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.     

低维非线性呼吸系统的复杂性计算

首先应用混沌计算方法：关联维数D2和最大李亚普努夫指数LLE,以及替代数据分析法,分析了男性受试者在平静状态下记录的呼吸系统时间序列的混沌特征。同时,在呼吸变量的非线性分析中首次引进了被称为C0复杂度的新技术．它的应用将有助于更好地理解自主神经系统中潜在的生理过程。LLE计算的替代数据法分析结果显示,没有明确的证据可以证实受试者在平静状态下的呼吸时序的模态是混沌的。然而,C0复杂度的计算结果却表明大部分呼吸系统的时间序列表现为某种程度的复杂性,这为呼吸模态的非随机变化的属性提供了部分的实验和计算证据。更进一步,C0复杂度有可能以一种新的、确定的方式给出呼吸系统在激励状态下的量化改变。     

混沌在神经系统中的作用

随着非线性动力学的发展,发现神经的不规则电活动具有确定混沌特性。混沌广泛地存在于神经系统,神经元的混沌电活动对神经元的生理功能必不可少,服电的混沌活动特性与大脑的功能状态密切相关,在大脑正常状态下脑电混沌活动的维数、李雅普指数、复杂度等指标较高;而在服功能受损的病理状态下,上述混沌指标降低。混沌在神经系统中起着重要的作用。     

豚鼠背侧海马锥体细胞自发放电特征

实验采用细胞外玻璃微电极采集豚鼠海马神经元放电信号,并将信号转化为峰峰间期(interspike interval,ISI)以研究麻醉和清醒状态海马锥体细胞自发放电线性和非线性特点。实验建立了豚鼠海马锥体细胞与中间神经元电生理鉴别标准;麻醉和清醒状态下豚鼠海马CA1和CA3区锥体细胞自发放电频率、时程、复杂度等无显著区别;麻醉组豚鼠海马锥体细胞ISI序列的复杂度小于清醒组,锥体细胞分型和ISI变异度等表现不同。实验表明,麻醉和清醒状态下豚鼠海马锥体细胞自发放电呈不同线性和非线性特征。传统和非线性研究手段的结合,可能较全面地反映海马锥体细胞自发放电特性。     

脑电图复杂度分析中的粗粒化问题II.量化对复杂度计算的影响

在对生物医学信号时间序列进行复杂度分析时,粗粒化预处理有可能会造成丢失原始信号中所蕴含的信息,甚至在某些情况下根本改变原信号的动力学性质.用计算机计算时的量化过程也是一种粗粒化,因此也有这类问题.通过对近似熵和我们所定义的C0复杂度这两种复杂度在不同量化精度下对一些典型时间序列复杂度分析的比较研究,发现一般说来量化精度对复杂度分析的影响不是很大,仅当对原始信号进行二值化等极端情况下,才会显著改变原信号的复杂性.对脑电信号进行计算表明上述结论是实际可取的.     

复杂度脑电地形图研究

脑电地形图是近年脑电分析的热点之一。通过对各种复杂度算法的分析得出,近似熵由于所需要的时间序列长度较短,大大减少了脑电非平稳性所带来的困难,且无需粗粒化,在对生物医学信号的复杂度分析中有其一定的优点,采用近似熵对多道脑电信号的复杂度运算结果,通过空间插值,构建复杂性动态脑地形图,以便于观察大脑各部ＥＥＧ信号复杂度在同一时刻的相对强弱关系和这种关系随时间的变化。并通过对一些脑疾病患者脑电数据的分析,     

静息态fMRI在神经、精神类疾病中的研究进展

静息态功能磁共振(resting-state functional MRI,rs-fMRI)是近年出现的一种新的fMRI技术,它通过研究大脑静息状态的自发活动来反映复杂的人脑活动状态,可为我们探索大脑活动的内部机制提供新的途径。由于它无需复杂的任务实验,近年来被广泛用于神经、精神类疾病的基础与临床研究。本文就近年来国内外对静息态脑功能磁共振在神经、精神类疾病中的研究做一综述。     

一种新的人脑信息传输复杂性的研究

我们在过去的工作中曾用Ｋｏｌｍｏｇｒｏｖ复杂性（简称ＫＣ）来研究人脑信息传输的过程。但计算ＫＣ时需要对序列作粗粒化。粗粒化过程很容易丢失许多有意义的细节。为此,本文提出一种新的复杂性测度,即Ｃ０复杂性。我们认为复杂运动时间序列是由规则运动部分时间序列及随机运动部分时间序列组成的,因此Ｃ０复杂性就被定义为随机运动部分时序与时间轴所围区域的面积与整个复杂运动时序与时间轴所围区域面积之比。Ｃ０复杂性测度在计算过程中避免了粗粒化,因而较之ＫＣ等复杂性测度能更好反映序列的动力学性质。计算Ｃ０复杂性的关键是怎样从一复杂运动时序中找到规则部分时间序列。     

局限性癫痫脑电时间序列的三种复杂度计算比较

为探索非线性动力学复杂性测度诊断癫痫病的新方法,对局限性癫痫病患者脑电时间序列进行了三种复杂度（Ｋｃ、Ｃ１、Ｃ２）的计算比较。观察到,痫性导联脑电的三种复杂度多低于对侧导联的值;复杂度Ｋｃ的相对变化量较Ｃ１和Ｃ２大;复杂度Ｋｃ和Ｃ１的变化趋势相似,而复杂度Ｃ２的变化趋势与复杂度Ｋｃ和Ｃ１的规律不尽相同;但正常人ＥＥＧ信号的复杂度没有这种显著变化。结果提示,脑电复杂性测度有可能成为诊断癫痫的特征参数,值得进一步深入研究。     

应用小波熵分析大鼠脑电信号的动态变化特性

应用小波熵（一种新的信号复杂度测量方法）分析大鼠在不同生理状态下脑电复杂度的动态时变特性。采用慢性埋植电极记录自由活动大鼠的皮层EEG,使用多分辨率小波变换将EEG信号分解为δ、θ、α和β四个分量,求得随时间变化的小波熵。结果表明：在清醒、慢波睡眠和快动眼睡眠三种生理状态下,EEG的小波熵之间存在显著差别,并且在不同时期其值与各个分解分量之间具有不同的关系,其中,慢波睡眠期小波熵还具有较明显的变化节律,反映了EEG微状态中慢波和纺锤波的互补性。由此可见,小波熵既能区别长时间段EEG复杂度之间的差别,又能反映EEG微状态的快速变化特性。     

Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs

The dynamic behavior of scalp potentials (EEG) is apparently due to some combination of global and local processes with important top-down and bottom-up interactions across spatial scales. In treating global mechanisms, we stress the importance of myelinated axon propagation delays and periodic boundary conditions in the cortical-white matter system, which is topologically close to a spherical shell. By contrast, the proposed local mechanisms are multiscale interactions between cortical columns via short-ranged non-myelinated fibers. A mechanical model consisting of a stretched string with attached nonlinear springs demonstrates the general idea. The string produces standing waves analogous to large-scale coherent EEG observed in some brain states. The attached springs are analogous to the smaller (mesoscopic) scale columnar dynamics. Generally, we expect string displacement and EEG at all scales to result from both global and local phenomena. A statistical mechanics of neocortical interactions (SMNI) calculates oscillatory behavior consistent with typical EEG, within columns, between neighboring columns via short-ranged non-myelinated fibers, across cortical regions via myelinated fibers, and also derives a string equation consistent with the global EEG model.     

Electric Field Encephalography as a Tool for Functional Brain Research: A Modeling Study

We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2–3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.     

脑电图机电极与头皮接触阻抗的检测

脑电图机中电极与头皮接触的好坏对脑电波形质量有很大影响，本文利用AT89C51单片机实现电极与头皮接触阻抗的检测。还通过发光二极管给予医务人员对电极接触好坏直观的指示。     

Human gamma oscillations during slow wave sleep

Neocortical local field potentials have shown that gamma oscillations occur spontaneously during slow-wave sleep (SWS). At the macroscopic EEG level in the human brain, no evidences were reported so far. In this study, by using simultaneous scalp and intracranial EEG recordings in 20 epileptic subjects, we examined gamma oscillations in cerebral cortex during SWS. We report that gamma oscillations in low (30-50 Hz) and high (60-120 Hz) frequency bands recurrently emerged in all investigated regions and their amplitudes coincided with specific phases of the cortical slow wave. In most of the cases, multiple oscillatory bursts in different frequency bands from 30 to 120 Hz were correlated with positive peaks of scalp slow waves ("IN-phase" pattern), confirming previous animal findings. In addition, we report another gamma pattern that appears preferentially during the negative phase of the slow wave ("ANTI-phase" pattern). This new pattern presented dominant peaks in the high gamma range and was preferentially expressed in the temporal cortex. Finally, we found that the spatial coherence between cortical sites exhibiting gamma activities was local and fell off quickly when computed between distant sites. Overall, these results provide the first human evidences that gamma oscillations can be observed in macroscopic EEG recordings during sleep. They support the concept that these high-frequency activities might be associated with phasic increases of neural activity during slow oscillations. Such patterned activity in the sleeping brain could play a role in off-line processing of cortical networks.     

Measures of entropy and complexity in altered states of consciousness

Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies have revealed a dependence on different states of consciousness, and in particular that wakefulness is characterized by a greater complexity of brain signals, perhaps due to the necessity for the brain to handle varied sensorimotor information. Thus, these frameworks are very useful in attempts to quantify cognitive states. We set out to analyze different types of signals obtained from scalp electroencephalography (EEG), intracranial EEG and magnetoencephalography recording in subjects during different states of consciousness: resting wakefulness, different sleep stages and epileptic seizures. The signals were analyzed using a statistical (permutation entropy) and a deterministic (permutation Lempel–Ziv complexity) analytical method. The results are presented in complexity versus entropy graphs, showing that the values of entropy and complexity of the signals tend to be greatest when the subjects are in fully alert states, falling in states with loss of awareness or consciousness. These findings were robust for all three types of recordings. We propose that the investigation of the structure of cognition using the frameworks of complexity will reveal mechanistic aspects of brain dynamics associated not only with altered states of consciousness but also with normal and pathological conditions.     

