基于多分辨分析与连续小波变换提取和分析兔体感诱发电位

研究单次提取兔体感诱发电位,并定位和分析诱发电位波形成分。麻醉兔,以0.5Hz频率电脉冲刺激兔下肢隐神经,3764Hz采样率收集兔头皮电位。采用一维多分辨分析提取兔体感诱发电位,并用连续小波变换定位和分析诱发电位波形成分。单次诱发电位的小波变换与叠加平均诱发电位比较,表明Daubechies小波多分辨分析可以单次提取诱发电位。连续小波变换能够精确定位诱发电位中波形成分,并可采用连续小波变换分析诱发成分的频域特性。连续小波变换技术把一维时域信号投影到二维时频空间研究将成为医学信号处理的一个有用方法。     

提升小波在诱发电位提取中的应用研究

目的诱发电位信号的少次甚至单次提取一直是信号处理领域关注的热点问题之一,探讨如何利用提升小波有效提取诱发电位信号。方法首先基于仿真脑电数据,比较提升小波方法与多孔算法的去噪效果,选出具有最优小波基和分解层数的提升小波方法,再应用提升小波方法提取实际诱发电位信号。结果提升小波方法提取诱发电位信号波形特征明显,提高了信噪比,且其运算量只有传统方法的一半左右。结论提升小波的方法在诱发电位的应用中效果明显,有应用前景。     

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。     

听觉诱发电位信号分析技术进展

听觉诱发电位(AEP)信号的提取技术是生物医学工程领域和信号处理领域长期以来一直关注的问题。本文综述了听觉诱发电位信号处理技术的一些新进展，主要介绍了功率谱估计、自适应处理方法、小波变换法、混沌理论、人工神经网络、正则化方法、独立分量分析7种处理方法，这些方法都致力于少次刺激、高质量提取听觉诱发电位信号，充分地展示了听觉诱发信号处理技术高效提取的前景。     

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     

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。     

听觉诱发电位提取方法的研究与分析

论述了运用小波变换进行听觉诱发电佗单次提取的原理、方法和实验分析。结果表明，对单次试验信号，经小波变换及相关分析后，可从带自发脑电干扰的信号中提取诱发电位信号。小波变换分析方法与传统的叠加平均方法相比，可减少试验次数，缩短检测周期。     

基于虚拟通道ICA方法的诱发电位单导少次提取

脑电诱发电位的单导少次提取一直是生物医学信号处理领域倍受关注的问题。独立分量分析作为解决盲源分离问题的一种有效算法已被广泛应用于诱发电位提取之中。独立分量分析处理的是多路观测信号,且要求观测信号路数大于或等于独立信号源的个数。为了能够应用独立分量分析算法实现诱发电位的单导少次提取,引入虚拟通道构建观测信号矩阵,从而得到符合实际应用条件的算法模型。4路信号仿真实验表明了虚拟通道模型可以有效提取诱发电位。对12位受试者进行模式翻转视觉诱发电位测试,仅用单导连续4次记录即可实现诱发电位的初步提取,信噪比增加约为12 dB,当采用10路虚拟通道,信噪比提高约20 dB。4路和10路虚拟通道ICA方法下得到的多导联VEP相关系数的统计结果进一步证实增加虚拟通道的数量,EP信号提取效果也会更好。     

基于自适应动态特征库的P300诱发脑电单次提取算法研究

目的 诱发电位的单次提取技术一直是脑电信息处理领域的难题之一,为进一步提高单次提取算法的时间准确性和特征精度,针对体感诱发脑电数据信噪比低、试次间参数变化大的特点,研究诱发脑电参数单次提取新算法,保留试次间诱发脑电的动态特性,并提高估计准确率.方法 基于小波滤波和多元线性分析技术,加入自适应动态特征库并由此提出的诱发脑电P300参数单次提取新方法.随机选取4组小波滤波(WF)后诱发脑电数据,分别叠加平均后进行主成分分析(PCA)组成特征库.单次提取时,针对每试次数据从特征库中选择与当次诱发脑电信号相关系数最高的成分作为自变量开展多元线性回归分析,由回归分析结构重构出单次诱发电位信号并自动提取潜伏期和幅值等关键特征.结果 与专家判定的基准数值相比,新算法预测的P300成分潜伏期与幅值参数更准确,两者的平均差值分别为(11.16±8.60) ms和(1.40±1.34)μV;与常用的叠加平均法结果亦更为接近,平均差值分别为(23.26±25.76) ms和(2.52±2.50) μV,新算法相比传统多元线性回归分析算法具有显著优势.结论 将动态更新的诱发脑电数据主成分样本库应用于小波滤波与多元线性回归方法,能有效保留单次诱发脑电数据中的动态特征,从而提升参数估计的准确率.     

