基于集合经验模态分解和小波收缩算法的自适应心电信号去噪问题研究

本文将集合经验模态分解(EEMD)与小波软阈值去噪算法相结合,提出了一种新的心电图信号去噪EEMD-WS算法.算法首先对信号进行EEMD分解得到有限个固有模态函数(IMF);其次,根据实际含噪心电信号中各成分的特性,将所有IMF分为低阶含噪、中阶有用信号和高阶含基线漂移三类,对于低阶含噪IMF利用IMF能量变化分界点自适应地确定含噪IMF个数,随后对其利用小波收缩算法中的启发式软阈值选择算法进行去噪;对于高阶含基线漂移IMF根据其自身是否包含周期信息自适应地判断并去除与基线漂移关系密切的IMF.最后通过将滤除噪声的低阶IMF、中阶有用信号重构达到抑制噪声和去除基线漂移的目的.仿真信号和MIT-BIH心电数据库真实心电信号实验显示,EEMD-WS算法不仅能够克服小波去噪算法不能去除基线漂移的不足,而且能够比常用的EMD-WS算法更好地提高消噪效果,总体去噪性能优于传统算法.     

基于思维脑电信号的假手的研究

本文主要研究利用思维脑电信号来控制假手动作。采用小波变换对思维脑电信号进行分解,选取合适的子带信号并提取相应能量特征,组成特征向量输入BP神经网络进行分类识别。整个信号处理过程在LabVIEW软件平台上实现,并利用其串口通信模块输出控制指令来控制假手的张开和闭合。     

基于HIFU回波信号和多尺度模糊熵的生物组织变性识别研究

为了解决高强度聚焦超声（HIFU）治疗中的监测问题,在无需引入其他监测源的情况下,通过研究HIFU回波基波与二次谐波的多尺度模糊熵（MFE）,提出了一种生物组织变性辨析新方法。HIFU回波信号通过谱减法去噪后,利用信息散度优化的变分模态分解（KLD-VMD）提取其基波与二次谐波分量,然后结合基波与二次谐波的MFE对组织进行变性识别,并使用等错误概率（EER）评价了该方法的有效性。最后,研究还比较了KLD-VMD与VMD、经验模态分解（EMD）和固有时间尺度分解（ITD）等其他分解方法,结合MFE分析了其辨析变性组织的能力。试验结果表明：基于KLD-VMD和MFE的组织变性识别其EER达到5.1%,相较于其他方法表现出了更好的识别效果;结合基波和二次谐波的识别结果比使用单一特征参数更好。该研究为HIFU治疗提供了一种新的监测方法,具有潜在的实际应用价值。     

运动观察与运动想象的皮层节律活动与神经生理机制

研究证实,运动观察与运动想象对大脑的激活有利于中风后的运动功能再学习,可用于探索人类行为过程中大脑的神经机制.为对比分析运动观察和运动想象时皮层神经元的活动特征,选取10名健康被试,采集每名被试在运动观察和运动想象时特定手部抓握动作模式下的脑电信号(EEG);引入Gabor滤波器对感觉运动区和视觉区的EEG进行时频能量谱估计,并在此基础上对EEG进行事件相关去同步/同步化(ERD/ERS)分析;最后建立ERDI(ERD index)指标对左手和右手进行模式分类并量化比较运动观察与运动想象.研究结果表明,运动观察与运动想象类似,均激活大脑感觉运动皮层,并且运动想象产生对侧主导的α和βERD;基于ERDI指标的运动想象左右手识别正确率高于运动观察分类正确率;此外,运动观察过程还同时伴随视觉皮层活动,使β节律能量产生显著衰减.本研究为运动观察和运动想象在临床康复训练以及脑机接口领域的应用提供了神经生理基础和实现途径.     

基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

基于小波变换心电信号消噪方法研究

小波变换是近年来兴起的热门信号处理技术,是一种非常有用的信号处理工具。本文阐述了连续小波去噪和离散小波去噪的原理,分析了基于小波去噪的几种不同方法（其中包括小波分解与重构,小波变换阈值法,小波变换模极大值法,以及它与独立分量分析相结合去除噪声的方法等）。通过检测和验证,表明该方法能较好的实现心电信号的消噪,都取得了较好的效果;同时,比较了每种方法的不足和缺陷。基于小波变换心电信号消噪的研究进展较快,通过多种方法结合运用进行消噪并取得了很好的效果,展望了利用基于小波变换心电信号消噪的前景。     

基于独立元分析和非线性指数分析的脑电信号中伪迹分量的自动去除

脑电(electroencephalography,EEG)信号中不可避免地存在着眼动、心跳、肌电信号以及线性噪声等伪迹干扰,这些伪迹的存在极大地影响了脑电信号分析的准确性,因此在进行脑电信号分析前需要去除伪迹干扰。为了有效地去除伪迹,结合独立元分析和非线性指数分析,提出一种自动识别并去除脑电信号中伪迹分量的方法。该方法还可同时用于提取脑电信号中的基本节律如!波等。相应的模拟与实际脑电数据的实验结果表明所提议的方法具有很好的识别和去除脑电信号伪迹分量的性能。     

Labview在动物脑电信号采集分析中的应用

分析动物行为活动中的脑电特征是脑机接口(Brain-computer interface,BCI)研究中的一个重要内容.本文利用最新测控软件-虚拟仪器技术(LabVIEW)进行脑电信号采集与处理,实现了信号实时显示、中值滤波、小渡消噪的设计.实验结果显示提取出了与特定行为(抓食)相关的脑电活动特征信号,为研究大脑如何控制行为提供了一个有效的方法.     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

基于小波变换的单次诱发电位信号时频分析

比较小波变换和平均叠加两种方法提取“模拟自然阅读”刺激模式下的诱发脑电信号,分析其时频特性,并进行脑功能源分布定位分析。结果显示,采用平均叠加法来提取和分析诱发电位信号,损失了某些重要的诱发电位成分,且其功能源分布定位反映的只是等效功能源的静态过程;而使用小波变换和脑功能源定位来提取和分析单次诱发电位信号,既能观察到丰富的诱发电位成分,又能反映脑功能源的实时动态活动过程。这表明,小波变换下的时频分析是脑电信号处理的一种可行的新方法。     

基于时频分析检测EEG中癫痫样棘/尖波的方法

提出了一种基于Ｃｈｏｉ－Ｗｉｌｌｉａｍｓ分布检测ＥＥＧ中癫痫样棘波／尖波的方法。该方法通过计算ＥＥＧ信号的时频分布,得到一段信号在各个时刻上沿频率方向上的能量分布。这种能量分布相当于一种瞬时频谱,反映了ＥＥＧ信号在局部时间范围里的波形特征。以一段ＥＥＧ信号在各个时刻的瞬时频谱的平均作为这段脑电的背景信号频谱,通过计算每一时刻的瞬时频谱与背景信号频谱之间的频谱差,检测这段信号中的棘波／尖波。对临床Ｅ     

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony. Action Editor: Carson C. Chow     

Information quantification of empirical mode decomposition and applications to field potentials

The empirical mode decomposition (EMD) method can adaptively decompose a non-stationary time series into a number of amplitude or frequency modulated functions known as intrinsic mode functions. This paper combines the EMD method with information analysis and presents a framework of information-preserving EMD. The enhanced EMD method has been exploited in the analysis of neural recordings. It decomposes a signal and extracts only the most informative oscillations contained in the non-stationary signal. Information analysis has shown that the extracted components retain the information content of the signal. More importantly, a limited number of components reveal the main oscillations presented in the signal and their instantaneous frequencies, which are not often obvious from the original signal. This information-coupled EMD method has been tested on several field potential datasets for the analysis of stimulus coding in visual cortex, from single and multiple channels, and for finding information connectivity among channels. The results demonstrate the usefulness of the method in extracting relevant responses from the recorded signals. An investigation is also conducted on utilizing the Hilbert phase for cases where phase information can further improve information analysis and stimulus discrimination. The components of the proposed method have been integrated into a toolbox and the initial implementation is also described.     

Time-Dependent Statistical and Correlation Properties of Neural Signals during Handwriting

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the spectral range (13–30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2–3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals.     

Local Properties of Vigilance States: EMD Analysis of EEG Signals during Sleep-Waking States of Freely Moving Rats

Understanding the inherent dynamics of the EEG associated to sleep-waking can provide insights into its basic neural regulation. By characterizing the local properties of the EEG using power spectrum, empirical mode decomposition (EMD) and Hilbert-spectral analysis, we can examine the dynamics over a range of time-scales. We analyzed rat EEG during wake, NREMS and REMS using these methods. The average instantaneous phase, power spectral density (PSD) of intrinsic mode functions (IMFs) and the energy content in various frequency bands show characteristic changes in each of the vigilance states. The 2nd and 7th IMFs show changes in PSD for wake and REMS, suggesting that those modes may carry wake- and REMS-associated cognitive, conscious and behavior-specific information of an individual even though the EEG may appear similar. The energy content in θ₂ (6Hz-9Hz) band of the 1st IMF for REMS is larger than that of wake. The decrease in the phase function of IMFs from wake to REMS to NREMS indicates decrease of the mean frequency in these states, respectively. The rate of information processing in waking state is more in the time scale described by the first three IMFs than in REMS state. However, for IMF5-IMF7, the rate is more for REMS than that for wake. We obtained Hilbert-Huang spectral entropy, which is a suitable measure of information processing in each of these state-specific EEG. It is possible to evaluate the complex dynamics of the EEG in each of the vigilance states by applying measures based on EMD and Hilbert-transform. Our results suggest that the EMD based nonlinear measures of the EEG can provide useful estimates of the information possessed by various oscillations associated with the vigilance states. Further, the EMD-based spectral measures may have implications in understanding anatamo-physiological correlates of sleep-waking behavior and clinical diagnosis of sleep-pathology.     

EMD-based EEG signal enhancement for auditory evoked potential recovery under high stimulus-rate paradigm

Short inter-stimulus interval (ISI) is one inherent characteristic of the high stimulus-rate (HSR) paradigms for studying auditory evoked potentials (AEPs). At short ISIs, the AEPs to adjacent stimuli overlap. To resolve the AEP to a specific stimulus requires an inverse process of overlapping. Inverse filtering (also called as deconvolution) has been commonly employed to achieve this goal. However, the resulted signal may be severely distorted as inverse filtering can substantially amplify such undesired components as noises and artifacts in the raw EEG recordings. In practice, even if care be taken to obtain quality EEGs, noises and artifacts are unavoidable. It is thus critical to remove or at least supress these undesired components for studies using HSR paradigms. In this paper, we propose a systematic approach to EEG signal enhancement based on empirical mode decomposition (EMD) and threshold filtering/rejection. Using synthetic and real data, we test the effectiveness of our approach. Results for both types of data consistently demonstrate that our methods can significantly improve the quality of recovered AEPs, according to visual inspection and SNRs estimated using two metrics.     

基于脑电四阶累积量的运动意识分类研究

提出了基于四阶累积量为脑电特征的意识任务分类思想.对被测试者想象左右手运动时的脑电归一化四阶累积量（峭度）及其动态变化情况进行了研究.结果表明,归一化四阶累积量能较好地反映左右手运动想象的脑电特征变化.在此基础上,进行了基于脑电四阶累积量的左右手运动意识识别和分类研究,实验结果表明,正确识别率能达到87.5%.由于四阶累积量的计算比较简单,而且可在线计算,因此可以认为,基于脑电四阶累积量为特征的运动意识分类及其在脑机接口技术中的应用,具有较高的实际应用价值.     

The comparison of motor learning performance with and without feedback

Ten individuals were divided into two feedback and no-feedback groups. The effect of abstract visual feedback was investigated in these two groups. Using eight electroencephalography (EEG) electrodes, the induced event-related desynchronization/synchronization of the EEG of three motor imagery tasks (left hand, right hand, and right foot) was analyzed by wavelet and spatial filtering methods. Linear discriminant analysis was used to classify the three imagery tasks. Each imagery task's total length was set to 3?s and 1?s of it was used for the classification. The classification result was shown to the subjects of the feedback group in a real-time manner as an abstract visual feedback. While the paired t-test of the first and third sessions of the training days confirmed the improvement of the motor imagery learning in the feedback group (p?<?0.01), the motor imagery learning of the no-feedback group was not significant.     

Functional regional asymmetry of the EEG intrahemispheric coherence in dogs during conditioning

Individual features of the regional interhemispheric relations in the brain were studied in dogs during alimentary conditioning. The electrical activity was recorded from symmetrical anterior (frontal and motor cortices) and posterior (visual and auditory cortices) areas of the neocortex. Comparison between the averaged left and right intrahemispheric EEG coherences revealed a dynamic character of interhemispheric relations dependent on the stage of conditioning. Individual features were shown. In a dog with strong type of the nervous system, in the anterior brain regions, the EEG coherence was higher in the left hemisphere than in the right one, whereas, on the contrary, in the posterior regions, the values were higher in the right than in the left hemisphere. In dogs with weak type of the nervous system, there was an inverse relationship. Thus, the spatial organization of the cortical electrical activity in the associative and projection brain areas was different.     

Electrical activity of prefrontal cortex and ventral tegmental area during rat maternal behavior

Maternal behavior is a motivated behavior that includes pup-directed sequential motor acts. The dopaminergic (DAergic) brain systems have been proposed to play an important role in voluntary maternal acts, however, not much is known about the way these systems function during the performance of this behavior. The electroencephalogram (EEG) is a sensitive tool that allows determination of the simultaneous functioning of different structures in relation to specific cognitive processes or motor acts. The present study recorded the function of the two structures that constitute the mesoprefrontal DAergic system, ventral tegmental area (VTA) and prefrontal cortex (PFC) by EEG during the performance of various maternal behaviors. Bilateral EEG from the VTA and medial PFC (mPFC) was simultaneously recorded during typical maternal acts and was compared to that recorded during non-maternal behaviors in freely moving female rats. Three different frequency bands (6-7, 8-11, and 12-21 Hz) were obtained from principal component analysis applied to the EEG for both structures. In the left and right mPFC and VTA, absolute power (AP) of the 8-11 Hz band showed a significant increase during pup retrieval compared to the EEG during walking. In the left and right mPFC and VTA, AP of the three bands showed a significant increase during pup licking with respect to forepaw licking. No differences in the EEG were found during inactive nursing behaviors compared to the awake quiet condition. The mPFC and VTA presented characteristic EEG patterns during active maternal behaviors but not during inactive maternal behaviors. This provides electrical evidence of the involvement of these structures in the performance of maternal behavior.