小波和主分量分析方法研究思维脑电

研究自发脑电和思维活动的关系．利用小波和主分量分析结合的WPCA算法对不同思维任务记录的六导脑电进行处理,并对思维特征的频谱能量和变化率等多指标进行综合分析和计算。结果表明WPCA算法不仅可以实现噪声的去除,而且能提高主分量的贡献率,降低输入矢量的维数。对脑电主分量的分析揭示了脑电与思维个体、思维种类、复杂度以及注意力的联系,思维任务的神经网络分类结果验证了WPCA方法研究脑电和思维的有效性,为进一步理解认知和思维过程,实现对思维的定位和分类提供了依据。     

基于流形学习的基因表达谱数据可视化

基因表达谱的可视化本质上是高维数据的降维问题。采用流形学习算法来解决基因表达谱的降维数据可视化,讨论了典型的流形学习算法（Isomap和LLE）在表达谱降维中的适用性。通过类内／类间距离定量评价数据降维的效果,对两个典型基因芯片数据集（结肠癌基因表达谱数据集和急性白血病基因表达谱数据集）进行降维分析,发现两个数据集的本征维数都低于3,因而可以用流形学习方法在低维投影空间中进行可视化。与传统的降维方法（如PCA和MDS）的投影结果作比较,显示Isomap流形学习方法有更好的可视化效果。     

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。     

广西英罗港红树植物群落的非线性排序

采用主分量分析 (PCA)、无偏主分量 (DPC)和非度量多维调节 (NMDS)等方法对广西英罗港 2 2个红树植物群落样地进行了排序。PCA和 DPC分析结果表明 ,取样数据具有明显的非线性结构。通过 NMDS分析 ,得到二维 NMDS排序格局 ,它能较好地反映了红树植物群落与环境因子之间的相互关系。     

基于拉曼光谱和化学计量学方法判别大米分类的研究

本文利用拉曼光谱和化学计量学方法,建立快速分类模型对大米进行区分。在使用最小二乘法对离散拉曼光谱进行多项式拟合去除荧光背景的前提下,利用在第一次迭代过程去除大型拉曼峰和计算噪声电平的方法,并且保留数据维数在原来的50%以下。获取精确的拉曼信号。再用主成分分析法(Principal component Analysis,PCA)对3种大米全波段的拉曼光谱进行降维分析,线性判别方法 (Linear discrimination analysis,LDA)对样品进行分类,结果显示采用前两个主成分能达到93.8%的正确分类,采用前三个主成分能达到97.9%的正确分类。优化之后的模型对于大米的判别分析具有很好的效果。     

江西省自然保护区的排序研究

本文运用主分量分析的方法,分析了江西森林自然保护区的自然属性的地理分布规律,旨在从中找到一组可靠的自然属性,以此预测建立森林自然保护区的合适之地,并对已建立的5个省级自然保护区进行分布合理性评价。研究表明：前三维主分量的信息量占总信息量的87．73％,降维效果良好。通过对不同自然属性在前三维主分量上的负荷量以及它们之间的离散性分析,确定了12个自然属性是评价和预测自然保护区的有效因子。从而证明 PCA 在自然保护区的规划和研究中具有良好的适应性。     

应用非负矩阵分解方法提取注意力相关脑电特征

介绍了非负矩阵分解算法(NMF)的基本原理,给出一种利用NMF进行脑电能量谱特征提取的方法。设计试验对10个被试在三种不同注意任务中的脑电信号进行特征提取,并采用人工神经网络作为分类器进行分类测试。结果表明,NMF算法在高维特征空间具有较强的特征选择能力,其分类正确率明显高于主分量分析(PCA)方法和直接法,三种意识任务的分类正确率分别达到84.5、88%和86.5。     

山西霍山森林群落的排序

运用主分量分析法对霍山东、西两坡的１０５个样方进行了排序。前３个主分量占总信息量的７０％左右。降维效果良好。排序图中,样方间距离抽象地表达了群落间的生态学相关程度。据此将所作样方划分为若干群系,分析了群落随环境梯度变化的规律、观测了群落演替的趋向。从而证明ＰＣＡ法在该区森林群落研究中具有良好的适应性。     

四川江津四面山常绿阔叶林永久样地的非线性排序

 本文以四川省江津四面山1 ha永久样地的常绿阔叶林为研究对象,利用样地内20个样方优势乔木种的重要值数据,采用主分量分析(PCA),无偏主分量分析(DPC)和非度量多维调节(NMDS)等方法进行分类和排序。PCA分析结果发现样方坐标数据具有非线性,DPC检测证实其非线性相当明显。通过NMDS分析,经过39次反复迭代,获得结果清楚地反映了群落优势种与环境因子的相互关系,为以后的次生演替研究提供了基础资料。     

