基于多尺度传递熵的脑肌电信号耦合分析

神经运动控制中脑肌电同步特征可以反映皮层与肌肉之间的功能联系. 为定量研究脑电和肌电信号在不同时间尺度上的同步耦合特征, 提出多尺度传递熵方法实现静态握力输出下的脑肌电耦合分析: 对同步采集的头皮脑电信号(EEG) 和表面肌电信号(EMG)进行多尺度化, 计算不同尺度因子下EEG与EMG间的传递熵值, 获取不同耦合方向(EEG→EMG及EMG→EEG)上的非线性脑肌电耦合特征; 进一步计算功能频段下的显著性面积指标, 定量分析不同尺度下皮层肌肉功能耦合强度的差异. 分析结果显示, 静态握力输出时beta频段(15–35 Hz)皮层肌肉功能耦合特征显著, 且beta2频段(25–35 Hz)在不同尺度上EEG→EMG方向的耦合强度大于EMG→EEG方向, 耦合强度最大值和方向间耦合强度差异显著值均出现于较高时间尺度. 研究结果揭示: 皮层肌肉功能耦合具有双向性, 且耦合强度在不同时间尺度和不同功能频段上有所差异, 可利用多尺度传递熵定量刻画大脑皮层与肌肉之间的非线性同步特征及功能联系.     

基于变分模态分解-相干分析的肌间耦合特性

肌间耦合是肢体运动过程中不同肌肉间的相互关联与相互协调作用.通过研究肌电信号(s EMG)间特征频段的耦合特性可以获得肌肉间的功能联系及中枢神经系统支配肢体运动的执行与协调方式机理.本文将变分模态分解与相干分析相结合,构建变分模态分解-相干分析模型,定量描述肢体运动中相关肌肉sEMG在特征频段的耦合特性.在20%最大自主收缩力静态负荷强度下,采集20名健康被试的sEMG,基于变分模态分解方法将sEMG时频尺度化,进而分析不同sEMG在特征频段的相干性,并计算显著相干面积指标,定量分析肌间特征频段的功能耦合特性.结果表明:低负荷静态握力维持过程中,指浅屈肌与尺侧腕曲肌、指浅屈肌与指伸肌的beta与gamma频段耦合强度随时间推进而增强;相较于指浅屈肌与指伸肌,疲劳状态下指浅屈肌与尺侧腕曲肌beta与gamma频段耦合强度变化更显著,且瞬时频率特征变化相似,揭示运动致疲劳过程中协同肌受中枢神经系统控制以更加同步的方式活动.     

基于弹性变分模态分解的癫痫脑电信号分类方法

癫痫脑电信号分类对于癫痫诊治具有重要意义.为了实现病灶性与非病灶性癫痫脑电信号的分类,本文利用弹性网回归重构变分模态分解算法,提出弹性变分模态分解算法并将其应用到所提癫痫脑电信号分类方法中.该方法先将原信号分割成多个子信号,并对各子信号进行弹性变分模态分解,然后从分解后的不同变分模态函数中提取精细复合多尺度散布熵作为特征,最后利用支持向量机进行分类.针对癫痫脑电的公共数据集,最终的实验结果表明,准确率、灵敏度和特异度三个性能指标分别达到92.54%,93.22%和91.86%.     

