基于真实头模型的EEG参考电极标准化技术

目的 :研究基于真实头模型的参考电极标准化技术并验证它的有效性。方法 :基于真实头模型的等效源技术 ,建立了由以头表某点或平均电位为参考点的实际记录近似得到以无穷远点为参考的记录的方法。由于无穷远点的电位可假设为常数零 ,因此可称该方法为真实头模型的参考电极标准化技术 (REST)。对真实头模型全空间内任意位置的源的实际头表记录进行REST处理 ,然后计算经REST处理后的近似记录与以无穷远点为参考的记录之间的相对误差 ,以此检验该技术的有效性。结果 :对于真实头模型REST是有效的 ,尤其对重要的表层皮质区域更为有效 ,它的应用可以降低因非零的参考电位而引入头表记录的干扰。结论 :采用参考电极标准化技术能得到更真实的脑电 (EEG)记录 ,从而可能对脑电的应用产生重要的影响。     

脑电记录中"参考电极位置"的设置研究

目的：探讨一种可以把头皮脑电活动（EEG）记录中的“参考电极位置”近似地转化到无穷远点的新技术。方法：基于由头皮记录确定神经电活动源或其等效源不受参考电极影响的事实，用基于普通参考电极描记到的头皮EEG记录近似地重建出一组等效源，然后由等效源计算出以无穷远点为参考电极位置的头皮记录。结果：用在导体中假设神经电活动源的方式进行的仿真研究表明，当电源位于最重要的皮层表面区域时，可以明显降低参考电极对EEG记录的不良影响。结论：EEG记录的“参考电极位置”可以被近似地转换到无穷远点，从而近似地得到一个以零电位为参考的标准记录。     

利用有限差分算法研究开颅术对头皮电位分布的影响

本研究在三层同心球模型上利用有限差分法求解开颅手术后的脑电正问题,通过与开颅术前脑电正问题的解进行比较,研究颅骨上的洞对头皮电位分布的影响。仿真试验表明,颅骨开洞对头皮电位分布的影响程度取决于偶极子源的位置和方向:径向偶极子源位于洞正下方时,洞对头皮电位分布的影响最大;而切向偶极子源靠近洞边缘时的影响最大。     

颅内电极脑电图及其对癫痫灶的定位

1　颅内电极记录的必要性脑电图描记的是大脑皮层神经细胞产生的神经电活动 ,需一定数量的神经细胞同步活动才能产生足够强大的电流 ,此种电流传导到头皮表面才能被常规脑电图描记下来。常规脑电图所描记到的脑电活动受到多种因素的影响 ,比如同步活动的神经细胞数量的多寡、神经细胞在大脑皮层脑沟和脑回内排列的方向、颅骨和头皮的完整性以及其导电率的改变等。头皮和颅骨是电的不良导体 ,大脑皮层产生的神经电活动在经过颅骨和头皮的传导时会受到明显的衰减 ,所以头皮记录到的脑电活动通常只有皮层记录的几分之一到十几分之一。此外 ,不…     

临床神经电生理学术语(8)

指将活体脑自发生物电位经电极引出并输入脑电图仪放大和描记下来的随时间变化而变化的曲线。以常规脑电图为例，要点如下：①在本质上，脑电图描记的是电极对之间——两个头皮电极正下方的脑细胞群之间（双极导联），或一个头皮电极正下方的脑细胞群与参考电极之间（参考导联）——脑自发生物电的电位差在频率、波幅、位相等方面随时间的变化而变化的曲线；②最初使用的真空管放大器由于放大倍数受限且每放大一级信号被倒相一次，心电图为mV级，经两次放大已可供分析，其位向又被倒回，因而向上偏转为正，向下偏转为负。但脑电图系μV级，需再经第三级放大才可供分析，因而是向下偏转为正，向上偏转为负，与数学、物理学上座标图不一样，是很特别的现象。当今的晶体管集成电路“级”的概念已淡化，     

一种建立在有限电阻网络模型上的高分辨脑电图的新算法--Ⅰ.同心球头模型

本文提出一种由头皮测量脑电逆推皮层电位分布的新方法 ,使脑电图的空间分辨率明显提高。方法建立在把无源似稳电场过程转化成有限电阻网络过程的基础上。不但可以逐层递推头皮下各层组织结构上的电位分布 ,而且减少了计算量。仿真计算初步证明了方法的可行性。     

