独立分量分析的拟牛顿迭代算法及其在视觉诱发电位特征提取中的应用

少次叠加平均处理后的视觉诱发电位（VEP）中仍含有一定的背景噪声．对其进行进一步的提取与处理有重要的实用价值。独立分量分析（ICA）能够从混合信号中分离出最独立的成分，有效抑制噪声。本文尝试采用ICA的拟牛顿迭代算法进行VEP特征提取，介绍该方法的原理、实验和结果，并与采用牛顿迭代准则的快速独立孕量分析（Fast ICA）算法进行了比较。结果表明，基于拟牛顿法的ICA可以有效增强信号，从少次叠加平均的结果中提取出易于辨识的VEP的P300信号，具有较高的应用价值。     

基于独立分量分析的脑电噪声消除

作为一种新的多元统计处理方法，独立分量分析(ICA)是解决盲源分离(BSS)问题的一个有效手段。在简要分析ICA理论及其算法的基础上，提出将其应用到脑电中的眼电伪迹的去除任务。实际采集的生理信号大多由相互独立的成分线性迭加而成，符合ICA要求源信号统计独立的基本假设。与传统方法相比，ICA这种空间滤波器不受信号频谱混迭的限制，消噪的同时能对有用信号的细节成分做到很好的保留，很大程度上弥补了时频域方法的不足。此外解混矩阵的逆可以用来反映独立源的空间分布模式，具有重要的生理意义。     

快速独立分量分析及其在脑电信号处理中的应用

独立分量分析(Independent Component Analysis, ICA)是一种基于信号统计特性的盲源分离方法,由于其分离的信号之间是互相独立的,所以在生物电信号去除干扰和伪迹、信号分离以及特征提取等方面有很大的潜在价值.本文提出了一种改进的快速ICA方法,提高了收敛速度.通过仿真,证明这种方法的优越性.最后利用该方法去除脑电中眼动伪迹,达到了较好的效果.     

独立分量分析及其在脑电逆问题中的应用

由大脑头皮电压推断大脑内活动源的信息，称之为脑电逆问题。脑电逆问题的解决对于脑认识功能的研究有重要的科学意义和临床应用价值。本首先对脑电逆问题及其主要解决方法作了简要介绍，然后介绍了独立分量分析(ICA)的工作原理、算法及其在脑电逆问题中的应用，分析了尚未解决的问题，提出ICA是一个在脑电逆问题中值得注意的研究方向。     

独立分量分析及其在脑电信号预处理中的应用

作为盲源分离（blind source separation,BSS)的一种新的方法。独立分量分析（independent component analysis,ICA)受到国内外信息处理领域科技工作者的广泛关注,本文简要介绍了独立分量分析的基本思想及算法。并将独立分量分析用于脑电信号的预处理中,成功的分离出脑电信号中的心电干扰。     

基于独立分量分析的脑电中眼电伪迹消除

利用独立分最分析的方法对脑电中眼电伪迹成分进行剔除。针对扩腮熵最大算法能够同时分离超高斯和亚高斯信号的特点，将脑电信号分解成独立分量，利用伪迹脑地形图的特征，将伪迹分最分离，得到不含伪迹的脑电信号。实验结果表明。该算法具有较强的稳健性和实用性。     

混合遗传算法结合时空模型评估脑电多源定位问题

脑电源定位可以归结为优化问题，非线性优化方法是解决这一问题的有力工具。本研究在时空模型的基础上，引入遗传算法，并将遗传算法与局域算法结合起来构成混合遗传算法，用来评估多偶极子源的定位问题。仿真结果表明，在多偶极子定位问题上，遗传算法和混合遗传算法的运算性能要优于传统的非线性局域优化方法，当偶极子数目为2时，混合遗传算法的定位结果要好于遗传算法的定位结果；当偶极子数目为3时，遗传算法与混合遗传算法的运算性能相差不大。     

独立分量分析及其在脑电逆问题中的应用

由大脑头皮电压推断大脑内活动源的信息,称之为脑电逆问题.脑电逆问题的解决对于脑认识功能的研究有重要的科学意义和临床应用价值.本文首先对脑电逆问题及其主要解决方法作了简要介绍,然后介绍了独立分量分析(ICA)的工作原理、算法及其在脑电逆问题中的应用,分析了尚未解决的问题,提出ICA是一个在脑电逆问题中值得注意的研究方向.     

运动意识脑电的动态独立分量分析

研究了用独立分量分析方法进行运动意识脑电信号特征分析的可行性。提出了用峭度极大动态独立分量分析方法进行μ节律提取的新思想。通过对批处理ICA算法和动态ICA算法在运动意识脑电特征分析的结果比较,得出了动态ICA算法更适合于运动意识脑电特征分析和提取。研究中发现,动态ICA混合矩阵系数的时间波形能准确即时地反映受试者进行左右手运动想象时运动神经皮层的μ节律变化,这一结果对脑认知和脑—机接口研究具有较大的实际意义,为独立分量分析方法在事件相关电位(ERP)特征提取中的应用提供了新的思路。     

独立分量分析在脑电信号处理中的应用及研究进展

独立分量分析(independent component analysis,ICA)方法是从一组观测信号中提取统计独立分量的方法.因为用这种方法分解出的各信号分量之间是相互独立的,而测得的脑电信号往往包含若干相对独立的成分,所以用它来分解脑电信号,所得的结果更具有生理意义,有利于去除干扰和伪差.本文简要地回顾了ICA的发展历史和主要算法,综述了它在脑电信号处理中的应用及研究进展,并指出了需要进一步研究解决的问题.     