基于变分模态分解与多尺度排列熵的生物组织变性识别

根据高强度聚焦超声(HIFU)治疗中超声散射回波信号的特点,本文利用变分模态分解(VMD)与多尺度排列熵(MPE)对生物组织变性识别进行了研究.首先对生物组织中的超声散射回波信号进行变分模态分解,根据各阶模态的功率谱信息熵值分离出噪声分量和有用分量;对分离出的有用信号进行重构并提取其多尺度排列熵;然后通过Gustafson-Kessel (GK)模糊聚类确定聚类中心,采用欧氏贴近度与择近原则对生物组织进行变性识别.将所提方法应用于HIFU治疗中超声散射回波信号实验数据,用遗传算法对多尺度排列熵的参数优化后,对293例未变性组织和变性组织的超声散射回波信号数据进行了多尺度排列熵分析,发现变性组织的超声散射回波信号的多尺度排列熵值要高于未变性组织;多尺度排列熵可以较好地识别生物组织是否变性.相对于EMD-MPE-GK模糊聚类以及VMD-小波熵(WE)-GK模糊聚类变性识别方法,本文所提方法中变性与未变性组织特征交叠区域数据点更少,聚类效果和分类性能更好;本实验环境下生物组织变性识别结果表明,该方法的识别率更高,高达93.81%.     

多尺度多变量模糊熵分析

多尺度多变量样本熵评价同步多通道数据的多变量复杂度, 是非线性动态相互关系的一种反映, 但其统计稳定性差, 且不适用于非线性非平稳信号. 研究利用模糊隶属度函数代替模式相似判断的硬阈值准则, 并分析模糊隶属度函数形式的影响; 研究利用多变量经验模态分解算法进行多尺度化, 并对比其处理效果. 仿真试验表明, 模糊隶属度函数的引入可以有效提高算法的统计稳定性, 所构造的物理模糊隶属度函数的性能最为显著; 基于多变量经验模态分解算法的多尺度化过程可更有效地捕获信号的不同尺度成分, 从而更敏感地区分具有不同复杂度的信号. 对临床试验数据的分析支持以上结论, 且结果提示随着年龄增加或心脏疾病的发生, 心率变异性和心脏舒张间期变异性的多变量复杂度以不同的方式降低: 年龄增加会使低尺度熵值降低, 表示近程相关性的丢失; 而心脏疾病会同时影响各个尺度的熵值, 即同时丢失了近程和长时相关性. 该结论可用于指导心血管疾病的无创预警研究. 关键词： 多变量复杂度 多尺度多变量模糊熵 物理模糊隶属度函数 多变量经验模态分解     

基于自适应无参经验小波变换和选择集成分类模型的运动想象

运动想象模式识别率的提高对脑机接口(BCI)技术的应用具有重要意义,本文采用自适应无参经验小波变换(APEWT)和选择集成分类模型相结合的方法提高脑电(EEG)信号的分类识别准确率.首先,通过APEWT将EEG信号分解成不同的模态;然后,使用最优模态重构后的信号计算其能量谱(ES)特征,使用最优模态分量计算其边际谱(MS)特征;最后,将不同时间段的ES特征和不同频段的MS特征输入到构建的选择集成分类模型中,从而得到其分类结果,并将该方法与其他4种组合方法进行比较.实验结果表明,本文方法具有较好分类准确率和实时性,其平均分类正确率高于其他4种方法,同时较近期使用相同数据的文献也有优势.本文为在线运动想象类BCI的应用提供了新的方法和思路.     

运动后人体心肺节律同步关系及信号的耦合方向

研究运动后青年心肺系统的耦合关系.通过提取在校学生运动后的心跳和呼吸信号,采用经验模态分解的方法对信号进行滤波,分析了心肺信号的相同步行为和心肺节律间耦合作用的相对强弱关系.结果表明,人体在运动后仍然存在心肺系统节律同步现象.这种同步比例不仅因人而异,而且因时而异.由传递熵的计算结果得知,呼吸对于心跳的耦合作用相对较大.     

2D经验模态分解与非下采样方向滤波器组的红外与可见光图像融合算法

针对当前红外(IR)与可见光(VI)图像融合中细节保留能力不足及目标配准精度不高的问题,设计了一种多尺度2D经验模态分解耦合非下采样方向滤波器组(NSDFB)的红外与可见光图像融合算法。分别计算红外与可见光图像的熵值,并比较二者阈值的大小,计算阈值较大图像的残差。通过2D经验模态分解(2D-EMD)和NSDFB机制,构建了多尺度方向分解模型,将熵值较大图像的残差和熵值较小的图像变换为高频方向系数与低频系数,以获得源图像的细节和特征信息。对于低频系数,引入加权平均作为低频系数的融合准则;根据区域能量对比度与清晰度来定义融合规则,完成高频系数的融合。利用2D-EMD多尺度分解逆变换将获取的低频与高频系数生成新图像。实验表明:与当前常用红外与可见光图像融合对比,所提算法具有更高的融合质量,所输出的图像具有更好的对比度与丰富的细节信息。     