PCA 和PLS 应用于胃癌亚型分类研究

文章研究了基于微阵列基因表达数据的胃癌亚型分类。微阵列基因表达数据样本少、纬度高、噪声大的特点,使得数据降维成为分类成功的关键。作者将主成分分析(PCA) 和偏最小二乘（PLS）两种降维方法应用于胃癌亚型分类研究,以支持向量机（SVM）、K- 近邻法（KNN）为分类器对两套胃癌数据进行亚型分类。分类效果相比传统的医理诊断略高,最高准确率可达100%。研究结果表明,主成分分析和偏最小二乘方法能够有效地提取分类特征信息,并能在保持较高的分类准确率的前提下大幅度地降低基因表达数据的维数。     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

EEG动力学模型中混沌现象的研究

通过对脑电图（electroencephalogram,EEG）动力学模型模拟出的EEG信号的相图、分岔图、功率谱、关联维数和Lyapunov指数的对比研究,得出如下结论：1）该模型是按周期行为与混沌现象交替出现的间歇突发通向混沌的,且该间歇性与Hopf分岔、倍周期分岔和逆分岔有关;2）支持了EEG中存在混沌运动的观点。     

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.     

Complex networks and deep learning for EEG signal analysis

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human’s physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain–computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.     

The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part I-measuring techniques.

The purpose of this study is to examine whether or not the application of independent component analysis (ICA) is useful for separation of motor unit action potential trains (MUAPTs) from the multi-channel surface EMG (sEMG) signals. In this study, the eight-channel sEMG signals were recorded from tibialis anterior muscles during isometric dorsi-flexions at 5%, 10%, 15% and 20% maximal voluntary contraction. Recording MUAP waveforms with little time delay mounted between the channels were obtained by vertical sEMG channel arrangements to muscle fibers. The independent components estimated by FastICA were compared with the sEMG signals and the principal components calculated by principal component analysis (PCA). From our results, it was shown that FastICA could separate groups of similar MUAP waveforms of the sEMG signals separated into each independent component while PCA could not sufficiently separate the groups into the principal components. A greater reduction of interferences between different MUAP waveforms was demonstrated by the use of FastICA. Therefore, it is suggested that FastICA could provide much better discrimination of the properties of MUAPTs for sEMG signal decomposition, i.e. waveforms, discharge intervals, etc., than not only PCA but also the original sEMG signals.     

Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain

Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.     

Uncovering chaotic structure in mechanomyography signals of fatigue biceps brachii muscle

The mechanomyography (MMG) signal reflects mechanical properties of limb muscles that undergo complex phenomena in different functional states. We undertook the study of the chaotic nature of MMG signals by referring to recent developments in the field of nonlinear dynamics. MMG signals were measured from the biceps brachii muscle of 5 subjects during fatigue of isometric contraction at 80% maximal voluntary contraction (MVC) level. Deterministic chaotic character was detected in all data by using the Volterra–Wiener–Korenberg model and noise titration approach. The noise limit, a power indicator of the chaos of fatigue MMG signals, was 22.20±8.73. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D₂, which was 3.35±0.36 across subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for analysis and modeling of fatigue MMG signals.     

Nonlinearity in normal human EEG: cycles,temporal asymmetry,nonstationarity and randomness,not chaos

Two-hour vigilance and sleep electroencephalogram (EEG) recordings from five healthy volunteers were analyzed using a method for identifying nonlinearity and chaos which combines the redundancy–linear redundancy approach with the surrogate data technique. A nonlinear component in the EEG was detected, however, inconsistent with the hypothesis of low-dimensional chaos. A possibility that a temporally asymmetric process may underlie or influence the EEG dynamics was indicated. A process that merges nonstationary nonlinear deterministic oscillations with randomness is proposed for an explanation of observed properties of the analyzed EEG signals. Taking these results into consideration, the use of dimensional and related chaos-based algorithms in quantitative EEG analysis is critically discussed. Received: 25 September 1994 / Accepted in revised form: 10 July 1996     

Automatic analysis of sleep using two parameters based on principal component analysis of electroencephalography spectral data

A computer program for the analysis of a sleep electroencephalogram (EEG) is presented. The method relies on two steps. First, a spectral analysis is performed for signals recorded from one or more electrode locations. Then, two EEG parameters are obtained by storing the spectral activity in a multidimensional space, whose dimension is reduced using principal component analysis (PCA) techniques. The main advantage of these parameters is in describing the process of sleep on a continuous scale as a function of time. Validation of the method was performed with the data collected from 16 subjects (8 young volunteers and 8 elderly insomniacs). Results snowed that the parameters correlate highly with the hypnograms established by conventional visual scoring. This signal parametrisation, however, offers more information regarding the time course of sleep, since small variations within individual sleep stages as well as smooth transitions between stages are assessed. Finally, the concurrent use of both parameters provides an original way of considering sleep as a dynamic process evolving cyclically in a single plane.