脑电记录中不同参考电极位置的同步对比分析

目的：探讨脑电记录中不同参考电极位置对图形分布特征的影响。方法：对500例病人进行了相同部位参考电极导联脑电图的同步对比分析。结果：耳垂参考电极有活化现象和伪迹干扰，但波幅高，中线两侧脑波失真较少。Cz参考电极干扰少有利于颞部的异常电位的暴露，但不利于头顶部病变的发现。颧骨弓处盘状电极减少了活化和伪迹干扰。结论：颞部病变以Cz为参考点的导联阳性率最高，而中线两侧各部的病变则以耳垂为参考点的单极导联阳性率最高，因此脑电图描记时应变换各种参考电极位置进行比较。     

脑电皮层成像技术

由头皮电位测量信号重构脑电活动源分布,对于研究脑的认知功能和临床医学应用具有重要意义。近10年发展起来的皮层成像技术(corticalimagingtechnique)因其在脑电源的定位方面相对于传统脑电图具有更高的空间分辨率,成为脑科学研究的有力工具。本文详细介绍了皮层成像技术的研究现状,主要包括等效皮层源及头容积导体的建模、头皮测量电位与等效皮层源间定量关系的建立、等效皮层源的重构算法。     

脑电逆问题的建模与仿真

从头皮电位的分布推算脑电活动源的脑电逆问题的关键之一是头模型的建立.作者利用CT扫描图像,建立了更接近实际情况的头的双层边界模型,并且用边界元方法求解脑电正问题.文中提出一种"两步法"有效地解决了正问题求解中的数值计算病态,并使模型误差限定在一定范围之内.脑电求逆是一个非线性最小均方优化问题,可利用Marquardt算法进行,其中电位对偶极子参量偏导阵同样可通过"两步法"解决.在求逆过程中,另一个问题是噪声的影响,本文通过仿真计算验证了求逆结果对噪声的敏感性,并初步探索了用奇异值分解技术和时空全局优化方法来消除和抑制噪声的效果.     

一种建立在有限电阻网络模型上的高分辨脑电图的新算法 …

本文提出一种由头皮测量脑电逆推皮层电位分布的新方法,使脑电图的空间分辨率明显提高。方法建立在把无源似稳电场过程转化成有限电阻网络过程的基础上。不但可以逐层递推头皮下各层组织结构上的电位分布,而且减少了量。仿真计算初步证明了方法的可行性。     

Separation of electrocardiographic and encephalographic components based on signal averaging and wavelet shrinkage techniques

During ambulatory monitoring, it is often required to record the electroencephalogram (EEG) and the electrocardiogram (ECG) simultaneously. It would be ideal if both EEG and ECG can be obtained with one measurement. We introduce an algorithm combining the wavelet shrinkage and signal averaging techniques to extract the EEG and ECG components from an EEG lead signal to a noncephalic reference (NCR). The evaluation using simulation data and measured data showed that the normalized power spectrum unvaried in all frequency bands for the EEG components, and the sensitivity and specificity of R-wave detection for the ECG component were nearly 100%.     

EEG assessment of brain activity: Spatial aspects,segmentation and imaging

High temporal resolution and sensitivity to index different functional brain states makes the EEG a powerful tool in psychophysiology. Its full potential can now be utilized since recording technology and computational power for the large data masses has become affordable. However, basic traditional strategies in EEG need reviewing.Conventional, spontaneous or evoked EEG traces which are used for various complex analyses give ambiguous information on EEG power (amplitude) and phase for a given point on the scalp. Principally, analysis should first be done over space, then over time, to avoid ambiguities or pre-selections. First or second spatial derivative computations can provide “reference-free” data for analyses over time. We propose to use direct, spatial approaches for the analysis of the scalp EEG field distributions when simultaneous recording in several EEG channels can be examined.The ambiguity of the conventional EEG waveshapes results in different, equally “correct” scalp maps of EEG power of the same multichannel data for different reference electrodes. An exeption are scalp maps of EEG power computed against the common, average reference, as they are related to the reference-free spatial distribution (maps) of the maximal and minimal (extreme) field values over time, and thus are directly interpretable in terms of net orientation of the generator process.A proposed, reference-free EEG segmentation into epochs of periodically stationary spatial distributions of the mapped scalp EEG fields uses the locations of maximal and minimal (extreme) field values at each moment in time as classifiers, and thus avoids the priviledging of two arbitrarily chosen recording points in the field.     

