychological research questions and clinical applications. This brief review highlights the major accomplishments of the last several decades in human BEMS analysis and discusses obstacles to progress. Five main topics are addressed: 1) the historical problem of developing a computerized expert clinical electroencephalogram (EEG) system; 2) advances in signal processing methods, including primary analysis, feature extraction, and statistical hypothesis testing and pattern classification; 3) integrated computing systems for BEMS analysis; 4) biophysical, basic science, practical and conceptual obstacles to progress; and 5) the long-term goal of developing a device for measuring the functional integrity of major neural systems, and the related topic of neurocybernetics. Cutting-edge issues discussed include measurement and modeling of nonstationary event-related signals, characterization of spatial processes, single-trial signal detection, location of the sources of scalp-recorded field distributions, and studies of the functional significance of BEMS.     

Multi-source Neural Activity Estimation and Sensor Scheduling: Algorithms and Hardware Implementation

Electroencephalography (EEG) and magnetoencephalography (MEG) measurements are used to localize neural activity by solving the electromagnetic inverse problem. In this paper, we propose a new approach based on the particle filter implementation of the probability hypothesis density filter (PF-PHDF) to automatically estimate the unknown number of time-varying neural dipole sources and their parameters using EEG/MEG measurements. We also propose an efficient sensor scheduling algorithm to adaptively configure EEG/MEG sensors at each time step to reduce total power consumption. We demonstrate the improved performance of the proposed algorithms using simulated neural activity data. We map the algorithms onto a Xilinx Virtex-5 field-programmable gate array (FPGA) platform and show that it only takes 10 ms to process 100 data samples using 6,400 particles. Thus, the proposed system can support real-time processing of an EEG/MEG neural activity system with a sampling rate of up to 10 kHz.     

Detection and visualization method of dynamic state transition for biological spatio-temporal imaging data

In the statistical analysis of functional brain imaging data, regression analysis and cross correlation analysis between time series data on each grid point have been widely used. The results can be graphically represented as an activation map on an anatomical image, but only activation signal, whose temporal pattern resembles the predefined reference function, can be detected. In the present study, we propose a fusion method comprising innovation approach in time series analysis and statistical test. Autoregressive (AR) models were fitted to time series data of each pixel for the range sufficiently before or after the state transition. Then, the remaining time series data were filtered using these AR parameters to obtain its innovation (filter output). The proposed method could extract brain neural activation as a phase transition of dynamics in the system without employing external information such as the reference function. The activation could be detected as temporal transitions of statistical test values. We evaluated this method by applying to optical imaging data obtained from the mammalian brain and the cardiac sino-atrial node (SAN), and demonstrated that our method can precisely detect spatio-temporal activation profiles in the brain or SAN.     

Multivariate reconstruction of functional networks from cortical sources dynamics in MEG/EEG

In this paper, we present a simple method to find networks of time-correlated brain sources, using a singular value decomposition (SVD) analysis of the source matrix estimated after any linear distributed inverse problem in magnetoencephalography (MEG) and electroencephalography (EEG). Despite the high dimension of the source space, our method allows for the rapid computation of the source matrix. In order to do this, we use the linear relationship between sensors and sources, and show that the SVD can be calculated through a simple and fast computation. We show that this method allows the estimation of one or several global networks of correlated sources without calculating a coupling coefficient between all pairs of sources. A series of simulations studies were performed to estimate the efficiency of the method. In order to illustrate the validity of this approach in experimental conditions, we used real MEG data from a visual stimulation task on one test subject and estimated, in different time windows of interest, functional networks of correlated sources.     

EEG and MEG: forward solutions for inverse methods

A solution of the forward problem is an important component of any method for computing the spatio-temporal activity of the neural sources of magnetoencephalography (MEG) and electroencephalography (EEG) data. The forward problem involves computing the scalp potentials or external magnetic field at a finite set of sensor locations for a putative source configuration. We present a unified treatment of analytical and numerical solutions of the forward problem in a form suitable for use in inverse methods. This formulation is achieved through factorization of the lead field into the product of the moment of the elemental current dipole source with a "kernel matrix" that depends on the head geometry and source and sensor locations, and a "sensor matrix" that models sensor orientation and gradiometer effects in MEG and differential measurements in EEG. Using this formulation and a recently developed approximation formula for EEG, based on the "Berg parameters," we present novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG. We also present novel investigations of different boundary element methods (BEM's) and present evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods. Explicit expressions for the matrix kernels for MEG and EEG for spherical and realistic head geometries are included.     

A multiresolution framework to MEG/EEG source imaging

A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from electroencephaloram (EEG)/magnetoencephalogram (MEG) signals is proposed in a distributed source model. At each step of the algorithm, a regularized solution to the inverse problem is used to constrain the source space on the cortical surface to be scanned at higher spatial resolution. We present the iterative procedure together with an extension of the ST-maximum a posteriori method [1] that integrates spatial and temporal a priori information in an estimator of the brain electrical activity. Results from EEG in a phantom head experiment with a real human skull and from real MEG data on a healthy human subject are presented. The performances of the multiresolution method combined with a nonquadratic estimator are compared with commonly used dipolar methods, and to minimum-norm method with and without multiresolution. In all cases, the proposed approach proved to be more efficient both in terms of computational load and result quality, for the identification of sparse focal patterns of cortical current density, than the fixed scale imaging approach.     

Estimating stationary dipoles from MEG/EEG data contaminated withspatially and temporally correlated background noise

The stationary dipole model for the inverse problem of magnetoencephalographic (MEG) and electroencephalographic (EEG) data is extended by including spatio-temporal correlations of the background noise. For that purpose, the spatio-temporal covariances are described as a Kronkecker product of a spatial and a temporal covariance matrix. The maximum likelihood method is used to estimate this Kronecker product from a series of trials of MEG/EEG data. A simulation study shows that the inclusion of the background noise generally improves the dipole estimate substantially. When the frequency of the source time functions, however, coincides with the frequency contents of the covariance function, the dipole estimate worsens when the temporal correlations are included. The inclusion of spatial correlations always improves the estimates     

Bayesian estimates of error bounds for EEG source imaging

Given a set of electrical potential measurements at the surface of the head, localizing the sources of the electrical activity is an inherently ill-posed problem. Bayesian methods can be used to specify prior information to constrain the possible source solutions. We show that Bayesian analysis can also provide a means for characterizing system noise levels, estimating the "error bars" surrounding source localization results, and estimating the information about brain processes conveyed by dense sensor array electroencephalographic (EEG) recordings. This method is, in principal, applicable to any linear model of EEG or magnetoencephalographic (MEG) processes. A series of simulations demonstrated the internal consistency of our method, the robustness to noise levels, and the limitations of accurate source localization with large numbers of sources.     

基于互信息的脑网络及测谎研究

互信息分析方法是基于信息论提出的一种描述两信号间信息交互情况的算法,其在脑电信号领域的有效性已得到了充分证实.针对当前测谎方法中脑电信号特征提取困难以及大脑整体认知功能分析在脑认知科学研究中越来越被重视的情况,本文首次将互信息分析方法应用到脑电测谎领域中,使用互信息量化大脑各节点之间的相关性,对计算结果进行统计分析,选取出在两类人群中具有显著性差异的电极对的互信息作为分类特征,进行模式识别,得到了99.67%的准确率.这一结果表明,互信息分析方法是一种有效的脑功能连接分析方法,为基于脑电信号连接分析的测谎研究提供了一种新的途径.另外,对说谎与诚实两类受试者的大脑功能网络的分析结果表明：处于说谎状态时,大脑的额叶、顶叶、颞叶及枕叶之间协同实现谎言功能,并在躯体行为所对应的脑区与其他脑区的连接上也表现出相对诚实组的显著性差异,以上结果均有助于进一步揭示谎言的神经活动机制.