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。     

脑电信号的高阶奇异谱分析

奇异谱分析是脑电信号分析的一种新方法,脑电信号的奇异谱可以反映脑电的特征,它有助于研究大脑的动力学行为。奇异谱分析方法是基于二阶统计的方法,反映的是信号时间上和空间上的一种线性相关关系。而脑电信号属于非线性信号,其内在的非线性关系很难通过奇异谱得到真实的反映,从而会丢失某些有用的信息。提出一种新的基于高阶统计的脑电奇异谱分析方法,并将其运用于正常脑电和癫痫患者的脑电分析中。大量的实测信号样本仿真实验结果表明,正常脑电和癫痫脑电的奇异谱有明显的不同。此外,基于高阶统计的奇异谱和基于二阶统计的奇异谱相比更能反映出信号的细节。     

非线性动力学在脑电分析中的应用

非线性科学于20世纪60年代发展起来,被誉为20世纪自然科学的“第三次革命”,己广泛应用于生物、物理、经济、通讯及天文学等领域。脑电图(EEG)反映了作为非线性系统的大脑的电活动,体现出混沌行为。在癫痫病症的EEG研究中,混沌特性得到了很好的证明。在精神分裂症和老年痴呆等病症的EEG研究中,混沌的作用也体现得越来越明显。本文综述了近年来非线性动力学在脑电信号分析中应用的进展,以期获得在健康和疾病状态下对大脑神经动力系统的更好理解。     

复杂性与脑功能

EEG代表了大脑活动的一种电信号,但是用它来研究脑的功能活动是非常困难的.近年来由于非线性动力学的新发展,为我们提供了从一维EEG的时间序列提取脑的多维动力系统的信息,其中一个重要的方法是测量“关联维数”,但发现EEG是非平稳的混沌态,分维的知识只能给出系统的几何特征.而非平稳性表现出的是动态特性.因此我们对EEC的“复杂性”进行了研究,并与其它已知的标准的奇异吸引子做了比较.     

一种改进的近似熵——样品熵及其在颞叶癫痫患者脑电信号分析中的应用

采用了近似熵(approximately entropy,ApEn)和它的改进算法,即样品熵(sample entropy,SampEn)分析了8位颞叶癫痫患者和10位健康人员的短程脑电信号。在计算过程中使用了两种滑动窗口和5个不同的过滤标准r。结果显示颞叶癫痫患者组脑电信号的熵值显著低于健康组,而且患者癫痫病灶所在的脑半球的复杂度远远小于非癫痫病灶的脑半球。小的滑动窗口能更多地反映与癫痫发作相关的细节。对于1秒的滑动窗口,过滤标准r不能小于时间序列标准差的0．15％;而对于4秒的滑动窗口,则过滤标准r不能小于时间序列标准差的10％。研究结果表明,在短程脑电信号的非线性分析中,样品熵是一种比近似熵更为可靠的非线性分析方法。颞叶癫痫患者脑电信号的熵值低于健康人员,这可能表明脑电活动的非线性程度的降低是由于神经信号在大脑内的传递受到了阻碍或者损坏,使得神经信号成了相对孤立的信息源。     

基于多特征融合的多分类运动想象脑电信号识别研究

针对目前多分类运动想象脑电识别存在特征提取单一、分类准确率低等问题,提出一种多特征融合的四分类运动想象脑电识别方法来提高识别率。对预处理后的脑电信号分别使用希尔伯特-黄变换、一对多共空间模式、近似熵、模糊熵、样本熵提取结合时频—空域—非线性动力学的初始特征向量,用主成分分析降维,最后使用粒子群优化支持向量机分类。该算法通过对国际标准数据集BCI2005 Data set IIIa中的k3b受试者数据经MATLAB仿真处理后获得93.30%的识别率,均高于单一特征和其它组合特征下的识别率。分别对四名实验者实验采集运动想象脑电数据,使用本研究提出的方法处理获得了72.96%的平均识别率。结果表明多特征融合的特征提取方法能更好的表征运动想象脑电信号,使用粒子群支持向量机可取得较高的识别准确率,为人脑的认知活动提供了一种新的识别方法。     

小波神经网络在脑电信号数据压缩与棘波识别中的应用

介绍了一种新的神经网络模型———小波神经网络,利用它并适当调节网络、小波基参数,实现了对脑电信号的压缩表达,较好的恢复了原有信号。另外,在其算法研究的基础上,提出了适应于非稳态和非线性信号处理的时频分析新方法。在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,与传统的短时傅立叶变换（ＳＴＦＴ）和Ｗｉｇｎｅｒ分布相比,此方法有更高的分辨率和自适应性,而且其时频能量分布没有交叉项干扰。     

基于脑电信号识别采集的假肢控制系统

本文介绍了脑电信号（EEG）的模式识别和步骤,分析了EEG采集领域的发展和医学原理。通过研究脑电信号和假肢运动的联系,总结脑电控制假肢的可行性结论。设计出从头皮电极到模／数转换器的基于脑电信号识别采集的假肢控制系统,能够满足脑电假肢的各种要求。     

脑死亡与脑昏迷脑电信号的复杂度研究

脑死亡诊断是有关病人生死的重要问题.许多国家都把脑电平坦列为脑死亡诊断的基本条件,但研究发现并非所有的脑死亡患者均表现为脑电平坦,同时脑昏迷患者在部分情况下也会表现出脑电平坦的现象,从而有可能在临床中造成误判.C0复杂度判断指标能够利用脑电信号中的复杂度特性帮助临床诊断中对于脑死亡和脑昏迷状况的鉴别.运用C0复杂度算法对22位脑死亡和脑昏迷病例进行分析实验,可以发现脑死亡脑电信号的复杂度明显高于脑昏迷脑电信号的复杂度.实验表明C0复杂度可以用来有效地区分脑死亡和脑昏迷脑电信号,具有潜在的重要临床价值.     