Separation of EEG and ECG components based on wavelet shrinkage and variable cosine window

During ambulatory monitoring, it is sometimes required to record an electroencephalogram (EEG) and an electrocardiogram (ECG) simultaneously. It would be ideal if both EEG and ECG could be obtained with one measurement. Here, we introduce an algorithm that combines the wavelet shrinkage and variable cosine window operation to separate the EEG and ECG components from an EEG signal recorded with a noncephalic reference (NCR). Evaluation using simulated data and actual measured data showed that accurate frequency analysis of EEG and an R-R detection-based heart rate analysis were feasible with our proposed algorithm, which improved the signal-averaging based algorithm so that ECG components containing ectopic beats can be applied.     

Automatic reference selection for quantitative EEG interpretation: Identification of diffuse/localised activity and the active earlobe reference,iterative detection of the distribution of EEG rhythms

EEG (Electroencephalograph) interpretation is important for the diagnosis of neurological disorders. The proper adjustment of the montage can highlight the EEG rhythm of interest and avoid false interpretation. The aim of this study was to develop an automatic reference selection method to identify a suitable reference. The results may contribute to the accurate inspection of the distribution of EEG rhythms for quantitative EEG interpretation. The method includes two pre-judgements and one iterative detection module. The diffuse case is initially identified by pre-judgement 1 when intermittent rhythmic waveforms occur over large areas along the scalp. The earlobe reference or averaged reference is adopted for the diffuse case due to the effect of the earlobe reference depending on pre-judgement 2. An iterative detection algorithm is developed for the localised case when the signal is distributed in a small area of the brain. The suitable averaged reference is finally determined based on the detected focal and distributed electrodes. The presented technique was applied to the pathological EEG recordings of nine patients. One example of the diffuse case is introduced by illustrating the results of the pre-judgements. The diffusely intermittent rhythmic slow wave is identified. The effect of active earlobe reference is analysed. Two examples of the localised case are presented, indicating the results of the iterative detection module. The focal and distributed electrodes are detected automatically during the repeating algorithm. The identification of diffuse and localised activity was satisfactory compared with the visual inspection. The EEG rhythm of interest can be highlighted using a suitable selected reference. The implementation of an automatic reference selection method is helpful to detect the distribution of an EEG rhythm, which can improve the accuracy of EEG interpretation during both visual inspection and automatic interpretation.     

基于带参考信号的ICA算法的脑电信号眨眼伪差的分离研究

独立分量分析（ICA）是一种从混合信号中提取统计独立的分量的一种方法.本研究提出了一种基于带参考信号的ICA算法的脑电信号眨眼伪差的分离方法,可以得到纯净的脑电信号.这个方法的主要思路是：先选取一导眨眼伪差比较明显的数据,从中获得眨眼伪差的参考信号,再用ICA方法把眨眼伪差第一个提取出来,最后得到消除伪差后的EEG信号.详细讨论了使用带参考信号的ICA算法消除眨眼伪差的方法与步骤,并给出了应用于真实信号的实验结果.     

平均参考电极在儿童脑电图中的应用研究

目的 :探讨平均参考电极技术在儿童脑电图记录中的优越性。方法 :对 183例小儿进行相同部位的单极、双极和平均参考导联脑电图的同步对比分析。结果 :平均参考导联记录的脑电图不仅能与单极和双极导联一样 ,发现常见普遍性样放电波 (98 4% ) ,而且能克服单极导联中常伴有的活化现象和参考电极伪迹 ,更清晰地记录出单极导联中不易分辨的局限性样放电波 (8 6 % )。结论 :在脑电图描记中采用平均参考电极技术 ,能更好地避免活化现象和伪迹干扰 ,准确发现样放电波 ,在儿童脑电图工作中更具有重要的实用价值。     

电流极子在均匀导电球模型中电位解析解的数值验证

80年代以来 ,基于脑电图进行无创性病灶定位的研究 ,特别是基于等效电流极子定位法 ,得到了相当的发展并已经投入到正式的临床应用之中 ,因此等效电流极子模型在脑电图分析中逐步得到了广泛地应用。本文在参考了国内外学者对于电流极子在均匀导电球模型中的电位解的求解思路 ,进一步推导出电位解析解的表达式 ,并对该解析解的正确性进行计算验证