Modelling spatial landscape complexity using the Levenshtein algorithm

A new measure (C_L) of spatial/structural landscape complexity is developed in this paper, based on the Levenshtein algorithm used in Computer Science and Bioinformatics for string comparisons. The Levenshtein distance (or edit distance) between two strings of symbols is the minimum of all possible replacements, deletions and insertions necessary to convert one string into the other. In this paper, it is shown how this measure can be applicable on raster landscape maps of any size or shape. Calculations and applications are shown on model and real landscapes. The main advantages of this measure for structural (spatial) landscape analysis are the following: it is easily applicable; it can be compared to its maximum value (depending on the grid resolution); it can be used to compare structural/spatial complexities between landscapes; it is applicable to raster landscape maps of any shape; and it can be used to calculate changes in landscape complexity over time. At the level of ecological practice, it may aid in landscape monitoring, management and planning, by identifying areas of higher structural landscape complexity, which may deserve greater attention in the process of landscape conservation.     

Use of the fractal dimension for the analysis of electroencephalographic time series

 Electroencephalogram (EEG) traces corresponding to different physiopathological conditions can be characterized by their fractal dimension, which is a measure of the signal complexity. Generally this dimension is evaluated in the phase space by means of the attractor dimension or other correlated parameters. Nevertheless, to obtain reliable values, long duration intervals are needed and consequently only long-term events can be analysed; also much calculation time is required. To analyse events of brief duration in real-time mode and to apply the results obtained directly in the time domain, thus providing an easier interpretation of fractal dimension behaviour, in this work we optimize and propose a new method for evaluating the fractal dimension. Moreover, we study the robustness of this evaluation in the presence of white or line noises and compare the results with those obtained with conventional spectral methods. The non-linear analysis carried out allows us to investigate relevant EEG events shorter than those detectable by means of other linear and non-linear techniques, thus achieving a better temporal resolution. An interesting link between the spectral distribution and the fractal dimension value is also pointed out. Received: 21 November 1996 / Accepted in revised form: 1 July 1997     

Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation (tACS)

Oscillatory brain activities are considered to reflect the basis of rhythmic changes in transmission efficacy across brain networks and are assumed to integrate cognitive neural processes. Transcranial alternating current stimulation (tACS) holds the promise to elucidate the causal link between specific frequencies of oscillatory brain activity and cognitive processes. Simultaneous electroencephalography (EEG) recording during tACS would offer an opportunity to directly explore immediate neurophysiological effects of tACS. However, it is not trivial to measure EEG signals during tACS, as tACS creates a huge artifact in EEG data. Here we explain how to set up concurrent tACS-EEG experiments. Two necessary considerations for successful EEG recording while applying tACS are highlighted. First, bridging of the tACS and EEG electrodes via leaking EEG gel immediately saturates the EEG amplifier. To avoid bridging via gel, the viscosity of the EEG gel is the most important parameter. The EEG gel must be viscous to avoid bridging, but at the same time sufficiently fluid to create contact between the tACS electrode and the scalp. Second, due to the large amplitude of the tACS artifact, it is important to consider using an EEG system with a high resolution analog-to-digital (A/D) converter. In particular, the magnitude of the tACS artifact can exceed 100 mV at the vicinity of a stimulation electrode when 1 mA tACS is applied. The resolution of the A/D converter is of importance to measure good quality EEG data from the vicinity of the stimulation site. By following these guidelines for the procedures and technical considerations, successful concurrent EEG recording during tACS will be realized.     

Nonlinear dynamics of the EEG separated by independent component analysis after sound and light stimulation

 The electroencephalogram (EEG) is a multiscaled signal consisting of several time-series components each with different dominant frequency ranges and different origins. Nonlinear measures of the EEG reflect the complexity of the overall EEG, but not of each component in it. The aim of this study is to examine effect of the sound and light (SL) stimulation on the complexity of each component of the EEG. We used independent component analysis to obtain independent components of the EEG. The first positive Lyapunov exponent (L1) was estimated as a nonlinear measure of complexity. Twelve subjects were administered photic and auditory stimuli with a frequency of 10 Hz, which corresponded to the alpha frequency of the EEG, by a sound and light entrainment device. We compared the L1 values of the EEGs and their independent components between baseline and after the SL stimulation. We detected that the L1 values of the EEG decreased after the SL stimulation in all channels except C3 and F4, indicating that the complexity of the EEG decreased. We showed that alpha components increased in proportion but decreased in complexity after the SL stimulation. The beta independent components were found to decrease in proportion and complexity. These results suggest that decreased complexity of the EEG after the SL stimulation may be principally caused by decreased complexity and increased proportion of the alpha independent components. We showed also that theta components increased in complexity after the SL stimulation. We propose that nonlinear dynamical analysis combined with independent component analysis may be helpful in understanding the temporal characteristics of the EEG, which cannot be detected by conventional linear or nonlinear methods. Received: 12 March 2001 / Accepted in revised form: 27 November 2001