结合小波分解和径向基神经网络进行事件相关电位的单次提取

从单次实验记录中提取事件相关电位，无论在临床诊断上还是在大脑高级功能的研究中都起着重要的作用。介绍了一种将小波多分辨率分解和重建与径向基神经网络结合起来进行事件相关电位单次提取的方法。它基于事件相关电位主要是低频信号的事实，发挥径向基神经网络对连续函数的逼近能力，从信号的小波分解系数中提取出与低频响应相关的成分，构造了一种新的时频域滤波的方法，实验表明本方法较好地从单次记录中提取出了事件相关电位。     

结合子空间方法和提升小波变换提取事件相关脑电位

我们提出了一种提取事件相关脑电位的复合方法。它用奇异值分解方法将含噪信号分解为噪声子空间和信号子空间,将含噪信号正交投影到信号子空间进行初步除噪,随后将得到的信号进行提升小波变换,对变换结果进行一维小波重构进一步去除噪声,最后提取出ERP成分。介绍了基于奇异值分解的子空间方法和对信号进行提升小波去噪的实现方法。仿真结果表明,结合两种方法提取事件相关脑电信号时,比单独采用其中一种方法的效果要好,并可减少提取事件相关电位所需的实验次数。对实验数据的处理结果表明,该方法的实际处理效果良好。     

基于小波变换和盲源分离算法提取事件相关电位

阐述了用小波分解和盲源分离(blind source separation,BSS)算法结合来去除噪声和干扰提取事件相关电位(event-related potential,ERP).采用小波变换分解ERP,抽取出不同频带的细节信息;由小波系数判断选择多个尺度的子带信号,将它们分别与原始ERP组合进行盲分离,方法是极大化信号时间上的可预测性;将分离的结果进一步叠加平均.两类ERP仿真实验结果表明,本文算法提取出的ERP主要成分波明显,易于辨识,信噪比比较单独运用盲分离算法提取出的结果要好.在应用实例中,有效地增强了ERP的μ波.该算法优点在于减少了刺激次数和波形失真,参数变化范围小,在临床上有很好的应用前景.     

基于参数模型和独立分量分析的事件相关诱发电位单次提取

我们针对脑电事件相关电位（ERP）这种信噪比极低的信号检测问题，提出了两种ERP信号单次提取方法，能非常有效地同时去除自发脑电、眼动伪迹和工频噪声三种常见噪声。（1）首次对自发脑电、眼动伪迹和工频噪声这三种常见成分连同事件相关电位同时进行ARX建模，利用基于最小二乘（Ls）的ARX算法进行参数辨识获得提取结果；（2）利用独立分量分析，采用FastICA算法进行事件相关电位的提取。明确指出ICA分解的一些重要分解特性及其内在机理，针对实际情况对FastICA算法进行了改进，实现了分解结果对ERP成分的自适应映射。数值仿真实验结果表明两种方法均有较高的信号分解提取能力。     

基于小波变换的事件相关电位少次提取方法

本文以反映大脑稀少认知事件的相关电位P300为例,介绍了基于小波变换(wavelet transform,WT)进行事件相关电位(event related potential,ERP)少次提取的原理、仿真和实验结果.通过小波变换先去除与ERP混杂的EEG背景干扰,然后对消除干扰后的脑电数据进行重构和ERP波峰检测,在此基础上尝试修正ERP波峰的潜伏期时移,再通过小波重构获得通常需多次相干平均才能得到的ERP波形.仿真计算和实验数据分析结果说明小波分析具有较强的ERP峰值检测和潜伏期模式识别能力,值得进一步在临床工程中深入研究和应用推广.     

基于自适应移动平均的脑事件关联电位单次提取

提出一种基于自适应移动平均的脑事件关联电位单次提取方法。采用零交叉方法对背景噪声进行分析,自适应提取单次记录中的最大脉冲噪声宽度,将其作为窗口大小的基准,然后用移动平均算法对测量信号进行处理。实验结果表明,我们提出的方法不但具有良好的单次提取效果,而且具有较强的适应性和稳健性。     

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。     

基于自适应中值滤波的脑事件关联电位单次提取

提出一种基于自适应中值滤波的脑事件关联电位单次提取方法。刺激方式为odd-ball模式，直接用实测的数据进行实验。背景噪声从波形上看呈现为脉冲状形式，中值滤波可以有效地去除脉冲噪声。利用零交叉方法估计各单次记录信号中噪声的脉冲宽度，取其最大值作为基准参数，自适应地动态确定各单次滤波时所用窗口的大小。在窗口大小确定后，对各单次记录到的信号进行中值滤波器处理，最后对结果进行低通平滑。实验结果表明，本文提出的方法不但具有良好的单次提取效果，而且具有很强的适应性和稳健性。     

基于经验模态分解-小波包变换的表面肌电信号手势识别

为了提高表面肌电信号（sEMG）手部运动识别的正确率,比较常规的sEMG预处理和特征提取方法,提出一种基于经验模态分解（EMD）和小波包变换（WPT）的sEMG手势识别模型。首先,使用EMD方法将sEMG进行平稳化,得到一系列的固有模态函数。其次,求取各个固有模态函数与原始信号的相关性,选取相关性较高的前4个分量作为有效分量。然后,采用Db3小波函数进行WPT,提取小波包系数中的平均能量、平均绝对值、最大值、均方根和方差等特征。分别采用线性判别分析和支持向量机对12种手部运动进行模式识别。结果表明基于EMD和WPT的sEMG手势识别正确率比直接提取小波包系数中的特征识别正确率高。     