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。     

Extraction of short-latency evoked potentials using a combination of wavelets and evolutionary algorithms

Somatosensory evoked potentials, recorded at the spine or scalp of a patient, are contaminated by noise. It is common practice to use ensemble averaging to remove the noise, which usually requires a large number of responses to produce one averaged signal. In this paper a post-processing technique is shown which uses a combination of wavelets and evolutionary algorithms to produce a representative waveform with fewer responses. The most suitable wavelets and a set of weights are selected by an evolutionary algorithm to form a filter bank, which enhances the extraction of evoked potentials from noisy recordings.     

Habituation and sensitization in rat auditory evoked potentials: a single-trial analysis with wavelet denoising.

In this work, systematic changes of single-trial auditory evoked potentials elicited in rats were studied. Single-trial evoked potentials were obtained with the help of wavelet denoising, a very recently proposed method that has already been shown to be useful in the analysis of scalp human evoked potentials. For the evoked components in the 13-24-ms range (i.e. P13, N18, P20 and N24), it was possible to identify slow exponential decreases in the peak amplitudes, most likely related to a slow habituation process, while for N18, an initial increase in amplitude was also found. On the contrary, the slower components (N38 and N52) habituated within a few trials, and we therefore propose that they are related to a different functional process. The outcomes of the present study show that wavelet denoising is a useful technique for analyzing evoked potentials in rats at the single-trial level. In fact, in the present study it was possible to obtain more information than the one described in previous related works. This allows the study of other forms of learning processes in rats with the aid of evoked potentials. Finally, the outcomes of this study may have some relevance for the comparison of human and rat evoked potentials.     

诱发电位快速提取算法的新进展

诱发电位 (EP)和事件相关电位 (ERP)的单次提取是生物医学信号处理领域颇受关注的一个研究问题。本文综述了近年来 EP/ERP快速提取算法的一些新进展 ,主要介绍了子空间正则化、自适应斯径向基神经网络、独立分量分析和奇异性检测四种方法 ,这些方法都立足于用较少的刺激次数提取出较高质量的诱发电位或事件相关电位 ,展示了 EP/ERP快速提取的前景。     

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。     

Beta (~16 Hz) frequency neural oscillations mediate auditory sensory gating in humans

The brain's oscillatory activities in response to sensory input are likely signals representing different stages of sensory information processing. To understand these signals, it is critical to establish the specificity of the timing and frequency of oscillations associated with sensory and sensory-related cognitive processing. We used a simple paired auditory stimulus paradigm for sensory gating and sought to identify time- and frequency-specific oscillatory components contributing to sensory gating. Using a discrete wavelet decomposition technique we separated single-trial time-frequency components of evoked potentials elicited by the first of two stimuli. Regression analyses were then used to identify the components most relevant to the suppression of the second evoked potential response. The results suggested that beta oscillation indexed a neural process associated with the strength of sensory gating.     

Latency analysis of single auditory evoked M100 responses by spatio-temporal filtering

Appropriate spatial filtering followed by temporal filtering is well suited for the single-trial analysis of multi-channel magnetoencephalogram or electroencephalogram recordings. This is demonstrated by the results of a single-trial latency analysis obtained for auditory evoked M100 responses from nine subjects using two different stimulation frequencies. Spatial filters were derived automatically from the data via noise-adjusted principle component analysis, and single-trial latencies were estimated from the signal phase after complex bandpass filtering. For each of the two stimulation frequencies, estimated single-trial latencies were consistent with results obtained from a standard approach using averaged evoked responses. The quality of the estimated single-trial latencies was additionally assessed by their ability to separate between the two different stimulation frequencies. As a result, more than 80% of the single trials can be classified correctly by their estimated latencies.