3-D Source Localization of Epileptic Foci Integrating EEG and MRI Data

This study evaluates the utility of 3-D localization of interictal spike activity on the electroencephalographs (EEG) superimposed on magnetic resonance imagery (MRI) in a pediatric population with extra-temporal lesional epileptic foci. 3-D software programming based on the CURRY platform (a multimodal neuro-imaging software) was adapted for analyzing scalp EEG data and reconstructing superimposed images in 10 children who underwent extensive pre-surgical evaluation for intractable partial seizures. The results of 3-D spike source localization were assessed in relationship to focal lesions evident on the patient's MRI scans. Calculated spike sources were closest to the lesions during intervals corresponding to the spike peaks. The information was useful in surgical planning in six children that underwent successful resections.     

基于fMRI加权约束的FOCUSS迭代脑电源定位方法及其初步应用

脑电(Electroencephalography, EEG)和功能磁共振(Functional magnetic resonance imaging, fMRI)技术的结合,可以实现两者优势的互补,获得更加合理的源定位结果.本文报道的是一种将fMRI先验信息结合到脑电源定位中的新方法.在该方法中,先利用SPM方法计算获得fMRI的统计映射参数,然后将基于计算获得的统计参数构造的权矩阵结合到FOCUSS的迭代过程中,对脑电的反演提供具有fMRI先验空间位置信息的约束,提高脑电的源空间定位精度,从而获得更加合理的定位结果.通过对一形状知觉实验fMRI和脑电数据的结合定位分析,结果初步证实了改进方法能获得和生理更加一致的结果.     

Discriminant Analysis and Equivalent Source Localization of the EEG Related to Cognitive Functions

Discriminant analysis and EEG source localization methods were employed to compare groups of normal subjects during different cognitive conditions using 43-channel EEG recordings in the alpha (8-13 Hz) frequency band. Recordings were obtained from 69 dextral females during 2 passive conditions, Eyes-Open and Eyes-Closed, and 2 active conditions, Word-Finding and Dot-Localization. The cross-spectral matrix between all of the electrode sites was used to characterize the EEGs obtained during each condition. The subjects were partitioned into training and test sets and quadratic discriminant functions were constructed from the training sets to classify the EEGs. The discriminant functions successfully classified both the training and test sets at rates approaching 80%. The classification was repeated using only the diagonal (power spectral) elements of the cross-spectral matrices in the discriminant functions and this approach was successful in discriminating between the EEGs from the passive cognitive conditions but failed to discriminate between the EEGs from the active conditions. Source localization using a modified MUSIC algorithm indicated that the centers of brain electrical activity that distinguished the Eyes-Closed condition from the Eyes-Open condition were located in the medial occipital and right frontal regions. Centers of electrical activity that distinguished the Word-Finding condition from the Dot-Localization condition were located in the right medial posterior and left temporal regions. Validation of the locations of the centers of activity was accomplished by repeating the classification procedures using the spatial patterns generated on the scalp by dipole current sources placed at these locations.     

Equivalent Charge Source Model Based Iterative Maximum Neighbor Weight for Sparse EEG Source Localization

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.     

The Validation of the Finite Difference Method and Reciprocity for Solving the Inverse Problem in EEG Dipole Source Analysis

The performance of the finite difference reciprocity method (FDRM) to solve the inverse problem in EEG dipole source analysis is investigated in the analytically solvable three-shell spherical head model for a large set of test dipoles. The location error for a grid with 2 mm and 3 mm node spacing is in general, not larger than twice the internode distance, hence 4 mm and 6 mm, respectively. Increasing the number of scalp electrodes from 27 to 44 only marginally improves the location error. The orientation error is always smaller than 4° for all the test dipoles considered. We have also compared the sensitivity to noise using FDRM in EEG dipole source analysis with the sensitivity to noise using the analytical expression for the forward problem. FDRM is not more sensitive to noise than the method using the analytical expression.     

独立分量分析的研究和脑电中心电干扰的消除

本文研究和提出了一种用于独立分量分析的迭代算法 ,采用该算法成功地消除了存在于脑电信号中的心电干扰。基于信息论原理 ,给出了一个衡量各分量统计独立的目标函数 ,优化该目标函数 ,得出一种用于对独立分量进行盲分离的迭代算法 ,该算法的优点在于不需要计算信号的高阶统计量 ,收敛速度快。该算法使用一种去冗余方法 ,在提取一分量后 ,将其从混迭信号中去除 ,能逐一提取各独立分量。实验结果表明独立分量分析可有效地去除脑电信号中的心电干扰成分     

独立分量分析技术在EEG工频干扰分离中的应用

工频干扰是脑电图(EEG)中常见噪声，严重影响EEG-信号的提取和分析。通过比较Fastica、Extended Infomax、EGLD、Pearson—ICA等四种独立分量分析(ICA)算法和奇异值分解(SVD)技术用于分离EEG中工频干扰的效果，确证ICA方法有很好的抗干扰性，而常用的SVD技术则难以奏效；其中推广的最大熵(Extended Info—max)ICA算法有较好的收敛性，文中使用该算法成功地从16导联早老性痴呆症患者EEG信号中(含混入的工频干扰，最低信噪比约为0dB)分离出工频干扰。ICA在生物医学信号处理特别是临床医学工程中潜在着重要应用前景和研究价值。     

Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization

In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmentation approach (HSA)–Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)–FMRIB’s automated segmentation tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20 % bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3 % noise and synthetic EEG (generated for a prescribed source). The source localization accuracy was determined in terms of localization error and relative error of potential. The experimental results demonstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and suggest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.     

Detection and Separation of Event-related Potentials from Multi-Artifacts Contaminated EEG by Means of Independent Component Analysis

Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto--adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.