同步脑电-功能磁共振信号处理进展

同步脑电-功能磁共振(EEG-fMRI)整合了脑电的高时间分辨率和功能磁共振的高空间分辨率,是无创的大脑观测技术。但目前,该技术仍然存在信噪比过低、受试者舒适性差和数据融合困难等问题。本综述在介绍同步EEG-fMRI硬件系统的基础上,从EEG伪迹去除、同步EEG-fMRI信号融合、同步EEG-fMRI的应用、未来研究前景四个方面,综述该领域的最新进展,特别是在信号处理方面的新突破。本文介绍了一个对强磁场环境下脑电的信号处理的技术分级框架,为此新技术的应用分流和推广提供了重要参考。      

PEB模型在同步核磁-脑电源定位中的应用

利用脑电(Electroencephalographic,EEG)和功能性磁共振成像(functional Magnetic Resonance Imaging,fMRI)时间和空间上的互补性可以获得大脑的电源活动。为了获得经典奖惩任务中脑区的激活情况,同步采集fMRI-EEG数据,使用以fMRI空间作为约束的参数经验贝叶斯(Parametric Empirical Bayesian,PEB)模型进行了脑电源活动分析。同时,结合稀疏求解的方法,提取更集中的神经电活动,进一步突出激活强度高的脑区。实验结果表明,在奖惩结果呈现后的200～350 ms内,奖赏刺激能够诱发出反馈相关负波(Feedback Related Negativity,FRN)。fMRI空间定位显示前额叶、眶额叶等奖赏相关脑区出现激活,EEG源定位提取到了前额叶脑区的激活,但是这些激活区域均分散在脑区的各个部位。相比于单一模态fMRI空间定位和EEG源定位结果,同步源定位提取的脑区更集中,获得的模型证据也更大,更准确地描绘了脑区激活情况。     

基于贝叶斯推断的脑源定位估计技术及应用

脑电图和脑磁图信号具有无创性和高时间分辨率,能够反映快速变化的大脑神经活动.然而,由于容积传导,脑电图和脑磁图空间分辨率较低.根据头表脑电图和脑磁图估计皮层脑源活动,即脑源定位技术,能够提供具有更高空间分辨率的信息,在认知过程基本机理理解和脑损伤病理学特征分析上发挥重要的作用.本文首先介绍脑源定位技术基本概念,然后具体介绍了基于贝叶斯概率推断的脑源定位技术,将当前的脑源定位技术分为基于空间先验约束和基于时间空间先验约束两大类,分析了不同算法的特性,接着介绍了基于贝叶斯的脑源定位技术的应用领域,最后介绍脑源定位技术的未来发展趋势.     

分布式无源雷达接收机配置优化及其成像技术

由于其较低的成像成本和较强的鲁棒性,使得利用多发射机和多接收机对目标进行有效观测的分布式无源雷达成为雷达技术研究的热门领域。本文在分布式雷达稀疏成像模型基础上,提出一种分布式无源雷达成像接收机配置优化方法,以成像分辨率最高为优化目标函数,针对不同发射机布局采用遗传算法计算出最优接收机布局。同时针对正交匹配追踪（Orthogonal Matching Pursuit, OMP）算法在低信噪比下成像精度较低,信号估计不准确的情况,推导出用协方差稀疏表示接收信号,利用稀疏贝叶斯学习（Sparse Bayesian Learning, SBL）进行信号重构的成像算法,并通过仿真实验对成像性能的改善进行了验证。      

基于Bi-LSTM-Attention的癫痫发作检测方法

本文针对癫痫脑电图(EEG)信号中的发作检测问题,分析了癫痫患者EEG信号中的特异性特征,在传统EEG信号时频域基础上提出了改进的脑网络特征。本文对EEG信号进行分解,并重构了EEG信号,发现在重构信号上癫痫发作和癫痫未发作表现出较大差异。因此在重构EEG信号上通过皮尔逊系数(PCC)构建脑网络,并在该网络上提取脑网络特征,最后将这些特征输入Bi-LSTM-Attention混合网络检测癫痫发作。该网络可以筛选出对癫痫发作检测结果具有决定性因素的特征,捕捉EEG时间序列中最重要的信息。为了评估本文的方法,在公开的CHB-MIT数据集上进行实验,获得了96.20%的准确率、96.80%的特异性和95.31%的敏感性,实验结果表明该方法在癫痫发作检测这个任务上具有不错的性能。     

基于贝叶斯压缩感知的子空间拟合DOA估计方法

经典加权子空间拟合算法需进行多维非线性优化,初始参数的难以设置和较大的计算量限制了其应用。结合压缩感知理论,本文提出了一种基于改进贝叶斯压缩感知的子空间拟合DOA估计新方法。该方法首先通过低复杂度的子空间分解算法PASTd估计信号加权子空间,进而基于入射信号的空域稀疏性,将信号子空间拟合建模为多测量值稀疏重构问题,并应用贝叶斯压缩感知算法进行求解。算法在贝叶斯压缩感知的迭代求解中引入了基于相对阈值判决的基消除机制,加快收敛速度的同时避免了矩阵奇异问题。仿真结果表明本文算法在低信噪比、小快拍情况下空间分辨率优于MUSIC和l1-SVD算法,可直接用于相干源的估计,并对信源数目的估计误差具有较强鲁棒性。      

基于过完备字典稀疏表示的多通道脑电信号压缩感知联合重构

该文基于多通道脑电信号时空特性构建非正交变换过完备字典,准确稀疏表示蕴含时空相关性信息的多通道脑电信号,提高基于时空稀疏贝叶斯学习模型的多通道脑电信号压缩感知联合重构算法性能。实验选用eegmmidb脑电数据库的多通道脑电信号验证所提算法有效性。结果表明,基于过完备字典稀疏表示的多通道脑电信号,能够为多通道脑电信号压缩感知重构算法提供更多的时空相关性信息,比传统多通道脑电信号压缩感知重构算法所得的信噪比值提高近12 dB,重构时间减少0.75 s,显著提高多通道脑电信号联合重构性能。     

基于贝叶斯融合的时空流异常行为检测模型

针对直接利用卷积自编码网络未考虑视频时间信息的问题,该文提出基于贝叶斯融合的时空流异常行为检测模型。空间流模型采用卷积自编码网络对视频单帧进行重构,时间流模型采用卷积长短期记忆(LSTM)编码-解码网络对短期光流序列进行重构。接着,分别计算空间流模型和时间流模型下每帧的重构误差,设计自适应阈值对重构误差图进行二值化,并基于贝叶斯准则对空间流和时间流下的重构误差进行融合,得到融合重构误差图,并在此基础上进行异常行为判断。实验结果表明,该算法在UCSD和Avenue视频库上的检测效果优于现有异常检测算法。     

基于高斯包络线性调频基自适应分解的ISAR成像

基于高斯包络线性调频基的自适应信号分解算法具有较高的分辨率,当被分析信号(如机动目标ISAR雷达回波信号)可以由调频类信号建模时具有超强分辨能力。该文在高斯包络线性调频基的自适应信号分解算法基础上,提出了基于优化初值选择高斯包络线性调频基自适应信号分解算法,并将其应用到ISAR成像中。仿真结果表明,和传统时频分析方法相比,该算法具有更加优良的性能,对机动目标进行瞬时成像时成像质量得到了较大幅度的改善。     

基于多分辨率变换和压缩感知的肺癌PET/CT图像融合方法

针对移动医疗背景下医学图像融合信息交互的局限性问题,提出一种基于多分辨率变换NSCT和压缩感知理论的肺癌PET/CT图像融合算法.第一步,对源图像进行单层NSCT分解;第二步,通过分析PET和CT不同的成像机制和显像信息,对分解后具有较差稀疏性且主要集中源图像大部分能量的低频子带,采取高斯隶属度函数加权的融合规则,对主要呈现源图像细节信息的高频子带使用高斯随机矩阵进行压缩测量,选择基于平均梯度和区域能量的方法法对高频测量值进行融合;第三步,采取正交匹配追踪算法重构融合后的高频测量值;第四步,对低频融合图像和重构后的高频融合图像进行NSCT逆变换得到最终的融合图像;最后,对该算法进行了两方面的仿真实验:与其他压缩感知图像融合方法的比较以及与其他多分辨率图像融合方法的比较,实验结果表明,该算法是有效可行的.     