应用变分模态分解及能量熵的扬声器异常声分类

为更准确地实现扬声器异常声分类以及促进其分类的自动化,提出一种基于变分模态分解(Variational Mode Decomposition,VMD)能量熵和遗传算法优化的支持向量机(Genetic Algorithm-Support Vector Machines,GA-SVM)的扬声器异常声分类方法。首先对测得的扬声器单元声响应信号进行VMD,然后提取每个变分模态函数(Variational Mode Function,VMF)的能量熵并进行统计分析,最后利用GA-SVM进行异常声判断。实验结果表明,与VMD时频熵、经验模态分解(Empirical Mode Decomposition,EMD)能量熵、EMD时频熵这3种特征提取方法相比,VMD能量熵能更准确地表征扬声器单元异常声特征,具有更高的平均识别率,其平均识别率为96.3%,较以上3种方法分别提高了18.3%,24.0%,54.3%。      

基于两节点间信息互传的量子特性

运用全量子理论,对腔耦合系统构建的两个节点,考虑节点内腔模与量子位(qubit)的耦合,结合数值计算,用熵表示信源的不确定性,对信源发出的信息进行度量,研究了两个节点相互进行信息传输过程的熵演化.通过两能级粒子与腔模的耦合强度、腔-腔之间的跃迁耦合系数和失谐量三个参数对熵变化进行分析,结论表明在共振条件下,节点间相互传输信息过程中,耦合双腔构成的两个节点熵呈现准周期性坍塌与复苏振荡变化特征,节点1与节点2熵的峰值交替出现;两个节点之间用跳跃频率λ/2π的光子作为信息传送的数椐总线,失谐使两个节点内量子位的一个频率高,一个频率低,无论先操纵哪一个量子位,在系统稳定工作状态下,量子态演化的信息传递方向总是从频率快的量子位向频率慢的量子位传递,此特性可扩展至多量子位之间量子信息的传递.用于两个节点间的远程操控.     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

Corticomuscular synchronization with small and large dynamic force output

Background  

Over the last few years much research has been devoted to investigating the synchronization between cortical motor and muscular activity as measured by EEG/MEG-EMG coherence. The main focus so far has been on corticomuscular coherence (CMC) during static force condition, for which coherence in beta-range has been described. In contrast, we showed in a recent study [1] that dynamic force condition is accompanied by gamma-range CMC. The modulation of the CMC by various dynamic force amplitudes, however, remained uninvestigated. The present study addresses this question. We examined eight healthy human subjects. EEG and surface EMG were recorded simultaneously. The visuomotor task consisted in isometric compensation for 3 forces (static, small and large dynamic) generated by a manipulandum. The CMC, the cortical EEG spectral power (SP), the EMG SP and the errors in motor performance (as the difference between target and exerted force) were analyzed.     

Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals.     

海洋波导中刚性球及旋转椭球前向散射场时频特征的畸变

利用波导中目标声散射理论的简正波方法,数值模拟了自由空间和浅海均匀波导中刚性球、旋转椭球的散射场,分析了波导中刚性体前向散射时频特征的畸变规律。仿真结果表明:波导中刚性体的前向散射时域波形与频谱特征受波导制约;目标散射场的固有特征受波导色散特征的调制,时频谱上呈现出由于色散的简正波间的耦合而导致各简正波能量条纹界限模糊的现象。时频特征的畸变程度与组成散射共振系统的波导、目标的尺度以及散射体布放深度有关,随波导底质透声能力和深度增加、散射体散射强度(信混比)增强而减弱。      

Estimation of time delay by coherence analysis

Using coherence analysis (which is an extensively used method to study the correlations in frequency domain, between two simultaneously measured signals) we estimate the time delay between two signals. This method is suitable for time delay estimation of narrow band coherence signals for which the conventional methods cannot be reliably applied. We show, by analysing coupled Rössler attractors with a known delay, that the method yields satisfactory results. Then, we apply this method to human pathologic tremor. The delay between simultaneously measured traces of electroencephalogram (EEG) and electromyogram (EMG) data of subjects with essential hand tremor is calculated. We find that there is a delay of 11–27 milli-seconds (ms) between the tremor correlated parts (cortex) of the brain (EEG) and the trembling hand (EMG) which is in agreement with the experimentally observed delay value of 15 ms for the cortico-muscular conduction time. By surrogate analysis we calculate error bars of the estimated delay.     

An investigation of the relationship between BOLD and perfusion signal changes during epileptic generalised spike wave activity

In pathological conditions interpretation of functional magnetic resonance imaging (fMRI) results can be difficult. This is due to a reliance on the assumed coupling between neuronal activity and changes in cerebral blood flow (CBF) and oxygenation. We wanted to investigate the coupling between blood oxygen level dependant contrast (BOLD) and CBF time courses in epilepsy patients with generalised spike wave activity (GSW) to better understand the underlying mechanisms behind the EEG-fMRI signal changes observed, especially in regions of negative BOLD response (NBR). Four patients with frequent GSW were scanned with simultaneous electroencephalographic (EEG)-fMRI with BOLD and arterial spin labeling (ASL) sequences. We examined the relationship between simultaneous CBF and BOLD measurements by looking at the correlation of the two signals in terms of percentage signal change on a voxel-by-voxel basis. This method is not reliant on coincident activation. BOLD and CBF were positively correlated in patients with epilepsy during background EEG activity and GSW. The subject average value of the Delta CBF/Delta BOLD slope lay between +19 and +36 and also showed spatial variation which could indicate areas with altered vascular response. There was not a significant difference between Delta CBF/Delta BOLD during GSW, suggesting that neurovascular coupling to BOLD signal is generally maintained between states and, in particular, within areas of NBR.     

Dispersion-based short-time Fourier transform applied to dispersive wave analysis

Although time-frequency analysis is effective for characterizing dispersive wave signals, the time-frequency tilings of most conventional analysis methods do not take into account dispersion phenomena. An adaptive time-frequency analysis method is introduced whose time-frequency tiling is determined with respect to the wave dispersion characteristics. In the dispersion-based time-frequency tiling, each time-frequency atom is adaptively rotated in the time-frequency plane, depending on the local wave dispersion. Although this idea can be useful in various problems, its application to the analysis of dispersive wave signals has not been made. In this work, the adaptive time-frequency method was applied to the analysis of dispersive elastic waves measured in waveguide experiments and a theoretical investigation on its time-frequency resolution was presented. The time-frequency resolution of the proposed transform was then compared with that of the standard short-time Fourier transform to show its effectiveness in dealing with dispersive wave signals. In addition, to facilitate the adaptive time-frequency analysis of experimentally measured signals whose dispersion relations are not known, an iterative scheme for determining the relationships was developed. The validity of the present approach in dealing with dispersive waves was verified experimentally.     

On the Synchronization of EEG Spindle Waves

Based on recently sleeping cellular substrates, a network model synaptically coupled by N three-cell circuits is provided. Simulation results show that: (i) the dynamic behavior of every circuit is chaotic; (ii) the synchronization of the network is incomplete; (iii) the incomplete synchronization can integrate burst firings of cortical cells into waxing-and-wanning EEG spindle waves. These results enlighten us that this kind of incomplete synchronization may integrate microscopic, electrical activities of neurons in billions into macroscopic, functional states in human brain. In addition, the effects of coupling strength, connectional mode and noise to the synchronization are discussed.