基于MEMD-rTVgPDC的皮层肌肉耦合分析

本文将多元经验模态分解(MEMD)与鲁棒时变广义偏定向相干性(rTV-gPDC)引入皮层肌肉耦合分析中,探索脑肌电之间线性和非线性耦合关系。首先同步采集8名健康志愿者在静态握力(5 kg、10 kg、20 kg)下的三通道脑电(EEG)和肌电(EMG)信号,接着采用MEMD对信号进行时-频尺度化,最后同时计算不同耦合方向(EEG→EMG和EMG→EEG)上的rTV-gPDC线性和非线性值。实验结果表明静态握力输出时,皮层肌肉耦合主要反映在beta和gamma频段,其中EEG→EMG方向的耦合强度略高于EMG→EEG方向的耦合强度,且随着左右手握力增加,EEG→EMG和EMG→EEG方向的耦合强度同时增加。此外脑肌电耦合中同时存在线性和非线性因果关系。本文方法能够定量刻画不同握力下三个脑肌电通道之间的线性和非线性交互影响,可为研究运动功能障碍及康复评价提供有效的生理参数指标。     

基于多元经验模态分解-传递熵的脑肌电信号耦合分析

皮层肌肉功能耦合是大脑皮层和肌肉组织间的相互作用,脑肌电信号的多尺度耦合特征可以体现皮层-肌肉间多时空的功能联系.将多元经验模态分解(MEMD)与传递熵(TE)结合,构建出MEMD-TE模型,应用于脑、肌间耦合分析.首先对同步采集的脑电(EEG)和肌电(EMG)信号进行预处理,然后采用多元经验模态分解算法对信号进行时-频尺度化,最后计算不同尺度上的传递熵值,分析各个尺度不同耦合方向(EEG→EMG及EMG→EEG)上的非线性耦合特征.采集了10名受试者静态握力(5 kg、10 kg、20 kg)下脑、肌电信号,实验结果表明:脑电对肌电的MEMD-TE值在高频段(40 Hz~75 Hz)上高于肌电对脑电的MEMD-TE值,皮层肌肉功能耦合具有双向性,且不同方向和频段上的耦合强度有所差异,显著性校验反映了不同力度下脑电对肌电的MEMD-TE值没有显著性差别.     

基于小波包-Copula互信息的肌间耦合特性

为探讨人体运动中肌肉间的功能联系,更加准确地理解不同特征频段下的肌间耦合情况,将小波包分解与Copula互信息相结合,构建了基于小波包-Copula互信息的肌间耦合分析模型,定量描述上肢肘关节屈曲运动中相关肌肉表面肌电(sEMG)信号在特征频段上的耦合特性。首先用小波包对同步采集到的sEMG进行信号分解,然后用Copula互信息计算sEMG在特征频段的耦合强度。肘关节屈曲运动中,肌间耦合强度在beta与gamma频段最为显著,协同肌肉对的耦合强度显著高于拮抗肌肉对;随着运动时间的增长,肌肉呈现疲劳状态,协同肌肉对与拮抗肌肉对的耦合强度增加。小波包-Copula互信息能定量分析肌间特征频段的功能耦合特性,揭示运动过程中协同肌与拮抗肌的运动控制机制。     

基于脑电和惯性同步分析的神经动力学耦合研究

运动控制是神经、运动和感觉功能的多方面协调及信息交互作用过程,探究运动系统中运动-生理信息间的关联关系对于理解人体运动控制机制具有重要意义。为此,本文通过对脑电信号（Electroencephalogram, EEG）和惯性信息中的加速度信号（Acceleration, ACC）进行相干分析,探究上肢静息态和任务态时EEG和ACC信号间的因果关系及演变规律。首先,通过对7名受试者的EEG和ACC信号进行预处理,去除信号中的干扰成分;进一步,分别计算在静息态、任务态（动态力、静态力）下的EEG和ACC信号间的相干性结果,并通过显著相干的阈值指标来计算显著性面积进而实现量化分析。结果显示,在动态力下的EEG-ACC相干显著性面积大于静态力下的值,静态力下的显著性面积大于静息态下的值;且分别在左、右侧上肢运动时,EEG的C3、C4通道与ACC间的显著性面积也呈现出在对侧运动脑区显著。研究结果表明,EEG和ACC信号间的同步特征在上肢运动的静息态、任务态（动态力、静态力）下有显著特征,这有助于深入理解神经-运动控制机制,为运动功能评估提供新的定量指标,进而为运动功能障碍疾病的早期诊断提供理论依据。     

一种基于EEG和sEMG的假手控制策略

针对残臂较短或残臂上肌电信号测量点较少的残疾人使用多自由度假手的需求,提出一种基于脑电信号（Electroencephalogram,EEG）和表面肌电信号（Surface electromyogram signal,sEMG）协同处理的假手控制策略.该方法仅用1个肌电传感器和1个脑电传感器实现多自由度假手的控制.采用1个脑电传感器测量人体前额部位的EEG,从测量得到的EEG中提取出眨眼动作信息并将其用于假手动作的编码;采用1个肌电传感器测量手臂上的sEMG,并针对肌电信号存在个体差异和位置差异的问题,采用自适应方法实现手部动作强度的估计;采用振动触觉技术设计触觉编码用于将当前假手的控制指令反馈给佩戴者,从而实现EEG和sEMG对多自由度假手的协同控制.为验证该控制策略的有效性进行了实验研究,结果表明,提出的假手控制策略是有效的.     

卒中后上肢肌间协同耦合分析研究

从运动的产生与执行角度，探讨肌肉协同模型中协同肌的耦合强度差异，能够为卒中患者康复运动提供生理依据。12名健康人和13名卒中患者参与了这项研究，利用非负矩阵分解算法，从上肢记录的8通道表面肌电数据中提取肌肉协同作用，然后在α、β、γ频段上利用相干性面积指标评估协同肌间的耦合强度，卒中患者的协同作用中，主要被激活的三角肌前束(AD)的协同关系发生了变化。相应地，协同肌在β频段的耦合强度存在显著性差异(p<0.05)。肌间协同耦合特性相近:AD与三角肌中束(MD)具有较高的协同关系。在γ频段具有较高的耦合强度，非协同肌之间的耦合较弱，肌间协同耦合特性的变化可以作为卒中患者运动功能障碍的重要指标。     

基于脑机接口的控制器

为实现大脑与设备的通讯,研究基于脑机接口的控制器．本文研究脑电信号（EEG）的特征提取,实验通过E．Prime心理学软件结合Neuroscan公司生产的64导脑电采集设备获取脑电原始信号,利用小波算法对原始脑电信号进行分析,提取感兴趣的频段小波系数作为特征;分析脑电信号的功率谱,基于Fisher准则设计分类器．本文在研究脑机接口（Brine．ComputerInterface,BCI）的基础上通过想象实现了对运动的控制．以轮椅为实际控制对象,仅仅通过两个电极采集脑电信号,设计制作了控制器,理论验证实验成功的通过想象控制轮椅的四个方向的运动．本文最后还探讨了脑机接口的应用前景．     

基于典型相关分析与低通滤波的肌电伪迹去除

提出了一种基于典型相关分析(CCA)和低通滤波的盲源分离方法去除脑电信号(EEG)中的肌电伪迹.该方法首先将混入了肌电伪迹的EEG信号分解为不相关的CCA分量,然后对与伪迹源相关的分量进行低通滤波处理,去除这些分量中的高频伪迹成分,最后利用与EEG相关的CCA分量和滤波处理后的新分量重构信号,消除肌电伪迹的影响.实验结果表明,采用CCA能够有效地分离出肌电伪迹,而结合低通滤波技术能够更有效地保留EEG信息.该方法取得了较好的去除肌电伪迹的效果.     

基于运动观察EEG的运动方向解析*

与运动想象相比,运动观察的脑电信号被试没有主动思维参与,解析难点在于其信号幅值更弱且难以获取。实验围绕运动观察中的方向判别,以获取运动观察过程脑电特征明显频段作为切入点,首先利用运动观察眼动追踪信号确定有效观察任务,绘制脑地形图序列,定位激活的脑区,选出关联通道,然后结合运动观察EEG在特定频段时域上的能量特征较明显的特性,提出改进的CSP算法,基于信号能量特征,利用SVM进行分类识别。实验得到运动方向解析平均80%以上分类准确率,最高在0-4Hz频段上,达到了86.15%,实现了运动观察虚拟小车左右转的解析与识别,为复杂运动观察任务EEG的解析与识别提供了有效的方法。     

基于运动相关皮层电位握力运动模式识别研究

面向基于脑-机接口（Brain-computer interface,BCI）的脑-机交互控制（Brain-machine interaction control,BMIC）——直接脑控机器人,提出一种新的左、右手握力运动参数范式,在该范式下探索左、右手握力运动相关皮层电位/运动相关电位（Movement-related potentials,MRPs）的时域特征表示并识别握力运动模式.在涉及左、右手4个不同任务的实验中采集了11个健康被试的脑电信号,任务期间要求被试以2种握力变化模式之一完成自愿握力运动,每种任务随机重复30次.不同握力任务之间具有显著差异的运动相关电位特征用于识别握力运动模式.分别用基于核的Fisher线性判别分析和支持向量机识别4个不同的握力运动任务.研究结果进一步证实运动相关电位可以表征握力运动规划、运动执行和运动监控的脑神经机制过程.基于核的Fisher线性判别分析和支持向量机分别获得24±4%和21±5%的平均错误分类率.最小误分类率是12%,所有被试平均最小误分类率为20.9±5%.与传统的仅仅识别参与运动的肢体类型以及识别单侧肢体运动参数的研究相比,本研究可望为脑-机交互控制/脑控机器人接口提供更多的力控制意图指令,奠定了后续的对比研究基础.     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5–30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8–12 Hz) frequency band is weakened, and the beta (13–30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake–fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5-30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8-12 Hz) frequency band is weakened, and the beta (13-30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake-fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline‐based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks.     

Data-driven visualization and group analysis of multichannel EEG coherence with functional units

A typical data-driven visualization of electroencephalography (EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, we define a functional unit (FU) as a data-driven region of interest (ROI). An FU is a spatially connected set of electrodes recording pairwise significantly coherent signals, represented in the coherence graph by a spatially connected clique. Earlier we presented two methods to detect FUs: a maximal clique based (MCB) method (time complexity O(3n/3), with n being the number of vertices) and a more efficient watershed based (WB) method (time complexity O (n2 log n)). To reduce the potential over-segmentation of the WB method, we introduce here an improved WB (IWB) method (time complexity O(n2 log n)). The IWB method merges basins representing FUs during the segmentation if they are spatially connected and if their union is a clique. The WB and IWB methods are both up to a factor of 100,000 faster than the MCB method for a typical multichannel setting with 128 EEG channels, thus making interactive visualization of multichannel EEG coherence possible. Results show that considering the MCB method as the gold standard, the difference between IWB and MCB FU maps is smaller than between WB and MCB FU maps. We also introduce two novel group maps for data-driven group analysis as extensions of the IWB method. First, the group mean coherence map preserves dominant features from a collection of individual FU maps. Second, the group FU size map visualizes the average FU size per electrode across a collection of individual FU maps. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data-driven group analysis. Results, in accordance with the conventional findings, indicate differences in EEG coherence between younger and older adults. However, they also suggest that an initial selection of hypothesis-driven ROIs could be extended with additional data-driven ROIs.     

Estimating vigilance level by using EEG and EMG signals

We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%.     

Classification of EMG signals using combined features and soft computing techniques

The motor unit action potentials (MUPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. Since recently there were different types of developments in computer-aided EMG equipment, different methodologies in the time domain and frequency domain has been followed for quantitative analysis of EMG signals. In this study, the usefulness of the different feature extraction methods for describing MUP morphology is investigated. Besides, soft computing techniques were presented for the classification of intramuscular EMG signals. The proposed method automatically classifies the EMG signals into normal, neurogenic or myopathic. Also, multilayer perceptron neural networks (MLPNN), dynamic fuzzy neural network (DFNN) and adaptive neuro-fuzzy inference system (ANFIS) based classifiers were compared in relation to their accuracy in the classification of EMG signals. Concerning the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques. The comparative analysis suggests that the ANFIS modelling is superior to the DFNN and MLPNN in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability.     

下肢表面肌电信号的降维和映射分析

肌电信号的采集和分析是外骨骼式康复机器人关节预测控制的重要基础之一.肌电信号数据量大并且复杂,相关性较高,信号处理通用性和高效性低,分析和预测人体运动信息误差大.采用最大自主等长收缩标准化处理算法,大大提高了表面肌电信号的通用性和泛化能力,并基于主成分分析方法,对肌电信号降维处理,利用神经网络实现与下肢的映射分析.实验结果表明,通过对比分析不同的降维处理方式,主成分降维后处理的肌电信号平均相关性达0.93,利用神经网络预测人体正常行走的下肢三关节运动角度,具有良好的可重复性和较高的精度,可以实现人体下肢肌电信号和各关节的映射控制.