Origin of the radio frequency pulse artifact in simultaneous EEG-fMRI recording: rectification at the carbon-metal interface

Simultaneous electroencephalograph-functional magnetic resonance imaging (EEG-fMRI) recording has become an important tool for investigating spatiotemporal properties of brain events, such as epilepsy, evoked brain responses, and changes in brain rhythms. Reduction of noise in EEG signals during fMRI recording is crucial for acquiring high-quality EEG-fMRI data. The main source of the noise includes the gradient artifact, the radio frequency (RF) pulse artifact, and the cardiac pulse artifact. Since the RF pulse artifact is relatively small in amplitude, little attention has been paid to this artifact, and its origin is not well understood. However, the amplitude of the RF pulse artifact fluctuates randomly even if a very high EEG sampling rate is used, making it more salient than the gradient artifact after postprocessing for noise removal. In this paper, we investigate the cause of the RF pulse artifact in EEG systems that use carbon wires.     

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.     

Automatic classification of brain resting states using fMRI temporal signals

A novel technique is presented for the automatic discrimination between networks of `resting states? of the human brain and physiological fluctuations in functional magnetic resonance imaging (fMRI). The method is based on features identified via a statistical approach to group independent component analysis time courses, which may be extracted from fMRI data. This technique is entirely automatic and, unlike other approaches, uses temporal rather than spatial information. The method achieves 83% accuracy in the identification of resting state networks.     

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.     

Automatic identification of functional clusters in FMRI data using spatial dependence

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.     

Contextual modeling of functional MR images with conditional random fields

This paper presents a conditional random field (CRF) approach to fuse contextual dependencies in functional magnetic resonance imaging (fMRI) data for the detection of brain activation. The interactions among both activation (activated/inactive) labels and observed data of brain voxels are unified in a probabilistic framework based on the CRF, where the interaction strength can be adaptively adjusted in terms of the data similarity of neighboring sites. Compared to earlier detection methods, including statistical parametric mapping and Markov random field, the proposed method avoids the suppression of high frequency information and relaxes the strong assumption of conditional independence of observed data. Experimental results show that the proposed approach effectively integrates contextual constraints within the detection process and robustly detects brain activities from fMRI data.     

Independent Component Analysis Applied to fMRI Data: A Generative Model for Validating Results

Methods for testing and validating independent component analysis (ICA) results in fMRI are growing in importance as the popularity of this model for studying brain function increases. We introduce and apply a synthesis/analysis model for analyzing functional Magnetic Resonance Imaging (fMRI) data using ICA. Classes of signal types relevant to fMRI are described and a statistical approach for validation of simulation results is developed. Additionally, we propose an empirical version of our validation approach to test the performance of various ICA approaches in “hybrid” fMRI data, a mixture of real fMRI data and known (validatable) sources. The synthesis portion of the model assumes statistically independent spatial sources in the brain. We also assume that the fMRI scanner acquires overdetermined data such that there are more time points than hemodynamic brain sources. We propose several signal classes relevant to fMRI and discuss the properties of each. The analysis portion of the model includes several candidates for spatial smoothing, ICA algorithm, and data reduction. We use the Kullback-Leibler divergence between the estimated source distributions and the “true” distributions as a measure of the optimality of the final ICA decomposition. Using this model, we generate fMRI-like data and optimize the analysis stage as a function of ICA algorithm, data reduction scheme, and spatial smoothing. An example of how our synthesis/analysis model can be used in validating an fMRI experiment is demonstrated using simulations and “hybrid” fMRI data.     

Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection

Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge.