基于虚拟通道ICA-WT大鼠视觉诱发电位少次提取

为了实现电极植入法采集到的单通道大鼠视觉诱发电位的少次提取,提出一种基于虚拟通道的独立分量分析-小波变换的大鼠视觉诱发电位少次提取方法。首先,引入虚拟通道,用快速独立分量分析方法将噪声与视觉诱发电位进行分离;然后,对分离出的视觉诱发电位进行少次叠加平均,提取出初步视觉诱发电位;最后,针对虚拟通道与实际噪声不完全吻合带来的残留噪声问题,用小波变换进一步去除初步视觉诱发电位中的残留噪声。实验结果表明,基于虚拟通道的独立分量分析8次叠加平均,就能很好地提取出初步的视觉诱发电位;小波阈值法进一步去除了残留噪声;本算法成功地实现大鼠视觉诱发电位的少次提取,同时该方法可以用于单通道其他脑电信号的提取。     

认知诱发电位动态提取的研究进展

认知诱发电位是评价脑认知功能的重要指标。目前临床上应用的平均认知诱发电位由于平均而丧失了大量的动态信息;尤其是认知诱发电位潜伏期(相位)变化大,平均方法将使其波形和波幅严重失真。因此认知诱发电位的单次(少次)动态提取是近年来诱发电位提取技术研究的热点与难点。动态提取认知诱发电位的关键之一在于如何使提取方法适于每个单次诱发电位相位变化大的问题。以三类典型的动态认知诱发电位单次提取方法为例,介绍了近年来在该领域中的研究动向。这三种方法是:B样条小波变换去噪声法、基于三阶相关的滤波法和小波神经网络法。     

基于AAA-ICM技术的视觉闪光诱发电位单次提取新方法的仿真研究

目的:利用计算机仿真人体脑电背景信号和人视觉诱发电位信号,验证基于自参考、自相关、自适应干扰对消理论(AAA-ICM)的诱发电位单次提取新方法。材料与方法:用Delphi语言编程仿真脑电背景信号与视觉闪光诱发电位信号,实现AAA-ICM算法提取诱发电位信号,用信号的功率值及相关系数衡量提取效果。结果:仿真结果表明使用该方法,可以从带有强的自发脑电干扰信号中单次提取出诱发电位信号。结论:与传统的叠加平均提取方法相比,简化了提取方法,改善了提取质量。     

自适应干扰对消的大鼠视觉诱发电位提取

研究大鼠的视觉诱发电位对认知人类视觉机理和疾病发生的位置具有重要意义.为获得大鼠视觉刺激诱发电位的特征信息,本研究设计了一种新的用于自适应干扰对消的大鼠视觉诱发电位信号采集方案,在大鼠初级视皮层不同位置和深度分别植入检测电极和干扰参考电极,有效提取了同源干扰信号.并基于该方案进行了大鼠视觉诱发电位的快速提取研究.仿真实验和实际应用结果表明,应用该方案实现自适应干扰对消,信噪比提高约6 dB,能够有效提取出大鼠单次闪光刺激诱发电位波形,并可从单次提取的结果中获得准确的潜伏期信息.该方案为实时获取大鼠视觉诱发电位特征信息提供了有效手段.     

认知诱发电位动态提取的研究进展

认知诱发电位是评价脑认知功能的重要指标。目前临床上应用的平均认知诱发电位由于平均而丧失了大量的动态信息；尤其是认知诱发电位潜伏期(相位)变化大，平均方法将使其波形和波幅严重失真。因此认知诱发电位的单次(少次)动态提取是近年来诱发电位提取技术研究的热点与难点。动态提取认知诱发电位的关键之一在于如何使提取方法适于每个单次诱发电位相位变化大的问题。以三类典型的动态认知诱发电位单次提取方法为例，介绍了近年来在该领域中的研究动向。这三种方法是：B样条小波变换去噪声法、基于三阶相关的滤波法和小波神经网络法。     

噪声中EP信号的提取算法及研究进展

诱发电位的提取是脑电信号处理领域的前沿课题近年来 ,通过少次甚至单次试验提取诱发电位已经成为研究的主流。本文对近年来提取诱发电位的信号处理方法进行了简要的回顾 ,并分别从小波变换、神经网络、高阶累积量、独立分量分析等四个方面对算法进行了介绍     

诱发电位快速提取方法及进展

每次刺激所产生的诱发电位的峰值和潜伏期以至波形是时变信号。随着信号处理技术的发展,诱发电位的提取方法由传统的平均叠加方法,向逐步减少累加次数,最终实现单次提取诱发响应波形,实现动态提取诱发电位的方向发展,因此对诱发电位的少次以至单次提取成为生物医学信号处理领域备受关注的一个研究课题。对近年来快速提取诱发电位的信号处理方法进行了简要的回顾,并分别从自适应滤波、小波变换、神经网络及独立成分分析等4个方面对算法进行了介绍。     

基于独立分量分析的大脑视觉诱发电位单次提取

脑电 (Electroencephalography ,EEG)视觉诱发电位 (VisualEvokedPotential,VEP)的单次提取是当前生物医学信号处理领域的一个研究热点。提出一种基于独立分量分析 (IndependentComponentAnalysis,ICA)的多道脑电信号VEP单次提取方法 ,与多次叠加求平均的方法相比较 ,可以得到令人满意的结果。     

诱发电位提取的聚类分析和小波去噪复合方法

本文研究了提取诱发电位的一种复合方法,它用聚类方法对诱发电位信号进行筛选,将筛选出的信号进行叠加以消除测试数据不一致性对叠加结果的影响.然后利用小波方法进一步去除噪声,提高信噪比.文中介绍了模糊聚类方法和小波去噪理论,并通过仿真计算来评估提取效果.仿真计算表明该方法可减少测试次数,提高信噪比,对视觉脑干诱发电位处理结果显示,该方法的实际处理效果良好.     

基于自参考自相关自适应干扰对消技术听觉皮层诱发电位单次提取

目的：为实现人体的听觉皮层诱发电位（auditory cortical evoked potential，ACEP）单次提取。方法：将当前流行的计算机应用于生物医学，通过多媒体音频技术产生适合诱发人体听见诱发电位的声音刺激，刺激人体，在电脑皮层提取诱发电位，在大脑皮层提取诱发电位，采用自参考、自相关、自适应干扰对消技术（auto—reference，auto—correlative and adaptive interference cancellation theories and techniques，AAA-ICT）实现听觉皮层诱发电位的单次提取。结果：通过AAA—ICT技术。成功提取出了人体的听觉皮层诱发电位。结论：通过结果可以得出：AAA—ICT技术能够很好地提取人体的听觉皮层诱发电位。而且其自参考思想的创新性明显。     

ICA在视觉诱发电位的少次提取与波形分析中的应用

本文提出一种基于扩展的独立分量分析 (ICA)算法的视觉诱发响应少次提取方法。经与目前临床通用的相干平均法比较 ,只经三次平均 ,在波形整体和P10 0潜伏期的提取上 ,效果显著 ,获得医师欢迎 ,很有进一步开发潜力。     

基于ANFIS的自适应噪声消除方法在视觉诱发脑电信号的单次提取中的应用

本研究将基于自适应模糊神经网络(ANFIS)的噪声消除方法应用于视觉诱发脑电信号的单次提取。通过数字仿真和实际临床应用的结果验证了该方法的有效性。经与目前临床通用的相干平均法比较，在波形整体和P100潜伏期的提取上，效果显著。     

Application of adaptive noise cancellation with neural-network-based fuzzy inference system for visual evoked potentials estimation

This paper presents an application of adaptive noise cancellation with neural-network-based fuzzy inference system (NNFIS) for rapid estimation of visual evoked potentials (VEPs). Usually a recorded VEP is severely contaminated by background ongoing activities of the spontaneous EEG signal in the human brain. Many approaches have been adopted to enhance the signal-to-noise ratio (SNR) of the recorded signal. However, nonlinear dynamic methods are rarely investigated in view of their complexity, and the fact that the nonlinear characteristics of the signal are hard to determine in general. An adaptive noise cancellation method with NNFIS was carefully designed to estimate the VEP signal. NNFIS, based on Takagi and Sugeno's fuzzy model, has the advantage of being linear-in-parameter; thus the conventional adaptive methods can be efficiently utilized to estimate its parameters. Another advantage of NNFIS lies in that it can track the dynamic behavior of VEP in a real-time fashion because the VEP variation tracking is important for critical patient monitoring in the clinical situation. A series of computer experiments conducted on simulated and real-test responses have confirmed the superiority of the method developed in this paper.     

基于内模自适应估计器的VEP信号提取新方法

本文基于确定信号和扰动和内模建立了两种视觉诱发脑电（VEP）和自发脑电（EEG）混合信号的组合状态空间模型，基于时域参数模型提出了采用迭代型推广卡尔曼滤波算法来实现VEP信号的自适应估计，最后通过临床实际应用比较分析了两种建模方法的滤波性能。     

Visual evoked potential estimation by adaptive noise cancellation with neural-network-based fuzzy inference system

Visual evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). In this paper, an adaptive noise cancellation with neural network-based fuzzy inference system (NNFIS) was used and the NNFIS was carefully designed to model the VEP signal. It is assumed that VEP responses can be modelled by NNFIS with the centres of its membership functions evenly distributed over time. The weights of NNFIS are adaptively determined by minimizing the variance of the error signal using the least mean squares (LMS) algorithm. As the NNFIS is dynamic to any change of VEP, the non-stationary characteristics of VEP can be tracked. Thus, this method should be able to track the VEP. Four sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method.     

Visual evoked potential estimation by adaptive noise cancellation with neural-network-based fuzzy inference system

Visual evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). In this paper, an adaptive noise cancellation with neural network-based fuzzy inference system (NNFIS) was used and the NNFIS was carefully designed to model the VEP signal. It is assumed that VEP responses can be modelled by NNFIS with the centres of its membership functions evenly distributed over time. The weights of NNFIS are adaptively determined by minimizing the variance of the error signal using the least mean squares (LMS) algorithm. As the NNFIS is dynamic to any change of VEP, the non-stationary characteristics of VEP can be tracked. Thus, this method should be able to track the VEP. Four sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method.     

A coded VEP method to measure interhemispheric transfer time (IHTT)

Interhemispheric transfer time (IHTT) is an important parameter for research on the information conduction time across the corpus callosum between the two hemispheres. There are several traditional methods used to estimate the IHTT, including the reaction time (RT) method, the evoked potential (EP) method and the measure based on the transcranial magnetic stimulation (TMS). The present study proposes a novel coded VEP method to estimate the IHTT based on the specific properties of the m-sequence. These properties include good signal-to-noise ratio (SNR) and high noise tolerance. Additionally, calculation of the circular cross-correlation function is sensitive to the phase difference. The method presented in this paper estimates the IHTT using the m-sequence to encode the visual stimulus and also compares the results with the traditional flash VEP method. Furthermore, with the phase difference of the two responses calculated using the circular cross-correlation technique, the coded VEP method could obtain IHTT results, which does not require the selection of the utilized component.     

重症肌无力患者视、听、体感诱发电位研究

目的：研究重症肌无力(MG)患者视觉诱发电位(VEP)、脑干听觉诱发电位(BAEP)和体感诱发电位(SEP)的变化及其与中枢神经系统(CNS)损害的关系。方法：对22例临床及重复神经电刺激(RNS)确诊的MG患者行VEP、BAEP检测，其中的18例还进行了SEP检测。结果：22例MG患者中VEP异常15例(68．2％)、BAEP异常8例(36．4％)。SEP检查18例均异常，其中上肢SEP异常8例(44．4％)，下肢SEP异常10例(55．6％)。结论：MG患者的VEP、BAEP、SEP均有不同程度异常，表明MG患者可伴有CNS损害，而VEP、BAEP、SEP可作为早期发现MG患者CNS改变的有效检测手段。