双任务事件中脑电信号的熵计算

采用复杂性分析中的样品熵算法,计算并分析了受试者在单任务事件以及双任务事件活动过程中的神经电生理数据.在利用样品熵算法对短时程（秒）脑电数据的复杂度和规则度进行计算之前,首先应用了代替数据分析法,以排除所分析的实验数据是由线性加随机部分构成.所有的实验数据分别在单任务和双任务等不同的生理条件下采集.其中单任务为一个听觉辨别任务;双任务有两种形式,分别为听觉任务和不同的震动任务的结合.计算结果显示,任何一种双任务过程中脑电信号的熵值都明显的低于单任务状态时脑电信号的熵值（P＜0.05～0.001）.研究表明对应于受试者仅仅进行单任务工作而言,当受试者处于双任务工作状态时大脑的神经信息传递可能会受到某种程度的削弱,神经信息流通的范围也可能更为孤立.结果进一步说明对于短时程（秒）脑电信号分析,样品熵算法是有效的非线性分析方法.     

Organization of the cortical bioelectric activity at different stages of a relaxation session

The dynamics of electroencephalographic (EEG) parameters in adults were studied during active relaxation (which involved simple psychological techniques) and passive relaxation (without using any techniques). The experiment included four stages: background 1, active relaxation, passive relaxation, and background 2. During the experiment, the EEG was recorded in the occipital, parietal-temporal-occipital, central, and frontal regions of both hemispheres of the brain. Comparative analysis of the dynamics of EEG parameters during changing experimental conditions was performed in the frequency range from 5 to 40 Hz divided into ten frequency subranges. It was possible to establish the general pattern in the dynamics of EEG parameters at different relaxation stages, as well as differences in system-level brain performance in the active and passive relaxation states.     

脑电信息处理

脑电信息处理是脑功能研究重要组成部分。本文介绍了脑电信息处理的前沿领域,包括诱发电位、事件相关电位（ＥＲＰ）、正弦调制光（声）诱发脑电、４０ＨｚＥＲＰ和脑电非线笥动力学研究,并论及了认知活动与分形维数的关系。     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data

Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.     

The electrical activity of the monkey brain in the period of early transient incapacity during gamma-irradiation at high dose rates

Changes in the electroencephalogram (EEG) of Macaca fascicularis during early transient incapacitation (ETI) were shown to correlate with the dynamics of clinical manifestations of the damage. Irradiation caused desynchronization of EEG followed by a generalized retardation of its fluctuations reaching the maximum at the height of ETI. EEG disturbances in animals during the comatose phase of ETI indicated a severe inhibition of the brain cortex functional activity.     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

EEG correlates and possible predictors of the efficacy of the treatment of endogenous depression

Analysis of EEG spectral power values and quantitative clinical scores of depressive conditions has been carried out in the dynamics of the treatment of 40 patients with endogenous depression, with the main goal to study the neurophysiological correlates and to search for possible predictors of therapeutic outcome. The reduction of depressive symptoms by the end of the treatment course was associated with EEG signs of improvement of the brain’s functional state. Significant correlations have been revealed between the EEG narrow-band spectral power values and clinical scores. As well, significant correlations have been revealed between some initial (before the beginning of the treatment) EEG parameters and quantitative clinical scores at the initial stage of remission. The values of EEG β₁ and β₂ spectral power appeared to be predictors, while initially larger values of EEG spectral power were associated with the high manifestation of residual depressive symptoms after the treatment. The results support the basic views on the brain’s mechanisms of various aspects of depressive disorders and reveal the possible neurophysiological predictors of the efficacy of the treatment of endogenous depression.     

Down syndrome’s brain dynamics: analysis of fractality in resting state

To the best knowledge of the authors there is no study on nonlinear brain dynamics of down syndrome (DS) patients, whereas brain is a highly complex and nonlinear system. In this study, fractal dimension of EEG, as a key characteristic of brain dynamics, showing irregularity and complexity of brain dynamics, was used for evaluation of the dynamical changes in the DS brain. The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an inverse pattern (left frontal laterality). Furthermore, the high accuracy of 95.8 % obtained by enhanced probabilistic neural network classifier showed the potential of nonlinear dynamic analysis of the brain for diagnosis of DS patients. Moreover, the results showed that the higher EEG fractality in DS is associated with the higher fractality in the low frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the frontal regions.     

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain’s multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new –ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG’s applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.     

The brain timewise: how timing shapes and supports brain function

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.