共查询到19条相似文献,搜索用时 281 毫秒
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将经典的多信号分类算法(MUSIC)用于研究脑电逆问题时存在两个问题:对有色噪音敏感和不能识别相干源.近年人们提出了利用延时相关、高阶累积量或假设已知噪音协方差来缓解有色噪音对算法的影响.对于相干源,则有人提出了递归的多维MUSIC方法.本文在这些工作的基础上建立了一种基于延时相关阵的、叠代的多维MUSIC算法.仿真数据及实际脑电应用研究表明,该方法能在压制有色噪音的同时识别多个相干源,因而具有明显的意义. 相似文献
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针对MUSIC算法运算量大,无法分辨相干源的情况,文中提出了基于共轭数据重排的快速MUSIC算法.该算法利用数据的共轭重排在不损失阵列有效孔径的情形下可以对信源解相干,然后通过阵列协方差矩阵的一个子阵来得到信号子空间,避免了特征分解,获得了MUSIC算法的快速实现.计算机仿真结果证实了该算法的正确性和有效性. 相似文献
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《现代电子技术》2020,(6):156-159
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。 相似文献
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波达方向估计是智能天线的重要功能之一,对于相干信号角度模糊问题通常严重影响了方向判断的精准度.文中根据一维空间平滑基本理论和算法,对二维六边形阵列相干信号MUSIC算法波达方向估计问题进行了理论分析和仿真实验,提出了平面六边形阵列到十字形阵列的变换方法,成功地把一维平滑技术应用到平面六边形阵列,仿真表明,这种变换可以有效地消除相干信号的角度模糊问题. 相似文献
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相干源的存在使矢量阵的高分辨方位估计性能严重恶化。分析了单矢量传感器的阵列流型,结合空间平滑算法的应用条件,给出了基于二元矢量阵的最小方差无失真响应(MVDR)和多重信号分类(MUSIC)解相干算法,研究了算法的性能。理论分析和仿真实验证明该算法可实现对相干源的到达方向(DOA)估计,在相同信噪比条件下,相较于MVDR算法、MUSIC算法具有更好的方位分辨力,受到相干源之间方位夹角的影响更小,而且对相干源之间的强度差异不敏感。 相似文献
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针对稀疏圆阵的波达方向估计问题,提出了解相干求根MUSIC算法(Sparse UCA Decorrelation Root-MUSIC,SDR)。通过改进传统的波束变换方法,进行相位校正,并在波束域进行误差补偿,得到具有共轭对称结构的波束域导向矢量。在波束域进行前后向平均处理和使用求根MUSIC算法,实现多组相干源的解相干,且避免了谱搜索,减少了运算量。平均处理增加了数据量,算法在低信噪比和低快拍数情况下有更好的估计性能。计算机仿真表明,本算法适用于稀疏圆阵对相干源的DOA估计而且有较好的估计性能。 相似文献
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Bénar CG Gunn RN Grova C Champagne B Gotman J 《IEEE transactions on bio-medical engineering》2005,52(3):401-413
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. 相似文献
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David O Garnero L Cosmelli D Varela FJ 《IEEE transactions on bio-medical engineering》2002,49(9):975-987
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. 相似文献
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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. 相似文献
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Baillet S Garnero L Marin G Hugonin JP 《IEEE transactions on bio-medical engineering》1999,46(5):522-534
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. 相似文献
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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. 相似文献
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Liu H Gao X Schimpf PH Yang F Gao S 《IEEE transactions on bio-medical engineering》2004,51(10):1794-1802
Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods. 相似文献
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Corsini J Shoker L Sanei S Alarcón G 《IEEE transactions on bio-medical engineering》2006,53(5):790-799
Most of the methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These recordings require intensive surgical operations to implant the electrodes within the brain which are hazardous to the patient. Here, we have developed a novel approach to quantify the dynamical changes of the brain using the scalp EEG. The scalp signals are preprocessed by means of an effective block-based blind source separation (BSS) technique to separate the underlying sources within the brain. The algorithm significantly removes the effect of eye blinking artifacts. An overlap window procedure has been incorporated in order to mitigate the inherent permutation problem of BSS and maintain the continuity of the estimated sources. Chaotic behavior of the underlying sources has then been evaluated by measuring the largest Lyapunov exponent. For our experiments, we provided twenty sets of simultaneous intracranial and scalp EEG recordings from twenty patients. The above recordings have been compared. Similar results were obtained when the intracranial electrodes recorded the electrical activity of the epileptic focus. Our preliminary results show a great improvement when the epileptic focus is not captured by the intracranial electrodes. 相似文献
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Many tomographic source localization algorithms used in biomagnetic imaging assume, explicitly or sometimes implicitly, that the source activity at different brain locations are either independent or that the correlation structure between sources is known. Among these algorithms is a class of adaptive spatial filters known as beamformers, which have superior spatiotemporal resolution abilities. The performance of beamformers is robust to weakly coherent sources. However, these algorithms are extremely sensitive to the presence of strongly coherent sources. A frequent mode of failure in beamformers occurs with reconstruction of auditory evoked fields (AEFs), in which bilateral auditory cortices are highly coherent in their activation. Here, we present a novel beamformer that suppresses activation from regions with interfering coherent sources. First, a volume containing the interfering sources is defined. The lead field matrix for this volume is computed and reduced into a few significant columns using singular value decomposition (SVD). A vector beamformer is then constructed by rejecting the contribution of sources in the suppression region while allowing for source reconstruction at other specified regions. Performance of this algorithm was first validated with simulated data. Subsequent tests of this modified beamformer were performed on bilateral AEF data. An unmodified vector beamformer using whole head coverage misplaces the source medially. After defining a suppression region containing the temporal cortex on one side, the described method consistently results in clear focal activations at expected regions of the contralateral superior temporal plane. 相似文献