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。     

ERP单次提取中的小波变换模极大值恢复算法

自发脑电的频谱不规则,与有效信号ＥＲＰ频谱相重叠,传统滤波方法难以奏效。但由于随机噪声奇异性指数与有效信号奇异性指数大小不一样,小波变换模极大值在不同尺度下传播行为也不一样,据此可将有效信号从随机噪声中提取出来。我们发现利用小波变换模极大值算法可以提取出单次ＥＲＰ,并将此方法用于１１名被试听觉的实时提取研究。     

基于离散小波变换提取脑机接口中脑电特征

在脑机接口中，针对脑电特征提取利用单一种类信息、使用数据量大、分类性能较差等缺点，提出一种新颖的基于离散小波变换的方法。分析了小波变换特征提取的特点和特征表示方式，用Daubechies类db4小波函数对脑电信号进行6层分解，抽取小波变换各子带关键的部分逼近系数、小波系数、小波子带系数均值组成特征向量。以分类正确率为指标检验了提取特征的性能。实验结果表明，这种方法能够利用少量数据提取脑电信号本质特征，具有较高的分类性能，为利用脑电识别人的不同意图提供了快速而有效的手段。     

Updating and validating a new framework for restoring and analyzing latency‐variable ERP components from single trials with residue iteration decomposition (RIDE)

Trial‐to‐trial latency variability pervades cognitive EEG responses and may mix and smear ERP components but is usually ignored in conventional ERP averaging. Existing attempts to decompose temporally overlapping and latency‐variable ERP components show major limitations. Here, we propose a theoretical framework and model of ERPs consisting of temporally overlapping components locked to different external events or varying in latency from trial to trial. Based on this model, a new ERP decomposition and reconstruction method was developed: residue iteration decomposition (RIDE). Here, we describe an update of the method and compare it to other decomposition methods in simulated and real datasets. The updated RIDE method solves the divergence problem inherent to previous latency‐based decomposition methods. By implementing the model of ERPs as consisting of time‐variable and invariable single‐trial component clusters, RIDE obtains latency‐corrected ERP waveforms and topographies of the components, and yields dynamic information about single trials.     

Time adaptive denoising of single trial event-related potentials in the wavelet domain

We present a new wavelet-based method for single trial analysis of transient and time variant event-related potentials (ERPs). Expecting more accurate filter settings than achieved by other techniques (low-pass filter, a posteriori Wiener filter, time invariant wavelet filter), ERPs were initially balanced in time. By simulation, better filter performance could be established for test signals contaminated with either white noise or isospectral noise. To provide an example of real application, the method was applied to limbic P300 potentials (MTL-P300). As a result, variance of single trial MTL-P300s decreased, without restricting the corresponding mean. The proposed method can be regarded as an alternative for single-trial ERP analysis.     

EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier

In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.     

小波时频滤波器在ERP单次提取中的应用

在对听觉事件关联电位时，频域分析的基础上，结合相关分析方法，设计了出了基于多分辨率样条小波的时频滤波器，可以实现ＥＲＰ单次提取。     

基于高阶谱的肌纤维传导速度谱坑法检测

针对目前采用的谱坑法抗噪声性能差的问题,提出了基于高阶谱重构功率谱的谱坑法,检测肌纤维传导速度(MFCV),并对这种方法的抗噪性能进行了研究.同时,提出一种基于小波多尺度分析的适于噪声环境下谱坑自动检测的多尺度-二阶差分法,完成对肌纤维传导速度的检测.仿真实验结果表明,本文的方法与经典周期图估计功率谱谱坑法相比,可以显著改善估计功率谱的信噪比,提高谱坑法的抗噪能力和MFCV检测的精度.对真实针电极肌电信号的实验结果也表明,利用这种检测方法得到的MFCV数值与通常给出的MFCV范围相符.     

A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error.

This paper describes a hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression. First, the ECG signal is wavelet transformed using four different discrete wavelet transforms (Daubechies, Coiflet, Biorthogonal and Symmlet). All the wavelet transforms are based on dyadic scales and decompose the ECG signals into five detailed levels and one approximation. Then, the wavelet coefficients are linearly predicted, where the error corresponding to the difference between these coefficients and the predicted ones is minimized in order to get the best predictor. In particular, the residuals of the wavelet coefficients are uncorrelated and hence can be represented with fewer bits compared to the original signal. To further increase the compression rate, the residual sequence obtained after linear prediction is coded using a newly developed coding technique. As a result, a compression ratio (Cr) of 20 to 1 is achieved with percentage root-mean square difference (PRD) less than 4%. The algorithm is compared to an alternative compression algorithm based on the direct use of wavelet transforms. Experiments on selected records from the MIT-BIH arrhythmia database reveal that the proposed method is significantly more efficient in compression. The proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels.