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Sample records for nonlinear eeg analysis

  1. Nonlinear dynamics and quantitative EEG analysis.

    Science.gov (United States)

    Jansen, B H

    1996-01-01

    Quantitative, computerized electroencephalogram (EEG) analysis appears to be based on a phenomenological approach to EEG interpretation, and is primarily rooted in linear systems theory. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of nonlinear dynamics and chaos theory, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing nonlinearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. A review of the fundamentals of chaos theory is provided. Evidence supporting the nonlinear dynamics paradigm to EEG interpretation is presented, and the kind of new information that can be extracted from the EEG is discussed. A case is made that a nonlinear dynamic systems viewpoint to EEG generation will profoundly affect the way EEG interpretation is currently done.

  2. Nonlinear analysis of EEG for epileptic seizures

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    Hively, L.M.; Clapp, N.E.; Daw, C.S.; Lawkins, W.F. [Oak Ridge National Lab., TN (United States); Eisenstadt, M.L. [Knoxville Neurology Clinic, St. Mary`s Medical Center, Knoxville, TN (United States)

    1995-04-01

    We apply chaotic time series analysis (CTSA) to human electroencephalogram (EEG) data. Three epoches were examined: epileptic seizure, non-seizure, and transition from non-seizure to seizure. The CTSA tools were applied to four forms of these data: raw EEG data (e-data), artifact data (f-data) via application of a quadratic zero-phase filter of the raw data, artifact-filtered data (g- data) and that was the residual after subtracting f-data from e-data, and a low-pass-filtered version (h-data) of g-data. Two different seizures were analyzed for the same patient. Several nonlinear measures uniquely indicate an epileptic seizure in both cases, including an abrupt decrease in the time per wave cycle in f-data, an abrupt increase in the Kolmogorov entropy and in the correlation dimension for e-h data, and an abrupt increase in the correlation dimension for e-h data. The transition from normal to seizure state also is characterized by distinctly different trends in the nonlinear measures for each seizure and may be potential seizure predictors for this patient. Surrogate analysis of e-data shows that statistically significant nonlinear structure is present during the non-seizure, transition , and seizure epoches.

  3. Nonlinear analysis of the alcoholic's EEG

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The nonlinear analysis is used to study the EEGs of the alcoholic and the control. Three kinds of expansions are discussed in order to get a more proper delay and then Liangyue Cao algorithm is implemented efficiently. Totally, there are 40 subjects involved in this study and the average values and the sample variances of D2s are computed. The results show that the average value of D2s of the alcoholic is larger than that of the control when the same electrode was used, which means that the brain dynamics of the alcoholic is more complex than that of the control. On the other hand, for most of the electrodes, the sample variance of D2s of the alcoholic is larger than that of the control, suggesting that the brain dynamics of the alcoholic is less steady.

  4. Non-linear analysis of EEG signals at various sleep stages.

    Science.gov (United States)

    Acharya U, Rajendra; Faust, Oliver; Kannathal, N; Chua, TjiLeng; Laxminarayan, Swamy

    2005-10-01

    Application of non-linear dynamics methods to the physiological sciences demonstrated that non-linear models are useful for understanding complex physiological phenomena such as abrupt transitions and chaotic behavior. Sleep stages and sustained fluctuations of autonomic functions such as temperature, blood pressure, electroencephalogram (EEG), etc., can be described as a chaotic process. The EEG signals are highly subjective and the information about the various states may appear at random in the time scale. Therefore, EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The sleep data analysis is carried out using non-linear parameters: correlation dimension, fractal dimension, largest Lyapunov entropy, approximate entropy, Hurst exponent, phase space plot and recurrence plots. These non-linear parameters quantify the cortical function at different sleep stages and the results are tabulated.

  5. Nonlinear Analysis of Clinical Epileptic EEG by Approximate Entropy

    Institute of Scientific and Technical Information of China (English)

    LIU Yan-su; XIA Yang; XU Hong-ru; ZHOU Dong; YAO De-zhong

    2005-01-01

    By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0. 002). Meanwhile, dynamic ApEn shows consistent change of EEG signal withdischarges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism.

  6. Non-linear analysis of EEG and HRV signals during sleep.

    Science.gov (United States)

    Martin, Alejandro; Guerrero-Mora, Guillermina; Dorantes-Méndez, Guadalupe; Alba, Alfonso; Méndez, Martin O; Chouvarda, Ioanna

    2015-01-01

    The sleep phenomenon is a complex process that involves fluctuations of autonomic functions such as the blood pressure, temperature and brain function. These fluctuations change their properties through the different sleep stages with specific relations among the different systems. In order to understand the relation between the cardiovascular and central nervous system at the different sleep stages, we applied different non-linear methods to the energy of electroencephalographic signal (EEG) and the heart rate fluctuations. The EEG was divided in the Delta, Theta, Alpha and Beta frequency bands and the mean energy of these bands was computed at each heart rate interval. Thus, the non-linear relation was evaluated between the energy of the EEG bands and the heart rate fluctuations using Cross-Correlation, Cross-Sample Entropy and Recurrence Quantification Analysis in segments of 5 minutes grouped by sleep stage. The results showed that a relation exists between the changes of the energy in the Delta band and the Heart rate fluctuations.

  7. Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

    Science.gov (United States)

    Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

    2017-02-01

    Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

  8. Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

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    Cauchy Pradhan

    2012-01-01

    Full Text Available The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

  9. Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals.

    Science.gov (United States)

    Lainscsek, Claudia; Hernandez, Manuel E; Weyhenmeyer, Jonathan; Sejnowski, Terrence J; Poizner, Howard

    2013-01-01

    The pathophysiology of Parkinson's disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve A'. In a companion paper, we show that DDEs can uncover hidden dynamical structure from short segments of simulated time series of known dynamical systems in high noise regimes. Using the same method for finding the best models, we found here that even short segments of EEG data in PD patients and controls contained dynamical structure, and moreover, that PD patients exhibited a greater dynamic range than controls. DDE model output on the means from one set of 50 trials provided nearly complete separation of PD patients off medication from controls: across brain regions, the area under the receiver-operating characteristic curves, A', varied from 0.95 to 1.0. For distinguishing PD patients on vs. off medication, classification performance A' ranged from 0.86 to 1.0 across brain regions. Moreover, the generalizability of the model to the second set of 50

  10. Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

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    Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

    2017-09-08

    EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

  11. A nonlinear perspective in understanding the neurodynamics of EEG.

    Science.gov (United States)

    Pradhan, N; Dutt, D N

    1993-11-01

    The developments in nonlinear dynamics and the theory of chaos have considerably altered our perception and analysis of many complex systems, including the brain. This paper reviews the physical and dynamical aspect of brain's electrical activity from this new perspective and indicates possible future directions. The importance of emerging trends of nonlinear dynamics and chaos to neurobiology has been discussed in the context of various states of consciousness and behaviour. In the past, EEG analysis has been confined to descriptive stochastic statistics and any understanding of the transitional process of brain activities was either nonexistent or not amenable for investigation. With the developments in nonlinear dynamics, the chaotic dynamical parameters and trajectory behaviour will find their use as feature detection techniques in EEG. Furthermore, nonlinear dynamics provides a model for EEG generation and temporal prediction which will help in determining the nature of neuronal processes governing various states of brain activity. The formalism of globally coupled dynamic systems will find applications in modelling the transitional states of EEG.

  12. Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

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    Nurujjaman, Md; Iyengar, A N Sekar

    2007-01-01

    Electroencephalography (EEG) for a normal person is different from an epileptic patient. We have studied EEG of normal men and epileptic patients for different mental conditions using nonlinear techniques like the surrogate analysis and the estimations of the correlation dimensions and Hurst exponents.

  13. Distribution entropy analysis of epileptic EEG signals.

    Science.gov (United States)

    Li, Peng; Yan, Chang; Karmakar, Chandan; Liu, Changchun

    2015-01-01

    It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

  14. Epileptic EEG classification based on extreme learning machine and nonlinear features.

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    Yuan, Qi; Zhou, Weidong; Li, Shufang; Cai, Dongmei

    2011-09-01

    The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities. Nonlinear features extracted from EEG signals such as approximate entropy (ApEn), Hurst exponent and scaling exponent obtained with detrended fluctuation analysis (DFA) are employed to characterize interictal and ictal EEGs. The statistics indicate that the differences of those nonlinear features between interictal and ictal EEGs are statistically significant. The ELM algorithm is employed to train a single hidden layer feedforward neural network (SLFN) with EEG nonlinear features. The experiments demonstrate that compared with the backpropagation (BP) algorithm and support vector machine (SVM), the performance of the ELM is better in terms of training time and classification accuracy which achieves a satisfying recognition accuracy of 96.5% for interictal and ictal EEG signals. Copyright © 2011 Elsevier B.V. All rights reserved.

  15. EEG signal analysis: a survey.

    Science.gov (United States)

    Subha, D Puthankattil; Joseph, Paul K; Acharya U, Rajendra; Lim, Choo Min

    2010-04-01

    The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.

  16. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  17. Fractal Dimension in Epileptic EEG Signal Analysis

    Science.gov (United States)

    Uthayakumar, R.

    Fractal Analysis is the well developed theory in the data analysis of non-linear time series. Especially Fractal Dimension is a powerful mathematical tool for modeling many physical and biological time signals with high complexity and irregularity. Fractal dimension is a suitable tool for analyzing the nonlinear behaviour and state of the many chaotic systems. Particularly in analysis of chaotic time series such as electroencephalograms (EEG), this feature has been used to identify and distinguish specific states of physiological function.Epilepsy is the main fatal neurological disorder in our brain, which is analyzed by the biomedical signal called Electroencephalogram (EEG). The detection of Epileptic seizures in the EEG Signals is an important tool in the diagnosis of epilepsy. So we made an attempt to analyze the EEG in depth for knowing the mystery of human consciousness. EEG has more fluctuations recorded from the human brain due to the spontaneous electrical activity. Hence EEG Signals are represented as Fractal Time Series.The algorithms of fractal dimension methods have weak ability to the estimation of complexity in the irregular graphs. Divider method is widely used to obtain the fractal dimension of curves embedded into a 2-dimensional space. The major problem is choosing initial and final step length of dividers. We propose a new algorithm based on the size measure relationship (SMR) method, quantifying the dimensional behaviour of irregular rectifiable graphs with minimum time complexity. The evidence for the suitability (equality with the nature of dimension) of the algorithm is illustrated graphically.We would like to demonstrate the criterion for the selection of dividers (minimum and maximum value) in the calculation of fractal dimension of the irregular curves with minimum time complexity. For that we design a new method of computing fractal dimension (FD) of biomedical waveforms. Compared to Higuchi's algorithm, advantages of this method include

  18. Epileptic EEG: a comprehensive study of nonlinear behavior.

    Science.gov (United States)

    Daneshyari, Moayed; Kamkar, L Lily; Daneshyari, Matin

    2010-01-01

    In this study, the nonlinear properties of the electroencephalograph (EEG) signals are investigated by comparing two sets of EEG, one set for epileptic and another set for healthy brain activities. Adopting measures of nonlinear theory such as Lyapunov exponent, correlation dimension, Hurst exponent, fractal dimension, and Kolmogorov entropy, the chaotic behavior of these two sets is quantitatively computed. The statistics for the two groups of all measures demonstrate the differences between the normal healthy group and epileptic one. The statistical results along with phase-space diagram verify that brain under epileptic seizures possess limited trajectory in the state space than in healthy normal state, consequently behaves less chaotically compared to normal condition.

  19. Nonlinear analysis

    CERN Document Server

    Nanda, Sudarsan

    2013-01-01

    "Nonlinear analysis" presents recent developments in calculus in Banach space, convex sets, convex functions, best approximation, fixed point theorems, nonlinear operators, variational inequality, complementary problem and semi-inner-product spaces. Nonlinear Analysis has become important and useful in the present days because many real world problems are nonlinear, nonconvex and nonsmooth in nature. Although basic concepts have been presented here but many results presented have not appeared in any book till now. The book could be used as a text for graduate students and also it will be useful for researchers working in this field.

  20. Covariation of spectral and nonlinear EEG measures with alpha biofeedback.

    NARCIS (Netherlands)

    Fell, J.; Elfadil, H.; Klaver, P.; Roschke, J.; Elger, C.E.; Fernandez, G.S.E.

    2002-01-01

    This study investigated how different spectral and nonlinear EEG measures covaried with alpha power during auditory alpha biofeedback training, performed by 13 healthy subjects. We found a significant positive correlation of alpha power with the largest Lyapunov-exponent, pointing to an increased

  1. The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

    Directory of Open Access Journals (Sweden)

    Massimo Buscema

    2007-01-01

    (2007, this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007; the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (∼92% (2007. Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG.

  2. INTELLIGENT EEG ANALYSIS

    Directory of Open Access Journals (Sweden)

    M. Murugesan

    2011-04-01

    Full Text Available Brain is the wonderful organ of human body. It is the agent of information collection and transformation. The neural activity of the human brain starts between the 17th and 23rd week of prenatal development. It is believed that from this early stage and throughout life electrical signals are generated by the brain function but also the status of the whole body. Understanding of neuronal functions and neurophysiologic properties of the brain function together with the mechanisms underlying the generation of signals and their recording is, however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. This research paper concentrated only on brain tumor detection. Using minimum electrode location the brain tumor possibility is detected. This paper is separated into two parts: the First part deals with electrode location on the scalp and the second part deals with how the fuzzy logic rule based algorithm is applied for estimation of brain tumor from EEG. Basically 8 locations are identified. After acquiring the pure EEG signal Fuzzy Logic Rule is applied to predict the possibility of brain tumor.

  3. Presence of nonlinearity in intracranial EEG recordings: detected by Lyapunov exponents

    Science.gov (United States)

    Liu, Chang-Chia; Shiau, Deng-Shan; Chaovalitwongse, W. Art; Pardalos, Panos M.; Sackellares, J. C.

    2007-11-01

    In this communication, we performed nonlinearity analysis in the EEG signals recorded from patients with temporal lobe epilepsy (TLE). The largest Lyapunov exponent (Lmax) and phase randomization surrogate data technique were employed to form the statistical test. EEG recordings were acquired invasively from three patients in six brain regions (left and right temporal depth, sub-temporal and orbitofrontal) with 28-32 depth electrodes placed in depth and subdural of the brain. All three patients in this study have unilateral epileptic focus region on the right hippocampus(RH). Nonlinearity was detected by comparing the Lmax profiles of the EEG recordings to its surrogates. The nonlinearity was seen in all different states of the patient with the highest found in post-ictal state. Further our results for all patients exhibited higher degree of differences, quantified by paired t-test, in Lmax values between original and its surrogate from EEG signals recorded from epileptic focus regions. The results of this study demonstrated the Lmax is capable to capture spatio-temporal dynamics that may not be able to detect by linear measurements in the intracranial EEG recordings.

  4. Mental EEG Analysis Based on Infomax Algorithm

    Institute of Scientific and Technical Information of China (English)

    WUXiao-pei; GuoXiao-jing; ZANGDao-xin; SHENQian

    2004-01-01

    The patterns of EEG will change with mental tasks performed by the subject. In the field of EEG signal analysis and application, the study to get the patterns of mental EEG and then to use them to classify mental tasks has the significant scientific meaning and great application value. But for the reasons of different artifacts existing in EEG, the pattern detection of EEG under normal mental states is a very difficult problem. In this paper, Independent Component Analysisis applied to EEG signals collected from performing different mental tasks. The experiment results show that when one subject performs a single mental task in different trials, the independent components of EEG are very similar. It means that the independent components can be used as the mental EEG patterns to classify the different mental tasks.

  5. Amplitude Coupling Analysis of EEG Using Nonlinear Regressive Coefficients during Mental Fatigue%基于非线性回归系数的中枢疲劳脑电的幅度耦合分析

    Institute of Scientific and Technical Information of China (English)

    刘建平; 郑崇勋; 张崇

    2009-01-01

    Computing the Nonlinear regressive (NLR) coefficients of electroencephalogram (EEG) rhythms at different brain cortical areas for the mental fatigue caused by long term cognitive task, the variations of NLR coefficients of EEG rhythms under different mental fatigue level are sought out.The experimental results show that the NLR coefficients of EEG rhythms can effectively characterize the changes of amplitude coupling at different brain cortical areas under different mental fatigue level.The NLR coefficient provides a powerful tool for the EEG functional coupling analysis of mental fatigue.%本文通过对连续长时间脑力劳动前后状态下的脑电节律进行幅度耦合分析,提取了非线性回归系数,研究它们在不同中枢疲劳状态下的变化规律.实验结果表明,非线性回归系数能有效地反映出导联间幅度耦合同步程度随中枢疲劳程度的变化情况.为中枢疲劳脑电幅度耦合分析提供了有力工具.

  6. Phase Spectral Analysis of EEG Signals

    Institute of Scientific and Technical Information of China (English)

    YOURong-yi; CHENZhong

    2004-01-01

    A new method of phase spectral analysis of EEG is proposed for the comparative analysis of phase spectra between normal EEG and epileptic EEG signals based on the wavelet decomposition technique. By using multiscale wavelet decomposition, the original EEGs are mapped to an orthogonal wavelet space, such that the variations of phase can be observed at multiscale. It is found that the phase (and phase difference) spectra of normal EEGs are distinct from that of epileptic EEGs. That is the variations of phase (and phase difference) of normal EEGs have a distinct periodic pattern with the electrical activity proceeds in the brain, but do not the epileptic EEGs. For epileptic EEGs, only at those transient points, the phase variations are obvious. In order to verify these results with the observational data, the phase variations of EEGs in principal component space are observed and found that, the features of phase spectra is in correspondence with that the wavelet space. These results make it possible to view the behavior of EEG rhythms as a dynamic spectrum.

  7. A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals.

    Science.gov (United States)

    Acharya, U Rajendra; Sudarshan, Vidya K; Adeli, Hojjat; Santhosh, Jayasree; Koh, Joel E W; Puthankatti, Subha D; Adeli, Amir

    2015-01-01

    Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.

  8. Nonlinear EEG Analysis In Epilepsy

    Science.gov (United States)

    2007-11-02

    Department of Epileptology, Bonn University Medical Center Sigmund Freud Str 25, 53105 Bonn, Germany Report Documentation Page Report Date 25 Oct 2001...University Medical Center Sigmund Freud Str 25, 53105 Bonn Germany Performing Organization Report Number Sponsoring/Monitoring Agency Name(s) and Address(es

  9. Linear and non-linear effects of gradient artifact filtering methods in simultaneous EEG-FMRI - biomed 2010.

    Science.gov (United States)

    Cusenza, Monica; Accardo, Agostino; Monti, Fabrizio; Bramanti, Placido

    2010-01-01

    Simultaneous EEG-fMRI is a powerful emerging tool in functional neuroimaging that exploits the relationship between neuronal electrophysiological activity and its hemodynamic response. It has found application in the study of both spontaneous and evoked brain activity. Combining the complementary advantages of the two techniques it provides a measurement with high temporal and spatial resolution, allowing a reliable localization of event generators. However, EEG data recorded inside MRI scanner are heavily corrupted by different types of artifacts due to the interactions between the patient, EEG electrodes wires and the magnetic fields inside the scanner. In particular, gradient switching and RF pulses, necessary to acquire fMRI data, generate large artifacts that can completely obscure EEG signals. Many methods have been proposed to eliminate or at least reduce gradient artifact. In this paper both a qualitative and a quantitative evaluation of two different algorithms used for gradient artifact removal are presented. Linear and non-linear characteristics of EEG, such as power spectra, fractal dimension and beta scaling exponent, are evaluated for EEGs recorded outside and inside the scanner, in MR static and dynamic conditions. The study highlights how residual artifacts after correction and artifacts induced by correction itself could still considerably affect EEG signals. The results suggest that the quality of both these gradient artifact filtering methods is not yet sufficient to preserve EEG characteristics and thus it must be further improved. The aim of this study is to make neurophysiologists aware of the filtering effects that can compromise linear and non-linear analysis of EEG recorded during functional MRI.

  10. Wavelet Variance Analysis of EEG Based on Window Function

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yuan-zhuang; YOU Rong-yi

    2014-01-01

    A new wavelet variance analysis method based on window function is proposed to investigate the dynamical features of electroencephalogram (EEG).The ex-prienmental results show that the wavelet energy of epileptic EEGs are more discrete than normal EEGs, and the variation of wavelet variance is different between epileptic and normal EEGs with the increase of time-window width. Furthermore, it is found that the wavelet subband entropy (WSE) of the epileptic EEGs are lower than the normal EEGs.

  11. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Sree, S Vinitha; Chattopadhyay, Subhagata; Yu, Wenwei; Ang, Peng Chuan Alvin

    2011-06-01

    Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

  12. Cross-evidence for hypnotic susceptibility through nonlinear measures on EEGs of non-hypnotized subjects

    Science.gov (United States)

    Chiarucci, Riccardo; Madeo, Dario; Loffredo, Maria I.; Castellani, Eleonora; Santarcangelo, Enrica L.; Mocenni, Chiara

    2014-07-01

    Assessment of hypnotic susceptibility is usually obtained through the application of psychological instruments. A satisfying classification obtained through quantitative measures is still missing, although it would be very useful for both diagnostic and clinical purposes. Aiming at investigating the relationship between the cortical brain activity and the hypnotic susceptibility level, we propose the combined use of two methodologies - Recurrence Quantification Analysis and Detrended Fluctuation Analysis - both inherited from nonlinear dynamics. Indicators obtained through the application of these techniques to EEG signals of individuals in their ordinary state of consciousness allowed us to obtain a clear discrimination between subjects with high and low susceptibility to hypnosis. Finally a neural network approach was used to perform classification analysis.

  13. Linear and Nonlinear Analysis of Brain Dynamics in Children with Cerebral Palsy

    Science.gov (United States)

    Sajedi, Firoozeh; Ahmadlou, Mehran; Vameghi, Roshanak; Gharib, Masoud; Hemmati, Sahel

    2013-01-01

    This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26…

  14. [Application of SVM and wavelet analysis in EEG classification].

    Science.gov (United States)

    Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei

    2011-04-01

    We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.

  15. Stochastic non-linear oscillator models of EEG: the Alzheimer's disease case

    Science.gov (United States)

    Ghorbanian, Parham; Ramakrishnan, Subramanian; Ashrafiuon, Hashem

    2015-01-01

    In this article, the Electroencephalography (EEG) signal of the human brain is modeled as the output of stochastic non-linear coupled oscillator networks. It is shown that EEG signals recorded under different brain states in healthy as well as Alzheimer's disease (AD) patients may be understood as distinct, statistically significant realizations of the model. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) resting conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing—van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects by selecting the model physical parameters and noise intensity. The selected signal characteristics are power spectral densities in major brain frequency bands Shannon and sample entropies. These measures allow matching of linear time varying frequency content as well as non-linear signal information content and complexity. The main finding of the work is that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. However, it is also shown that the inclusion of sample entropy in the optimization process, to match the complexity of the EEG signal, enhances the stochastic non-linear oscillator model performance. PMID:25964756

  16. Technical and clinical analysis of microEEG: a miniature wireless EEG device designed to record high-quality EEG in the emergency department.

    Science.gov (United States)

    Omurtag, Ahmet; Baki, Samah G Abdel; Chari, Geetha; Cracco, Roger Q; Zehtabchi, Shahriar; Fenton, André A; Grant, Arthur C

    2012-09-24

    We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes. EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm). Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can

  17. Spatiotemporal Analysis of Multichannel EEG: CARTOOL

    OpenAIRE

    Denis Brunet; Murray, Micah M; Michel, Christoph M.

    2011-01-01

    This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous resul...

  18. EEG

    Science.gov (United States)

    ... when you are awake, and slower in certain stages of sleep. There are also normal patterns to these waves. ... An EEG test is very safe. The flashing lights or fast breathing ( hyperventilation ) required during the test ...

  19. Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique.

    Directory of Open Access Journals (Sweden)

    Todd Zorick

    Full Text Available Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG signals is nonlinear, with self-affine dynamics, while scalp-recorded EEG signals themselves are nonstationary. Therefore, traditional methods of EEG analysis may miss many properties inherent in such signals. Similarly, fractal analysis of EEG signals has shown scaling behaviors that may not be consistent with pure monofractal processes. In this study, we hypothesized that scalp-recorded human EEG signals may be better modeled as an underlying multifractal process. We utilized the Physionet online database, a publicly available database of human EEG signals as a standardized reference database for this study. Herein, we report the use of multifractal detrended fluctuation analysis on human EEG signals derived from waking and different sleep stages, and show evidence that supports the use of multifractal methods. Next, we compare multifractal detrended fluctuation analysis to a previously published multifractal technique, wavelet transform modulus maxima, using EEG signals from waking and sleep, and demonstrate that multifractal detrended fluctuation analysis has lower indices of variability. Finally, we report a preliminary investigation into the use of multifractal detrended fluctuation analysis as a pattern classification technique on human EEG signals from waking and different sleep stages, and demonstrate its potential utility for automatic classification of different states of consciousness. Therefore, multifractal detrended fluctuation analysis may be a useful pattern classification technique to distinguish among different states of brain function.

  20. Nonlinear functional analysis

    Directory of Open Access Journals (Sweden)

    W. L. Fouché

    1983-03-01

    Full Text Available In this article we discuss some aspects of nonlinear functional analysis. It included reviews of Banach’s contraction theorem, Schauder’s fixed point theorem, globalising techniques and applications of homotopy theory to nonlinear functional analysis. The author emphasises that fundamentally new ideas are required in order to achieve a better understanding of phenomena which contain both nonlinear and definite infinite dimensional features.

  1. Spatiotemporal Analysis of Multichannel EEG: CARTOOL

    Directory of Open Access Journals (Sweden)

    Denis Brunet

    2011-01-01

    Full Text Available This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

  2. Spatiotemporal analysis of multichannel EEG: CARTOOL.

    Science.gov (United States)

    Brunet, Denis; Murray, Micah M; Michel, Christoph M

    2011-01-01

    This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

  3. An empirical EEG analysis in brain death diagnosis for adults.

    Science.gov (United States)

    Chen, Zhe; Cao, Jianting; Cao, Yang; Zhang, Yue; Gu, Fanji; Zhu, Guoxian; Hong, Zhen; Wang, Bin; Cichocki, Andrzej

    2008-09-01

    Electroencephalogram (EEG) is often used in the confirmatory test for brain death diagnosis in clinical practice. Because EEG recording and monitoring is relatively safe for the patients in deep coma, it is believed to be valuable for either reducing the risk of brain death diagnosis (while comparing other tests such as the apnea) or preventing mistaken diagnosis. The objective of this paper is to study several statistical methods for quantitative EEG analysis in order to help bedside or ambulatory monitoring or diagnosis. We apply signal processing and quantitative statistical analysis for the EEG recordings of 32 adult patients. For EEG signal processing, independent component analysis (ICA) was applied to separate the independent source components, followed by Fourier and time-frequency analysis. For quantitative EEG analysis, we apply several statistical complexity measures to the EEG signals and evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were categorized as brain death. We report statistically significant differences of quantitative statistics with real-life EEG recordings in such a clinical study, and we also present interpretation and discussions on the preliminary experimental results.

  4. EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform

    Science.gov (United States)

    2001-10-25

    research and medical applications. Wavelet transform (WT) is a new multi-resolution time-frequency analysis method. WT possesses localization feature both... wavelet transform , the EEG signals are successfully decomposed and denoised. In this paper we also use a ’quasi-detrending’ method for classification of EEG

  5. Prediction of Nociceptive Responses during Sedation by Linear and Non-Linear Measures of EEG Signals in High Frequencies.

    Directory of Open Access Journals (Sweden)

    Umberto Melia

    Full Text Available The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG, have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS. Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.

  6. Multidimensional nonlinear descriptive analysis

    CERN Document Server

    Nishisato, Shizuhiko

    2006-01-01

    Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations. This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for futu...

  7. A new cellular nonlinear network emulation on FPGA for EEG signal processing in epilepsy

    Science.gov (United States)

    Müller, Jens; Müller, Jan; Tetzlaff, Ronald

    2011-05-01

    For processing of EEG signals, we propose a new architecture for the hardware emulation of discrete-time Cellular Nonlinear Networks (DT-CNN). Our results show the importance of a high computational accuracy in EEG signal prediction that cannot be achieved with existing analogue VLSI circuits. The refined architecture of the processing elements and its resource schedule, the cellular network structure with local couplings, the FPGA-based embedded system containing the DT-CNN, and the data flow in the entire system will be discussed in detail. The proposed DT-CNN design has been implemented and tested on an Xilinx FPGA development platform. The embedded co-processor with a multi-threading kernel is utilised for control and pre-processing tasks and data exchange to the host via Ethernet. The performance of the implemented DT-CNN has been determined for a popular example and compared to that of a conventional computer.

  8. Nonlinear Analysis of Buckling

    Directory of Open Access Journals (Sweden)

    Psotný Martin

    2014-06-01

    Full Text Available The stability analysis of slender web loaded in compression was presented. To solve this problem, a specialized computer program based on FEM was created. The nonlinear finite element method equations were derived from the variational principle of minimum of potential energy. To obtain the nonlinear equilibrium paths, the Newton-Raphson iteration algorithm was used. Corresponding levels of the total potential energy were defined. The peculiarities of the effects of the initial imperfections were investigated. Special attention was focused on the influence of imperfections on the post-critical buckling mode. The stable and unstable paths of the nonlinear solution were separated. Obtained results were compared with those gained using ANSYS system.

  9. Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A nonlinear method named detrended fluctuation analysis (DFA) was utilized to investigate the scaling behavior of the human electroencephalogram (EEG) in three emotional music conditions (fear, happiness, sadness) and a rest condition (eyes-closed). The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides β band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the 'approach-withdrawal' model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.

  10. Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine.

    Science.gov (United States)

    Zhou, Jing; Wu, Xiao-ming; Zeng, Wei-jie

    2015-12-01

    Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30 min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30 s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p classification accuracy reached 95.1% corresponding to the sensitivity 93.2% and specificity 98.6%. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea.

  11. Emotion recognition method using entropy analysis of EEG signals

    Directory of Open Access Journals (Sweden)

    Seyyed Abed Hosseini

    2011-08-01

    Full Text Available This paper proposes an emotion recognition system using EEG signals, therefore a new approach to emotion state analysis by approximate (ApEn and wavelet entropy (WE is described. We have used EEG signals recorded during emotion in five channels (FP1, FP2, T3, T4 and Pz, under pictures induction environment (calm-neutral and negative excited for participants. After a brief introduction to the concept, the ApEn and WE were extracted from two different EEG time series. The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to the normal, indicating reduction in active neuronal process in the brain.

  12. Analysis of Seizure EEG in Kindled Epileptic Rats

    Directory of Open Access Journals (Sweden)

    A. K. Sen

    2007-01-01

    Full Text Available Using wavelet analysis we have detected the presence of chirps in seizure EEG signals recorded from kindled epileptic rats. Seizures were induced by electrical stimulation of the amygdala and the EEG signals recorded from the amygdala were analyzed using a continuous wavelet transform. A time–frequency representation of the wavelet power spectrum revealed that during seizure the EEG signal is characterized by a chirp-like waveform whose frequency changes with time from the onset of seizure to its completion. Similar chirp-like time–frequency profiles have been observed in newborn and adult patients undergoing epileptic seizures. The global wavelet spectrum depicting the variation of power with frequency showed two dominant frequencies with the largest amounts of power during seizure. Our results indicate that a kindling paradigm in rats can be used as an animal model of human temporal lobe epilepsy to detect seizures by identifying chirp-like time–frequency variations in the EEG signal.

  13. EEG source analysis of data from paralysed subjects

    Science.gov (United States)

    Carabali, Carmen A.; Willoughby, John O.; Fitzgibbon, Sean P.; Grummett, Tyler; Lewis, Trent; DeLosAngeles, Dylan; Pope, Kenneth J.

    2015-12-01

    One of the limitations of Encephalography (EEG) data is its quality, as it is usually contaminated with electric signal from muscle. This research intends to study results of two EEG source analysis methods applied to scalp recordings taken in paralysis and in normal conditions during the performance of a cognitive task. The aim is to determinate which types of analysis are appropriate for dealing with EEG data containing myogenic components. The data used are the scalp recordings of six subjects in normal conditions and during paralysis while performing different cognitive tasks including the oddball task which is the object of this research. The data were pre-processed by filtering it and correcting artefact, then, epochs of one second long for targets and distractors were extracted. Distributed source analysis was performed in BESA Research 6.0, using its results and information from the literature, 9 ideal locations for source dipoles were identified. The nine dipoles were used to perform discrete source analysis, fitting them to the averaged epochs for obtaining source waveforms. The results were statistically analysed comparing the outcomes before and after the subjects were paralysed. Finally, frequency analysis was performed for better explain the results. The findings were that distributed source analysis could produce confounded results for EEG contaminated with myogenic signals, conversely, statistical analysis of the results from discrete source analysis showed that this method could help for dealing with EEG data contaminated with muscle electrical signal.

  14. Analysis of generalized interictal discharges using quantitative EEG.

    Science.gov (United States)

    da Silva Braga, Aline Marques; Fujisao, Elaine Keiko; Betting, Luiz Eduardo

    2014-12-01

    Experimental evidence from animal models of the absence seizures suggests a focal source for the initiation of generalized spike-and-wave (GSW) discharges. Furthermore, clinical studies indicate that patients diagnosed with idiopathic generalized epilepsy (IGE) exhibit focal electroencephalographic abnormalities, which involve the thalamo-cortical circuitry. This circuitry is a key network that has been implicated in the initiation of generalized discharges, and may contribute to the pathophysiology of GSW discharges. Quantitative electroencephalogram (qEEG) analysis may be able to detect abnormalities associated with the initiation of GSW discharges. The objective of this study was to determine whether interictal GSW discharges exhibit focal characteristics using qEEG analysis. In this study, 75 EEG recordings from 64 patients were analyzed. All EEG recordings analyzed contained at least one GSW discharge. EEG recordings were obtained by a 22-channel recorder with electrodes positioned according to the international 10-20 system of electrode placement. EEG activity was recorded for 20 min including photic stimulation and hyperventilation. The EEG recordings were visually inspected, and the first unequivocally confirmed generalized spike was marked for each discharge. Three methods of source imaging analysis were applied: dipole source imaging (DSI), classical LORETA analysis recursively applied (CLARA), and equivalent dipole of independent components with cluster analysis. A total of 753 GSW discharges were identified and spatiotemporally analyzed. Source evaluation analysis using all three techniques revealed that the frontal lobe was the principal source of GSW discharges (70%), followed by the parietal and occipital lobes (14%), and the basal ganglia (12%). The main anatomical sources of GSW discharges were the anterior cingulate cortex (36%) and the medial frontal gyrus (23%). Source analysis did not reveal a common focal source of GSW discharges. However

  15. Discriminant Multitaper Component Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Sajda, Paul

    the method for predicting the handedness of a subject’s button press given multivariate EEG data. We show that our method learns multitapers sensitive to oscillatory activity in the 8–12 Hz range with spatial filters selective for lateralized motor cortex. This finding is consistent with the well-known mu...

  16. Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

    Science.gov (United States)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

    2016-11-16

    Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

  17. Block Sparse Compressed Sensing of Electroencephalogram (EEG Signals by Exploiting Linear and Non-Linear Dependencies

    Directory of Open Access Journals (Sweden)

    Hesham Mahrous

    2016-02-01

    Full Text Available This paper proposes a compressive sensing (CS method for multi-channel electroencephalogram (EEG signals in Wireless Body Area Network (WBAN applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV problem, the Block Sparse Bayesian Learning-BO (BSBL-BO method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.

  18. Unsupervised EEG analysis for automated epileptic seizure detection

    Science.gov (United States)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  19. Global Analysis of Nonlinear Dynamics

    CERN Document Server

    Luo, Albert

    2012-01-01

    Global Analysis of Nonlinear Dynamics collects chapters on recent developments in global analysis of non-linear dynamical systems with a particular emphasis on cell mapping methods developed by Professor C.S. Hsu of the University of California, Berkeley. This collection of contributions prepared by a diverse group of internationally recognized researchers is intended to stimulate interests in global analysis of complex and high-dimensional nonlinear dynamical systems, whose global properties are largely unexplored at this time. This book also: Presents recent developments in global analysis of non-linear dynamical systems Provides in-depth considerations and extensions of cell mapping methods Adopts an inclusive style accessible to non-specialists and graduate students Global Analysis of Nonlinear Dynamics is an ideal reference for the community of nonlinear dynamics in different disciplines including engineering, applied mathematics, meteorology, life science, computational science, and medicine.  

  20. A stochastic model for EEG microstate sequence analysis.

    Science.gov (United States)

    Gärtner, Matthias; Brodbeck, Verena; Laufs, Helmut; Schneider, Gaby

    2015-01-01

    The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal. In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences.

  1. Sleep EEG spectral analysis in a diurnal rodent : Eutamias sibiricus

    NARCIS (Netherlands)

    DIJK, DJ; DAAN, S

    1989-01-01

    1. Sleep was studied in the diurnal rodent Eutamias sibiricus, chronically implanted with EEG and EMG electrodes. Analysis of the distribution of wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep over the 24 h period (LD 12:12) showed that total sleep time was 27.5%

  2. A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

    Directory of Open Access Journals (Sweden)

    Iman Veisi

    2010-03-01

    Full Text Available Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.

  3. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Sree, S Vinitha; Alvin, Ang Peng Chuan; Yanti, Ratna; Suri, Jasjit S

    2012-04-01

    Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software

  4. Nonlinear functional analysis

    CERN Document Server

    Deimling, Klaus

    1985-01-01

    topics. However, only a modest preliminary knowledge is needed. In the first chapter, where we introduce an important topological concept, the so-called topological degree for continuous maps from subsets ofRn into Rn, you need not know anything about functional analysis. Starting with Chapter 2, where infinite dimensions first appear, one should be familiar with the essential step of consider­ ing a sequence or a function of some sort as a point in the corresponding vector space of all such sequences or functions, whenever this abstraction is worthwhile. One should also work out the things which are proved in § 7 and accept certain basic principles of linear functional analysis quoted there for easier references, until they are applied in later chapters. In other words, even the 'completely linear' sections which we have included for your convenience serve only as a vehicle for progress in nonlinearity. Another point that makes the text introductory is the use of an essentially uniform mathematical languag...

  5. Detection of neonatal seizures through computerized EEG analysis.

    Science.gov (United States)

    Liu, A; Hahn, J S; Heldt, G P; Coen, R W

    1992-01-01

    Neonatal seizures are a symptom of central nervous system disturbances. Neonatal seizures may be identified by direct clinical observation by the majority of electrographic seizures are clinically silent or subtle. Electrographic seizures in the newborn consist of periodic or rhythmic discharges that are distinctively different from normal background cerebral activity. Utilizing these differences, we have developed a technique to identify electrographic seizure activity. In this study, autocorrelation analysis was used to distinguish seizures from background electrocerebral activity. Autocorrelation data were scored to quantify the periodicity using a newly developed scoring system. This method, Scored Autocorrelation Moment (SAM) analysis, successfully distinguished epochs of EEGs with seizures from those without (N = 117 epochs, 58 with seizure and 59 without). SAM analysis showed a sensitivity of 84% and a specificity of 98%. SAM analysis of EEG may provide a method for monitoring electrographic seizures in high-risk newborns.

  6. [Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease].

    Science.gov (United States)

    Zhang, Meiyun; Zhang, Benshu; Chen, Ying

    2014-08-01

    Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (Pentropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (Pentropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, Pentropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.

  7. Multi-scale symbolic transfer entropy analysis of EEG

    Science.gov (United States)

    Yao, Wenpo; Wang, Jun

    2017-10-01

    From both global and local perspectives, we symbolize two kinds of EEG and analyze their dynamic and asymmetrical information using multi-scale transfer entropy. Multi-scale process with scale factor from 1 to 199 and step size of 2 is applied to EEG of healthy people and epileptic patients, and then the permutation with embedding dimension of 3 and global approach are used to symbolize the sequences. The forward and reverse symbol sequences are taken as the inputs of transfer entropy. Scale factor intervals of permutation and global way are (37, 57) and (65, 85) where the two kinds of EEG have satisfied entropy distinctions. When scale factor is 67, transfer entropy of the healthy and epileptic subjects of permutation, 0.1137 and 0.1028, have biggest difference. And the corresponding values of the global symbolization is 0.0641 and 0.0601 which lies in the scale factor of 165. Research results show that permutation which takes contribution of local information has better distinction and is more effectively applied to our multi-scale transfer entropy analysis of EEG.

  8. EEG and MEG Data Analysis in SPM8

    Directory of Open Access Journals (Sweden)

    Vladimir Litvak

    2011-01-01

    Full Text Available SPM is a free and open source software written in MATLAB (The MathWorks, Inc.. In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii dynamic causal modelling (DCM, an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra, induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI and batching tools.

  9. Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection

    Directory of Open Access Journals (Sweden)

    Liang Sheng-Fu

    2010-01-01

    Full Text Available Approximately 1% of the world's population has epilepsy, and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. If an automatic seizure-detection system was available, it could reduce the time required by a neurologist to perform an off-line diagnosis by reviewing electroencephalogram (EEG data. It could produce an on-line warning signal to alert healthcare professionals or to drive a treatment device such as an electrical stimulator to enhance the patient's safety and quality of life. This paper describes a systematic evaluation of current approaches to seizure detection in the literature. This evaluation was then used to suggest a reliable, practical epilepsy detection method. The combination of complexity analysis and spectrum analysis on an EEG can perform robust evaluations on the collected data. Principle component analysis (PCA and genetic algorithms (GAs were applied to various linear and nonlinear methods. The best linear models resulted from using all of the features without other processing. For the nonlinear models, applying PCA for feature reduction provided better results than applying GAs. The feasibility of executing the proposed methods on a personal computer for on-line processing was also demonstrated.

  10. Analysis of Small Muscle Movement Effects on EEG Signals

    Science.gov (United States)

    2016-12-22

    Lieutenant, TuAF AFIT- ENG -MS-16-D-051 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air...AFIT- ENG -MS-16-D-051 ANALYSIS OF SMALL MUSCLE MOVEMENT EFFECTS ON EEG SIGNALS THESIS Presented to the Faculty Department of...First Lieutenant, TuAF December 2016 DISTRIBUTION STATEMENT A. APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. AFIT- ENG -MS-16-D-051

  11. Nonlinear programming analysis and methods

    CERN Document Server

    Avriel, Mordecai

    2012-01-01

    This text provides an excellent bridge between principal theories and concepts and their practical implementation. Topics include convex programming, duality, generalized convexity, analysis of selected nonlinear programs, techniques for numerical solutions, and unconstrained optimization methods.

  12. Optimal channel selection for analysis of EEG-sleep patterns of neonates.

    Science.gov (United States)

    Piryatinska, Alexandra; Woyczynski, Wojbor A; Scher, Mark S; Loparo, Kenneth A

    2012-04-01

    This paper extends our previous work on automated detection and classification of neonate EEG sleep stages. In [19] we adapted and integrated a range of computational, mathematical and statistical tools for the analysis of neonatal electroencephalogram (EEG) sleep recordings with the aim of facilitating the assessment of neonatal brain maturation and dismaturity by studying the structure and temporal patterns of their sleep. That work relied on algorithms using a single channel of EEG. The present paper builds on our previous work by incorporating a larger selection of EEG channels that capture both the spatial distribution and temporal patterns of EEG during sleep. Using a multivariate analysis approach, we obtain the "optimal" selection of the EEG channels and characteristics that are most suitable for EEG sleep state separation. Copyright © 2011. Published by Elsevier Ireland Ltd.

  13. Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

    Science.gov (United States)

    Cichy, Radoslaw Martin; Pantazis, Dimitrios

    2017-07-14

    Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Mutual information analysis of sleep EEG in detecting psycho-physiological insomnia.

    Science.gov (United States)

    Aydın, Serap; Tunga, M Alper; Yetkin, Sinan

    2015-05-01

    The primary goal of this study is to state the clear changes in functional brain connectivity during all night sleep in psycho-physiological insomnia (PPI). The secondary goal is to investigate the usefulness of Mutual Information (MI) analysis in estimating cortical sleep EEG arousals for detection of PPI. For these purposes, healthy controls and patients were compared to each other with respect to both linear (Pearson correlation coefficient and coherence) and nonlinear quantifiers (MI) in addition to phase locking quantification for six sleep stages (stage.1-4, rem, wake) by means of interhemispheric dependency between two central sleep EEG derivations. In test, each connectivity estimation calculated for each couple of epoches (C3-A2 and C4-A1) was identified by the vector norm of estimation. Then, patients and controls were classified by using 10 different types of data mining classifiers for five error criteria such as accuracy, root mean squared error, sensitivity, specificity and precision. High performance in a classification through a measure will validate high contribution of that measure to detecting PPI. The MI was found to be the best method in detecting PPI. In particular, the patients had lower MI, higher PCC for all sleep stages. In other words, the lower sleep EEG synchronization suffering from PPI was observed. These results probably stand for the loss of neurons that then contribute to less complex dynamical processing within the neural networks in sleep disorders an the functional central brain connectivity is nonlinear during night sleep. In conclusion, the level of cortical hemispheric connectivity is strongly associated with sleep disorder. Thus, cortical communication quantified in all existence sleep stages might be a potential marker for sleep disorder induced by PPI.

  15. Nonlinear programming analysis and methods

    CERN Document Server

    Avriel, Mordecai

    2003-01-01

    Comprehensive and complete, this overview provides a single-volume treatment of key algorithms and theories. The author provides clear explanations of all theoretical aspects, with rigorous proof of most results. The two-part treatment begins with the derivation of optimality conditions and discussions of convex programming, duality, generalized convexity, and analysis of selected nonlinear programs. The second part concerns techniques for numerical solutions and unconstrained optimization methods, and it presents commonly used algorithms for constrained nonlinear optimization problems. This g

  16. Technical and clinical analysis of microEEG: a miniature wireless EEG device designed to record high-quality EEG in the emergency department

    OpenAIRE

    Omurtag, Ahmet; Baki, Samah G Abdel; Chari, Geetha; Cracco, Roger Q; Zehtabchi, Shahriar; Fenton, André A.; Grant, Arthur C

    2012-01-01

    Background We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). Methods The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the...

  17. Automated EEG signal analysis for identification of epilepsy seizures and brain tumour.

    Science.gov (United States)

    Sharanreddy, M; Kulkarni, P K

    2013-11-01

    Abstract Electroencephalography (EEG) is a clinical test which records neuro-electrical activities generated by brain structures. EEG test results used to monitor brain diseases such as epilepsy seizure, brain tumours, toxic encephalopathies infections and cerebrovascular disorders. Due to the extreme variation in the EEG morphologies, manual analysis of the EEG signal is laborious, time consuming and requires skilled interpreters, who by the nature of the task are prone to subjective judegment and error. Further, manual analysis of the EEG results often fails to detect and uncover subtle features. This paper proposes an automated EEG analysis method by combining digital signal processing and neural network techniques, which will remove error and subjectivity associated with manual analysis and identifies the existence of epilepsy seizure and brain tumour diseases. The system uses multi-wavelet transform for feature extraction in which an input EEG signal is decomposed in a sub-signal. Irregularities and unpredictable fluctuations present in the decomposed signal are measured using approximate entropy. A feed-forward neural network is used to classify the EEG signal as a normal, epilepsy or brain tumour signal. The proposed technique is implemented and tested on data of 500 EEG signals for each disease. Results are promising, with classification accuracy of 98% for normal, 93% for epilepsy and 87% for brain tumour. Along with classification, the paper also highlights the EEG abnormalities associated with brain tumour and epilepsy seizure.

  18. Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations

    National Research Council Canada - National Science Library

    Adrian Rodriguez Aguinaga; Miguel Angel Lopez Ramirez; Maria del Rosario Baltazar Flores

    2015-01-01

    This paper proposes a methodology to perform emotional states classification by the analysis of EEG signals, wavelet decomposition and an electrode discrimination process, that associates electrodes...

  19. Comparison of ictal and interictal EEG signals using fractal features.

    Science.gov (United States)

    Wang, Yu; Zhou, Weidong; Yuan, Qi; Li, Xueli; Meng, Qingfang; Zhao, Xiuhe; Wang, Jiwen

    2013-12-01

    The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

  20. Nonlinear analysis of anesthesia dynamics by Fractal Scaling Exponent.

    Science.gov (United States)

    Gifani, P; Rabiee, H R; Hashemi, M R; Taslimi, P; Ghanbari, M

    2006-01-01

    The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum Fractal Scaling Exponent. This optimum solution is based on the best box sizes in the Detrended Fluctuation Analysis (DFA) algorithm which have meaningful changes at different depth of anesthesia. The Fractal Scaling Exponent (FSE) Index as a new criterion has been proposed. The experimental results confirm that our new Index can clearly discriminate between aware to moderate and deep anesthesia levels. Moreover, it significantly reduces the computational complexity and results in a faster reaction to the transients in patients' consciousness levels in relations with the other algorithms.

  1. Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

    CERN Document Server

    Wingeier, B M; Silberstein, R B; Wingeier, Brett M.; Nunez, Paul L.; Silberstein, Richard B.

    2001-01-01

    We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier reports of an approximate frequency-wavenumber relationship in some bands.

  2. Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

    OpenAIRE

    Wingeier, Brett M.; Nunez, Paul L.; Silberstein, Richard B.

    2000-01-01

    We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier reports of an approximate frequency-wavenumber relationship in some bands.

  3. EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Kamel

    2012-04-01

    Full Text Available Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT with the Regulated Fisher Linear Discriminant (RFLD classifier. Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

  4. Nonlinear rotordynamics analysis

    Science.gov (United States)

    Day, W. B.; Zalik, R. A.

    1986-01-01

    Three analytic consequences of the nonlinear Jeffcott equations are examined. The primary application of these analyses is directed toward understanding the excessive vibrations recorded in the Liquid Oxygen (LOX) pump of the Space Shuttle Main Engine (SSME) during hot firing ground testing. The first task is to provide bounds on the coefficients of the equations which delimit the two cases of numerical solution as a circle or an annulus. The second task examines the mathematical generalization to multiple forcing functions, which includes the special problems of mass imbalance, side force, rubbing, and combination of these forces. Finally, stability and boundedness of the steady-state solutions is discussed and related to the corresponding linear problem.

  5. Epileptic Seizure Detection in Eeg Signals Using Multifractal Analysis and Wavelet Transform

    Science.gov (United States)

    Uthayakumar, R.; Easwaramoorthy, D.

    2013-06-01

    This paper explores the three different methods to explicitly recognize the healthy and epileptic EEG signals: Modified, Improved, and Advanced forms of Generalized Fractal Dimensions (GFD). The newly proposed scheme is based on GFD and the discrete wavelet transform (DWT) for analyzing the EEG signals. First EEG signals are decomposed into approximation and detail coefficients using DWT and then GFD values of the original EEGs, approximation and detail coefficients are computed. Significant differences are observed among the GFD values of the healthy and epileptic EEGs allowing us to classify seizures with high accuracy. It is shown that the classification rate is very less accurate without DWT as a preprocessing step. The proposed idea is illustrated through the graphical and statistical tools. The EEG data is further tested for linearity by using normal probability plot and we proved that epileptic EEG had significant nonlinearity whereas healthy EEG distributed normally and similar to Gaussian linear process. Therefore, we conclude that the GFD and the wavelet decomposition through DWT are the strong indicators of the state of illness of epileptic patients.

  6. Analysis of routine EEG usage in a general adult ICU.

    LENUS (Irish Health Repository)

    McHugh, J C

    2009-09-01

    Non-convulsive seizures and status epilepticus are common in brain-injured patients in intensive care units. Continuous electroencephalography (cEEG) monitoring is the most sensitive means of their detection. In centres where cEEG is unavailable, routine EEG is often utilized for diagnosis although its sensitivity is lower.

  7. Optimizing detection and analysis of slow waves in sleep EEG.

    Science.gov (United States)

    Mensen, Armand; Riedner, Brady; Tononi, Giulio

    2016-12-01

    Analysis of individual slow waves in EEG recording during sleep provides both greater sensitivity and specificity compared to spectral power measures. However, parameters for detection and analysis have not been widely explored and validated. We present a new, open-source, Matlab based, toolbox for the automatic detection and analysis of slow waves; with adjustable parameter settings, as well as manual correction and exploration of the results using a multi-faceted visualization tool. We explore a large search space of parameter settings for slow wave detection and measure their effects on a selection of outcome parameters. Every choice of parameter setting had some effect on at least one outcome parameter. In general, the largest effect sizes were found when choosing the EEG reference, type of canonical waveform, and amplitude thresholding. Previously published methods accurately detect large, global waves but are conservative and miss the detection of smaller amplitude, local slow waves. The toolbox has additional benefits in terms of speed, user-interface, and visualization options to compare and contrast slow waves. The exploration of parameter settings in the toolbox highlights the importance of careful selection of detection METHODS: The sensitivity and specificity of the automated detection can be improved by manually adding or deleting entire waves and or specific channels using the toolbox visualization functions. The toolbox standardizes the detection procedure, sets the stage for reliable results and comparisons and is easy to use without previous programming experience. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Continuous EEG signal analysis for asynchronous BCI application.

    Science.gov (United States)

    Hsu, Wei-Yen

    2011-08-01

    In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

  9. MEG and EEG data analysis with MNE-Python

    Directory of Open Access Journals (Sweden)

    Alexandre eGramfort

    2013-12-01

    Full Text Available Magnetoencephalography and electroencephalography (M/EEG measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation offunctional connectivity between distributed brain regions.All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysispipelines by writing Python scripts.Moreover, MNE-Python is tightly integrated with the core Python libraries for scientificcomptutation (Numpy, Scipy and visualization (matplotlib and Mayavi, as wellas the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD licenseallowing code reuse, even in commercial products. Although MNE-Python has onlybeen under heavy development for a couple of years, it has rapidly evolved withexpanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices.MNE-Python also gives easy access to preprocessed datasets,helping users to get started quickly and facilitating reproducibility ofmethods by other researchers. Full documentation, including dozens ofexamples, is available at http://martinos.org/mne.

  10. Topics in nonlinear functional analysis

    CERN Document Server

    Nirenberg, Louis

    2001-01-01

    Since its first appearance as a set of lecture notes published by the Courant Institute in 1974, this book served as an introduction to various subjects in nonlinear functional analysis. The current edition is a reprint of these notes, with added bibliographic references. Topological and analytic methods are developed for treating nonlinear ordinary and partial differential equations. The first two chapters of the book introduce the notion of topological degree and develop its basic properties. These properties are used in later chapters in the discussion of bifurcation theory (the possible br

  11. Stability analysis of nonlinear systems

    CERN Document Server

    Lakshmikantham, Vangipuram; Martynyuk, Anatoly A

    2015-01-01

    The book investigates stability theory in terms of two different measure, exhibiting the advantage of employing families of Lyapunov functions and treats the theory of a variety of inequalities, clearly bringing out the underlying theme. It also demonstrates manifestations of the general Lyapunov method, showing how this technique can be adapted to various apparently diverse nonlinear problems. Furthermore it discusses the application of theoretical results to several different models chosen from real world phenomena, furnishing data that is particularly relevant for practitioners. Stability Analysis of Nonlinear Systems is an invaluable single-sourse reference for industrial and applied mathematicians, statisticians, engineers, researchers in the applied sciences, and graduate students studying differential equations.

  12. Using S-transform in EEG analysis for measuring an alert versus mental fatigue state.

    Science.gov (United States)

    Tran, Yvonne; Thuraisingham, Ranjit; Wijesuriya, Nirupama; Craig, Ashley; Nguyen, Hung

    2014-01-01

    This paper presents research that investigated the effects of mental fatigue on brain activity using electroencephalogram (EEG) signals. Since EEG signals are considered to be non-stationary, time-frequency analysis has frequently been used for analysis. The S-transform is a time-frequency analysis method and is used in this paper to analyze EEG signals during alert and fatigue states during a driving simulator task. Repeated-measure MANOVA results show significant differences between alert and fatigue states within the alpha (8-13Hz) frequency band. The two sites demonstrating the greatest increases in alpha activity during fatigue were the Cz and P4 sites. The results show that S-transform analysis can be used to distinguish between alert and fatigue states in the EEG and also supports the use of the S-transform for EEG analysis.

  13. [EEG coherence analysis in depressive disorders and its possible use in clinical practice: a literature review].

    Science.gov (United States)

    Varlamov, A A; Strelets, V B

    2013-01-01

    Recent reviews and meta-analyses of clinical data have revealed that there is a clear need in objective biomarkers of mental disorders and, in particular, of depression. There is a lot of evidence that EEG coherence can be an important marker of depressive disorders, and can predict response to different antidepressants. The most consistent finding is a decrease of frontal interhemispheric EEG coherence which is observed for most disorders related to depression. Methodological issues are discussed with a particular emphasis on use of statistical classifiers and on coupling EEG coherence with other methods of EEG analysis.

  14. Localization of brain activities using multiway analysis of EEG tensor via EMD and reassigned TF representation.

    Science.gov (United States)

    Pouryazdian, Saeed; Beheshti, Soosan; Krishnan, Sridhar

    2015-01-01

    Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brain's physiological, mental and functional abnormalities. Processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations in such a way that they are localized in temporal, spectral and spatial domain. Here we use multi-way (Tensor) analysis for localizing the EEG events. We used EMD process for decomposing EEG into distinct oscillatory modes, which are then mapped to TF plane using the near optimal Reassigned Spectrogram. Temporal, Spatial and Spectral information of the Multichannel EEG are then used to generate a three-way Frequency-Time-Space EEG tensor. Exploiting EMD also enables us to detrend the EEG recordings. Simulation results on both synthetic and real EEG data show that tensor analysis greatly improve separation and localization of overlapping events in EEG and it could be effectively exploited for detecting and characterizing the evoked potentials.

  15. AN ANALYSIS OF TWO COMMON REFERENCE POINTS FOR EEGS.

    Science.gov (United States)

    López, S; Gross, A; Yang, S; Golmohammadi, M; Obeid, I; Picone, J

    2016-12-01

    Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical grounding used). This investigation explores the statistical differences present in two different referential montages: Linked Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately 45% of the data in the TUH EEG Corpus. In this study, we explore the impact this variability has on machine learning performance. We compare the statistical properties of features generated using these two montages, and explore the impact of performance on our standard Hidden Markov Model (HMM) based classification system. We show that a system trained on LE data significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We also demonstrate that performance of a system trained on both data sets is somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data suggests that mean, variance and channel normalization should be considered. However, cepstral mean subtraction failed to produce an improvement in performance, suggesting that the impact of these statistical differences is subtler.

  16. Complex dynamics of epileptic EEG.

    Science.gov (United States)

    Kannathal, N; Puthusserypady, Sadasivan K; Choo Min, Lim

    2004-01-01

    Electroencephalogram (EEG) - the recorded representation of electrical activity of the brain contain useful information about the state of the brain. Recent studies indicate that nonlinear methods can extract valuable information from neuronal dynamics. We compare the dynamical properties of EEG signals of healthy subjects with epileptic subjects using nonlinear time series analysis techniques. Chaotic invariants like correlation dimension (D2) , largest Lyapunov exponent (lambda1), Hurst exponent (H) and Kolmogorov entropy (K) are used to characterize the signal. Our study showed clear differences in dynamical properties of brain electrical activity of the normal and epileptic subjects with a confidence level of more than 90%. Furthermore to support this claim fractal dimension (FD) analysis is performed. The results indicate reduction in value of FD for epileptic EEG indicating reduction in system complexity.

  17. High-density EEG coherence analysis using functional units applied to mental fatigue

    NARCIS (Netherlands)

    Caat, Michael ten; Lorist, Monicque M.; Bezdan, Eniko; Roerdink, Jos B.T.M.; Maurits, Natasha M.

    2008-01-01

    Electroencephalography (EEG) coherence provides a quantitative measure of functional brain connectivity which is calculated between pairs of signals as a function of frequency. Without hypotheses, traditional coherence analysis would be cumbersome for high-density EEG which employs a large number of

  18. High-density EEG coherence analysis using functional units applied to mental fatigue

    NARCIS (Netherlands)

    Caat, Michael ten; Lorist, Monicque M.; Bezdan, Eniko; Roerdink, Jos B.T.M.; Maurits, Natasha M.

    2008-01-01

    Electroencephalography (EEG) coherence provides a quantitative measure of functional brain connectivity which is calculated between pairs of signals as a function of frequency. Without hypotheses, traditional coherence analysis would be cumbersome for high-density EEG which employs a large number of

  19. The Etiology and Outcome Analysis of Neonatal Burst Suppression EEG

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lian; ZHOU Yanxia; XU Sanqing

    2007-01-01

    The neonatal burst suppression is a severe EEG pattern and always demonstrates serious damage of nerve system. But the outcome of these patients depends on the different etiology. A total of 256 cases of video EEG recordings were analyzed in order to summarize the etiology and outcome of burst suppression. The results showed that some patients in all 17 cases of burst suppression showed EEG improvement. The etiology was the dominant factor in long term outcome. It was sug-gested that effective video EEG monitoring is helpful for etiologic study and prognosis evaluation.

  20. MEG and EEG data analysis with MNE-Python.

    Science.gov (United States)

    Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti

    2013-12-26

    Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.

  1. Fractal EEG analysis with Higuchi's algorithm of low-frequency noise exposition on humans

    Science.gov (United States)

    Panuszka, Ryszard; Damijan, Zbigniew; Kasprzak, Cezary

    2004-05-01

    Authors used methods based on fractal analysis of EEG signal to assess the influence of low-frequency sound field on the human brain electro-potentials. The relations between LFN (low-frequency noise) and change in fractal dimension EEG signal were measured with stimulations tones. Three types of LFN stimuli were presented; each specified dominant frequency and sound-pressure levels (7 Hz at 120 dB, 18 Hz at 120 dB, and 40 Hz at 110 dB). Standard EEG signal was recorded before, during, and after subject's exposure for 35 min. LFN. Applied to the analysis fractal dimension of EEG-signal Higuchis algorithm. Experiments show LFN influence on complexity of EEG-signal with calculated Higuchi's algorithm. Observed increase of mean value of Higuchi's fractal dimension during exposition to LFN.

  2. Convolutive ICA for Spatio-Temporal Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2007-01-01

    in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

  3. Brain Network Analysis from High-Resolution EEG Signals

    Science.gov (United States)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

    lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an

  4. Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

    Directory of Open Access Journals (Sweden)

    Shengkun Xie

    2014-01-01

    Full Text Available Classification of electroencephalography (EEG is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

  5. Dynamic principal component analysis with nonoverlapping moving window and its applications to epileptic EEG classification.

    Science.gov (United States)

    Xie, Shengkun; Krishnan, Sridhar

    2014-01-01

    Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

  6. Analysis of EEG features of neuronal surface antibody associated encephalitis

    Directory of Open Access Journals (Sweden)

    Lu-hua WEI

    2016-09-01

    Full Text Available Objective To summarize the clinical manifestations, EEG and head MRI features of neuronal surface antibody associated encephalitis, and to investigate the role of EEG in determining the relapse or fluctuation of this disease, characteristics of EEG corresponding to head MRI, and EEG features in different clinical stages. Methods A total of 23 patients with neuronal surface antibody associated encephalitis were divided into ascent, climax, descent and recovery stage according to their clinical course. The relation between EEG background activity, distribution of slow wave, epileptiform discharge, extreme delta brush (EDB and relapse or fluctuation of the disease was analyzed. The relation between EEG features and head MRI abnormalities, and also EEG features in different stages were analyzed. Results There were 19 anti-N-methyl-D-aspartate (NMDA receptor encephalitis patients, 3 anti-leucine-rich glioma-inactivated 1 (LGI1 antibody associated encephalitis and one anti-γ-aminobutyric acid B receptor (GABABR antibody associated encephalitis. The frequencies of clinical presentations were psychological or cognitive dysfunction, epileptic seizure, conscious disturbance, speech dysfunction and movement disorder in descending order. Within 30.50 d from onset, 6 patients demonstrated slow wave background, of whom 2 relapsed or fluctuated; 5 patients had α rhythm background and none of them relapsed or fluctuated. In patients with anti-NMDA receptor encephalitis, the difference in first hospital stay (Z = -0.785, P = 0.433 and relapse or fluctuation (Fisher's exact probability: P = 0.155 between EDB group and non-EDB group was not significant. There was no apparent correlation between EEG background activities and head MRI abnormalities in different stages. In ascent and climax stage, EEG background activities were predominantly slow wave, and the distribution of slow wave was relatively broader. EEG background changed to α rhythm from descent stage

  7. Evaluation of human mental stress states based on wavelet package transformation and nonlinear analysis of EEG signals%利用EEG信号的小波包变换与非线性分析实现精神疲劳状态的判定

    Institute of Scientific and Technical Information of China (English)

    韩清鹏

    2013-01-01

    EEG(脑电)信号的4个节律(δ波、θ波、α波、β波)与人的精神疲劳状态有对应关系,不同节律的能量值及其非线性特征参数可以用于疲劳状态的判定.本文首先利用小波包分解与重构技术,构造了以“db20”为基小波函数的6层分解,得到EEG信号的4个节律.然后,对4个节律信号分别计算相应的节律的频带能量比例值,这些频带能量比例值作为对人体精神状态进行评价的量化指标.通过计算EEG信号α波的非线性特征参数,包括最大Lyapunov指数、近似熵、复杂度,并将这些非线性特征参数组成疲劳状态的综合评估判据,可以实现疲劳状态的判定.10组EEG信号的分析结果表明了该本文方法的有效性,其中对疲劳和非疲劳状态的判定准确率较高,而对轻微疲劳、中等疲劳和严重疲劳三种状态的准确区分稍差一些.%The positive correlation is known between the four rhythms of human Electroencephalogram ( EEG) signals including 8 wave, 8 wave, a wave and (3 wave and human mental stress states. So the energy values of the four rhythms of EEG together with their nonlinear parameters can be used to evaluate mental stress states. Here, the four rhythms of EEG was firstly reconstructed by using the technique of wavelet package transformation, where a 6-level-frame was achieved to decompose the original EEG signal with help of the basis wavelet function of " db20". Then, the corresponding frequency-band energy ratio ( FBER) of each rhythm was calculated and used to estimate states of mental stress quantitatively. Some nonlinear parameters of a wave including maximum Lyapunov exponent, approximated entropy and complexity level were also calculated and a synthesized evaluating criterion was made to determine human mental stress states. The proposed method was verified to be effective with 10 sets of EEG data. It was shown that its accuracy is higher when evaluating fatigue or non-fatigue states

  8. Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

    Directory of Open Access Journals (Sweden)

    Yvonne Höller

    2017-09-01

    Full Text Available Single photon emission computed tomography (SPECT and Electroencephalography (EEG have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD, 69 patients with depressive cognitive impairment (DCI, 71 patients with amnestic mild cognitive impairment (aMCI, and 41 patients with amnestic subjective cognitive complaints (aSCC. We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%, aMCI vs. AD (70%, and AD vs. DCI (100%, while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82% and aMCI vs. DCI (90%. Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%. In general, accuracies were higher when measures of interaction (i.e., connectivity measures were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become

  9. Analysis of Age Dependent Effects of Heat Stress on EEG Frequency Components in Rats

    Institute of Scientific and Technical Information of China (English)

    RAKESH KUMAR SINHA

    2009-01-01

    Objective To demonstrate changes in different frequencies of cerebral electrical activity or electroencephalogram (EEG) following exposure to high environmental heat in three different age groups of freely moving rats. Methods Rats were divided into three groups (i) acute heat stress - subjected to a single exposure for four hours at 38 ℃; (ii) chronic heat stress -exposed for 21 days daily for one hour at 38 ℃, and (iii) handling control groups. The digital polygraphic sleep-EEG recordings were performed just after the heat exposure from acute stressed rats and on 22nd day from chronic stressed rats by simultaneous recording of cortical EEG EOG (electrooculogram), and EMG (electromyogram). Further, power spectrum analyses were performed to analyze the effects of heat stress. Results The frequency analysis of EEG signals following exposure to high environmental heat revealed that in all three age groups of rats, changes in higher frequency components (β2) were significant in all sleep-wake states following both acute and chronic heat stress conditions. After exposure to acute heat, significant changes in EEG frequencies with respect to their control groups were observed, which were reversed partly or fully in four hours of EEG recording. On the other hand, due to repetitive chronic exposure to hot environment, adaptive and long-term changes in EEG frequency patterns were observed. Conclusion The present study has exhibited that the cortical EEG is sensitive to environmental heat and alterations in EEG frequencies in different sleep-wake states due to heat stress can be differentiated efficiently by EEG power spectrum analysis.

  10. Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

    Science.gov (United States)

    Höller, Yvonne; Bathke, Arne C.; Uhl, Andreas; Strobl, Nicolas; Lang, Adelheid; Bergmann, Jürgen; Nardone, Raffaele; Rossini, Fabio; Zauner, Harald; Kirschner, Margarita; Jahanbekam, Amirhossein; Trinka, Eugen; Staffen, Wolfgang

    2017-01-01

    Single photon emission computed tomography (SPECT) and Electroencephalography (EEG) have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity) in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD), 69 patients with depressive cognitive impairment (DCI), 71 patients with amnestic mild cognitive impairment (aMCI), and 41 patients with amnestic subjective cognitive complaints (aSCC). We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO)-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%), aMCI vs. AD (70%), and AD vs. DCI (100%), while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82%) and aMCI vs. DCI (90%). Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%). In general, accuracies were higher when measures of interaction (i.e., connectivity measures) were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become an

  11. Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

    Science.gov (United States)

    Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

    2016-10-01

    In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

  12. Hypnotic assessment based on the recurrence quantification analysis of EEG recorded in the ordinary state of consciousness.

    Science.gov (United States)

    Madeo, Dario; Castellani, Eleonora; Santarcangelo, Enrica L; Mocenni, Chiara

    2013-11-01

    The cerebral cortical correlates of the susceptibility to hypnosis in the ordinary states of consciousness have not been clarified. Aim of the study was to characterize the EEG dynamics of subjects with high (highs) and low hypnotisability (lows) through the non-linear method of Recurrence Quantification Analysis (RQA). The EEG of 16 males--8 highs and 8 lows--was monitored for 1min without instructions other than keeping the eyes closed, being silent and avoiding movements (short resting), and during 15 min of simple relaxation, that is with the instruction to relax at their best. Highs and lows were compared on the RQA measures of Determinism (DET) and Entropy (ENT), which are related to the signal determinism and complexity. In the short resting condition discriminant analysis could classify highs and lows on the basis of DET and ENT values at temporo-parietal sites. Many differences in DET and all differences in ENT disappeared during simple relaxation, although DET still separated the two groups in the earliest 6min of relaxation at temporo-parietal sites. Our RQA based approach allows to develop computer-based methods of hypnotic assessment using short-lasting, single channel EEG recordings analyzed through standard mathematical methods.

  13. Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

    Science.gov (United States)

    Li, Yang; Wei, Hua-Liang; Billings, Stephen. A.; Sarrigiannis, P. G.

    2016-08-01

    The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.

  14. Nonlinear analysis of EAS clusters

    CERN Document Server

    Zotov, M Yu; Fomin, Y A; Fomin, Yu. A.

    2002-01-01

    We apply certain methods of nonlinear time series analysis to the extensive air shower clusters found earlier in the data set obtained with the EAS-1000 Prototype array. In particular, we use the Grassberger-Procaccia algorithm to compute the correlation dimension of samples in the vicinity of the clusters. The validity of the results is checked by surrogate data tests and some additional quantities. We compare our conclusions with the results of similar investigations performed by the EAS-TOP and LAAS groups.

  15. Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis.

    Science.gov (United States)

    Timothy, Leena T; Krishna, Bindu M; Nair, Usha

    2017-10-01

    The present study is aimed at the classification of mild cognitive impairment (MCI) EEG by combining complexity and synchronization features based on quantifiers from the common platform of recurrence based analysis. Recurrence rate (RR) of recurrence quantification analysis (RQA) is used for complexity analysis and RR of cross recurrence quantification analysis (CRQA) is used for synchronization analysis. The investigations are carried out on EEG from two states (i) resting eyes closed (EC) and (ii) short term memory task (STM).The results of our analysis show lower levels of complexity and higher levels of inter and intra hemispheric synchronisation in the MCI EEG compared to that of normal controls (NC) as indicated by the statistically significant higher value of RQA RR and CRQA RR. The results also evidence the effectiveness of memory activation task by bringing out the characteristic features of MCI EEG in task specific regions of temporal, parietal and frontal lobes under the STM condition.A new approach of combining complexity and synchronization features for EEG classification of MCI subjects is proposed, based on the geometrical signal separation in a feature space formed by RQA and CRQA RR values. The results of linear classification analysis of MCI and NC EEG also reveals the effectiveness of task state analysis by the enhanced classification efficiency under the cognitive load of STM condition compared to that of EC condition. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Bayesian Correlated Component Analysis for inference of joint EEG activation

    DEFF Research Database (Denmark)

    Poulsen, Andreas Trier; Kamronn, Simon Due; Parra, Lucas

    2014-01-01

    We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset....

  17. Review on solving the inverse problem in EEG source analysis

    Directory of Open Access Journals (Sweden)

    Fabri Simon G

    2008-11-01

    Full Text Available Abstract In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF, SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF

  18. Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest.

    Science.gov (United States)

    Noirhomme, Quentin; Lehembre, Rémy; Lugo, Zulay Del Rosario; Lesenfants, Damien; Luxen, André; Laureys, Steven; Oddo, Mauro; Rossetti, Andrea O

    2014-01-01

    Visual analysis of electroencephalography (EEG) background and reactivity during therapeutic hypothermia provides important outcome information, but is time-consuming and not always consistent between reviewers. Automated EEG analysis may help quantify the brain damage. Forty-six comatose patients in therapeutic hypothermia, after cardiac arrest, were included in the study. EEG background was quantified with burst-suppression ratio (BSR) and approximate entropy, both used to monitor anesthesia. Reactivity was detected through change in the power spectrum of signal before and after stimulation. Automatic results obtained almost perfect agreement (discontinuity) to substantial agreement (background reactivity) with a visual score from EEG-certified neurologists. Burst-suppression ratio was more suited to distinguish continuous EEG background from burst-suppression than approximate entropy in this specific population. Automatic EEG background and reactivity measures were significantly related to good and poor outcome. We conclude that quantitative EEG measurements can provide promising information regarding current state of the patient and clinical outcome, but further work is needed before routine application in a clinical setting.

  19. CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis

    Directory of Open Access Journals (Sweden)

    Federico Raimondo

    2012-01-01

    Full Text Available In recent years, Independent Component Analysis (ICA has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation.

  20. CUDAICA: GPU optimization of Infomax-ICA EEG analysis.

    Science.gov (United States)

    Raimondo, Federico; Kamienkowski, Juan E; Sigman, Mariano; Fernandez Slezak, Diego

    2012-01-01

    In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation.

  1. Entropy of the EEG in transition to burst suppression in deep anesthesia: Surrogate analysis.

    Science.gov (United States)

    Anier, Andres; Lipping, Tarmo; Jantti, Ville; Puumala, Pasi; Huotari, Ari-Matti

    2010-01-01

    In this paper 5 methods for the assessment of signal entropy are compared in their capability to follow the changes in the EEG signal during transition from continuous EEG to burst suppression in deep anesthesia. To study the sensitivity of the measures to phase information in the signal, phase randomization as well as amplitude adjusted surrogates are also analyzed. We show that the selection of algorithm parameters and the use of normalization are important issues in interpretation and comparison of the results. We also show that permutation entropy is the most sensitive to phase information among the studied measures and that the EEG signal during high amplitude delta activity in deep anesthesia is of highly nonlinear nature.

  2. Chaotic time series analysis of vision evoked EEG

    Science.gov (United States)

    Zhang, Ningning; Wang, Hong

    2010-01-01

    To investigate the human brain activities for aesthetic processing, beautiful woman face picture and ugly buffoon face picture were applied. Twelve subjects were assigned the aesthetic processing task while the electroencephalogram (EEG) was recorded. Event-related brain potential (ERP) was required from the 32 scalp electrodes and the ugly buffoon picture produced larger amplitudes for the N1, P2, N2, and late slow wave components. Average ERP from the ugly buffoon picture were larger than that from the beautiful woman picture. The ERP signals shows that the ugly buffoon elite higher emotion waves than the beautiful woman face, because some expression is on the face of the buffoon. Then, chaos time series analysis was carried out to calculate the largest Lyapunov exponent using small data set method and the correlation dimension using G-P algorithm. The results show that the largest Lyapunov exponents of the ERP signals are greater than zero, which indicate that the ERP signals may be chaotic. The correlations dimensions coming from the beautiful woman picture are larger than that from the ugly buffoon picture. The comparison of the correlations dimensions shows that the beautiful face can excite the brain nerve cells. The research in the paper is a persuasive proof to the opinion that cerebrum's work is chaotic under some picture stimuli.

  3. EEG Power Spectrum Analysis in Children with ADHD

    Science.gov (United States)

    Kamida, Akira; Shimabayashi, Kenta; Oguri, Masayoshi; Takamori, Toshihiro; Ueda, Naoyuki; Koyanagi, Yuki; Sannomiya, Naoko; Nagira, Haruki; Ikunishi, Saeko; Hattori, Yuiko; Sato, Kengo; Fukuda, Chisako; Hirooka, Yasuaki; Maegaki, Yoshihiro

    2016-01-01

    Background Attention deficit disorder/hyperactivity disorder (ADHD) is a pathological condition that is not fully understood. In this study, we investigated electroencephalographic (EEG) power differences between children with ADHD and healthy control children. Methods EEGs were recorded as part of routine medical care received by 80 children with ADHD aged 4–15 years at the Department of Pediatric Neurology in Tottori University Hospital. Additionally, we recorded in 59 control children aged 4–15 years after obtaining informed consent. Specifically, awake EEG signals were recorded from each child using the international 10–20 system, and we used ten 3-s epochs on the EEG power spectrum to calculate the powers of individual EEG frequency bands. Results The powers of different EEG bands were significantly higher in the frontal brain region of those in the ADHD group compared with the control group. In addition, the power of the beta band in the ADHD group was significantly higher in all brain regions, except for the occipital region, compared with control children. With regard to developmental changes, the power of the alpha band in the occipital region showed an age-dependent decrease in both groups, with slightly lower power in the ADHD group. Additionally, the intergroup difference decreased in children aged 11 years or older. As with the alpha band in the occipital region, the beta band in the frontal region showed an age-dependent decrease in both groups. Unlike the alpha band, the power of the beta band was higher in the ADHD group than in the control group for children of all ages. Conclusion The observed intergroup differences in EEG power may provide insight into the brain function of children with ADHD. PMID:27493489

  4. Distributed analysis of simultaneous EEG-fMRI time-series: modeling and interpretation issues.

    Science.gov (United States)

    Esposito, Fabrizio; Aragri, Adriana; Piccoli, Tommaso; Tedeschi, Gioacchino; Goebel, Rainer; Di Salle, Francesco

    2009-10-01

    Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales. The purpose of this article is to illustrate the combination of simultaneously recorded EEG and fMRI signals exploiting the principles of EEG distributed source modeling. We define a common source space for fMRI and EEG signal projection and gather a conceptually unique framework for the spatial and temporal comparative analysis. We illustrate this framework in a graded-load working-memory simultaneous EEG-fMRI experiment based on the n-back task where sustained load-dependent changes in the blood-oxygenation-level-dependent (BOLD) signals during continuous item memorization co-occur with parametric changes in the EEG theta power induced at each single item. In line with previous studies, we demonstrate on two single-subject cases how the presented approach is capable of colocalizing in midline frontal regions two phenomena simultaneously observed at different temporal scales, such as the sustained negative changes in BOLD activity and the parametric EEG theta synchronization. We discuss the presented approach in relation to modeling and interpretation issues typically arising in simultaneous EEG-fMRI studies.

  5. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

    Science.gov (United States)

    Wagatsuma, Hiroaki

    2017-01-01

    EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

  6. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

    Directory of Open Access Journals (Sweden)

    Balbir Singh

    2017-01-01

    Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

  7. Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

    Science.gov (United States)

    Somers, Ben; Bertrand, Alexander

    2016-12-01

    Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

  8. Nonlinear structural analysis using integrated force method

    Indian Academy of Sciences (India)

    N R B Krishnam Raju; J Nagabhushanam

    2000-08-01

    Though the use of the integrated force method for linear investigations is well-recognised, no efforts were made to extend this method to nonlinear structural analysis. This paper presents the attempts to use this method for analysing nonlinear structures. General formulation of nonlinear structural analysis is given. Typically highly nonlinear bench-mark problems are considered. The characteristic matrices of the elements used in these problems are developed and later these structures are analysed. The results of the analysis are compared with the results of the displacement method. It has been demonstrated that the integrated force method is equally viable and efficient as compared to the displacement method.

  9. Comparative Analysis of EEG Signals Based on Complexity Measure

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 an...

  10. A COMPARISION BETWEEN WALSHHADAMARD AND FOURIER ANALYSIS OF THE EEG SIGNALS

    Directory of Open Access Journals (Sweden)

    AZADEH BASTANI

    2011-07-01

    Full Text Available Electroencephalography (EEG is one of the most important diagnostic tools in neurology and getting information about the brain activity. One of this is real-time and quantified study of brain activities to measure the stage of unconsciousness due to injection drug in operation room. EEG signal is a stochastic non-stationary process. Regarding the complexity of brain activities on EEG process, studies are based on time-frequency features analysis of EEG signals. Most of these analyses are based on Fourier Transform and the most significant are classic and parametric estimation of power spectral density analysis. Considering the origins of EEG in the brain, it seems that Walsh-Hadamard transform is more effective than Fourier transform in feature extracting of these signals. In this paper the efficiency of Walsh-Hadamard transform features were comparedwith extracted features from Fourier transform. To evaluate these features, three different classifying algorithms are used. The results showed that Walsh-Hadamard extracted features are suitable tools for recognition of difference between different stages of EEG signals. Simplicity and speed of Walsh-Hadamard transform calculation made it preferable then Fourier spectral features. The fast Walsh-Hadamard transform is an attractive alternative to the fast fourier transform because it is computationally more efficient, and thus faster to perform on a digital computer.

  11. ELAN: a software package for analysis and visualization of MEG, EEG, and LFP signals.

    Science.gov (United States)

    Aguera, Pierre-Emmanuel; Jerbi, Karim; Caclin, Anne; Bertrand, Olivier

    2011-01-01

    The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and local field potentials (LFPs). The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes.

  12. Introduction to nonlinear finite element analysis

    CERN Document Server

    Kim, Nam-Ho

    2015-01-01

    This book introduces the key concepts of nonlinear finite element analysis procedures. The book explains the fundamental theories of the field and provides instructions on how to apply the concepts to solving practical engineering problems. Instead of covering many nonlinear problems, the book focuses on three representative problems: nonlinear elasticity, elastoplasticity, and contact problems. The book is written independent of any particular software, but tutorials and examples using four commercial programs are included as appendices: ANSYS, NASTRAN, ABAQUS, and MATLAB. In particular, the MATLAB program includes all source codes so that students can develop their own material models, or different algorithms. This book also: ·         Presents clear explanations of nonlinear finite element analysis for elasticity, elastoplasticity, and contact problems ·         Includes many informative examples of nonlinear analyses so that students can clearly understand the nonlinear theory ·    ...

  13. Tracking non-stationary EEG sources using adaptive online recursive independent component analysis.

    Science.gov (United States)

    Hsu, Sheng-Hsiou; Pion-Tonachini, Luca; Jung, Tzyy-Ping; Cauwenberghs, Gert

    2015-01-01

    Electroencephalographic (EEG) source-level analyses such as independent component analysis (ICA) have uncovered features related to human cognitive functions or artifactual activities. Among these methods, Online Recursive ICA (ORICA) has been shown to achieve fast convergence in decomposing high-density EEG data for real-time applications. However, its adaptation performance has not been fully explored due to the difficulty in choosing an appropriate forgetting factor: the weight applied to new data in a recursive update which determines the trade-off between the adaptation capability and convergence quality. This study proposes an adaptive forgetting factor for ORICA (adaptive ORICA) to learn and adapt to non-stationarity in the EEG data. Using a realistically simulated non-stationary EEG dataset, we empirically show adaptive forgetting factors outperform other commonly-used non-adaptive rules when underlying source dynamics are changing. Standard offline ICA can only extract a subset of the changing sources while adaptive ORICA can recover all. Applied to actual EEG data recorded from a task-switching experiments, adaptive ORICA can learn and re-learn the task-related components as they change. With an adaptive forgetting factor, adaptive ORICA can track non-stationary EEG sources, opening many new online applications in brain-computer interfaces and in monitoring of brain dynamics.

  14. Stochastic relevance analysis of epileptic EEG signals for channel selection and classification.

    Science.gov (United States)

    Duque-Muñoz, L; Guerrero-Mosquera, C; Castellanos-Dominguez, G

    2013-01-01

    Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain relevant features extracted from time-frequency plane for epileptic EEG signals. Particularly, EEG features are extracted by common spectral methods such as short time Fourier transform (STFT), wavelets transform and Empirical Mode Decomposition (EMD). Then, each method is evaluated by Stochastic Relevance Analysis (SRA) that is further used for EEG classification and channel selection. The classification measures are carried out based on the performance of the k-NN classifier, while the channels selected are validated by visual inspection and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. Results obtained in this paper show that SRA is a good alternative for automatic seizure detection and also opens the possibility of formulating new criteria to select, classify or analyze abnormal EEG channels.

  15. Analysis of Nonlinear Electromagnetic Metamaterials

    CERN Document Server

    Poutrina, Ekaterina; Smith, David R

    2010-01-01

    We analyze the properties of a nonlinear metamaterial formed by integrating nonlinear components or materials into the capacitive regions of metamaterial elements. A straightforward homogenization procedure leads to general expressions for the nonlinear susceptibilities of the composite metamaterial medium. The expressions are convenient, as they enable inhomogeneous system of scattering elements to be described as a continuous medium using the standard notation of nonlinear optics. We illustrate the validity and accuracy of our theoretical framework by performing measurements on a fabricated metamaterial sample composed of an array of split ring resonators (SRRs) with packaged varactors embedded in the capacitive gaps in a manner similar to that of Wang et al. [Opt. Express 16, 16058 (2008)]. Because the SRRs exhibit a predominant magnetic response to electromagnetic fields, the varactor-loaded SRR composite can be described as a magnetic material with nonlinear terms in its effective magnetic susceptibility...

  16. Nonlinear dynamical systems effects of homeopathic remedies on multiscale entropy and correlation dimension of slow wave sleep EEG in young adults with histories of coffee-induced insomnia.

    Science.gov (United States)

    Bell, Iris R; Howerter, Amy; Jackson, Nicholas; Aickin, Mikel; Bootzin, Richard R; Brooks, Audrey J

    2012-07-01

    Investigators of homeopathy have proposed that nonlinear dynamical systems (NDS) and complex systems science offer conceptual and analytic tools for evaluating homeopathic remedy effects. Previous animal studies demonstrate that homeopathic medicines alter delta electroencephalographic (EEG) slow wave sleep. The present study extended findings of remedy-related sleep stage alterations in human subjects by testing the feasibility of using two different NDS analytic approaches to assess remedy effects on human slow wave sleep EEG. Subjects (N=54) were young adult male and female college students with a history of coffee-related insomnia who participated in a larger 4-week study of the polysomnographic effects of homeopathic medicines on home-based all-night sleep recordings. Subjects took one bedtime dose of a homeopathic remedy (Coffea cruda or Nux vomica 30c). We computed multiscale entropy (MSE) and the correlation dimension (Mekler-D2) for stages 3 and 4 slow wave sleep EEG sampled in artifact-free 2-min segments during the first two rapid-eye-movement (REM) cycles for remedy and post-remedy nights, controlling for placebo and post-placebo night effects. MSE results indicate significant, remedy-specific directional effects, especially later in the night (REM cycle 2) (CC: remedy night increases and post-remedy night decreases in MSE at multiple sites for both stages 3 and 4 in both REM cycles; NV: remedy night decreases and post-remedy night increases, mainly in stage 3 REM cycle 2 MSE). D2 analyses yielded more sporadic and inconsistent findings. Homeopathic medicines Coffea cruda and Nux vomica in 30c potencies alter short-term nonlinear dynamic parameters of slow wave sleep EEG in healthy young adults. MSE may provide a more sensitive NDS analytic method than D2 for evaluating homeopathic remedy effects on human sleep EEG patterns. Copyright © 2012 The Faculty of Homeopathy. Published by Elsevier Ltd. All rights reserved.

  17. Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

    Directory of Open Access Journals (Sweden)

    Muthuraman Muthuraman

    Full Text Available Electroencephalography (EEG and magnetoencephalography (MEG are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC. MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

  18. Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

    Science.gov (United States)

    Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan

    2014-01-01

    Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

  19. Spectral theory and nonlinear functional analysis

    CERN Document Server

    Lopez-Gomez, Julian

    2001-01-01

    This Research Note addresses several pivotal problems in spectral theory and nonlinear functional analysis in connection with the analysis of the structure of the set of zeroes of a general class of nonlinear operators. It features the construction of an optimal algebraic/analytic invariant for calculating the Leray-Schauder degree, new methods for solving nonlinear equations in Banach spaces, and general properties of components of solutions sets presented with minimal use of topological tools. The author also gives several applications of the abstract theory to reaction diffusion equations and systems.The results presented cover a thirty-year period and include recent, unpublished findings of the author and his coworkers. Appealing to a broad audience, Spectral Theory and Nonlinear Functional Analysis contains many important contributions to linear algebra, linear and nonlinear functional analysis, and topology and opens the door for further advances.

  20. Characterizing Alzheimer's disease severity via resting-awake EEG amplitude modulation analysis.

    Directory of Open Access Journals (Sweden)

    Francisco J Fraga

    Full Text Available Changes in electroencephalography (EEG amplitude modulations have recently been linked with early-stage Alzheimer's disease (AD. Existing tools available to perform such analysis (e.g., detrended fluctuation analysis, however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD showed significant differences in amplitude modulations between the three groups. Most notably, i delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD, ii delta modulation of the theta band appeared with an increase in severity, and iii delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer's disease, but also to monitor its progression.

  1. Preparing laboratory and real-world EEG data for large-scale analysis: A containerized approach

    Directory of Open Access Journals (Sweden)

    Nima eBigdely-Shamlo

    2016-03-01

    Full Text Available Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface (BCI models.. However, the absence of standard-ized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the diffi-culty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a containerized approach and freely available tools we have developed to facilitate the process of an-notating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-analysis. The EEG Study Schema (ESS comprises three data Levels, each with its own XML-document schema and file/folder convention, plus a standardized (PREP pipeline to move raw (Data Level 1 data to a basic preprocessed state (Data Level 2 suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are in-creasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at eegstudy.org, and a central cata-log of over 850 GB of existing data in ESS format is available at study-catalog.org. These tools and resources are part of a larger effort to ena-ble data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org.

  2. EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods.

    Science.gov (United States)

    Lei, Xu; Valdes-Sosa, Pedro A; Yao, Dezhong

    2012-09-01

    Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

  3. MEG and EEG data analysis with MNE-Python

    OpenAIRE

    Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A.; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti

    2013-01-01

    Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Pyt...

  4. MEG and EEG data analysis with MNE-Python

    OpenAIRE

    Alexandre eGramfort; Martin eLuessi; Eric eLarson; Engemann, Denis A.; Daniel eStrohmeier; Christian eBrodbeck; Roman eGoj; Mainak eJas; Teon eBrooks; Lauri eParkkonen; Matti eHämäläinen

    2013-01-01

    Magnetoencephalography and electroencephalography (M/EEG) measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python tha...

  5. MEG and EEG data analysis with MNE-Python.

    OpenAIRE

    Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A.; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti

    2013-01-01

    Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Pyt...

  6. Advanced Signal Processing and Machine Learning Approaches for EEG Analysis

    Science.gov (United States)

    2010-07-01

    Transform (DCT) or the Discrete Wavelet Transform (DWT) for natural images. As EEG signals tend to be quite noisy, a good general model is hard to...illustration of subspace denoise method Figure 8. Absolute correlation coefficients between “good” channel 88 and 14 “bad” channels after applying the...subspace denoise method. The x-axis denotes the subspace dimension M, and the y-axis is the maximum absolute correlation coefficient of channel 88

  7. EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

    Directory of Open Access Journals (Sweden)

    Tom Eichele

    2011-01-01

    Full Text Available Independent component analysis (ICA is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.

  8. Analysis and simulation of brain signal data by EEG signal processing technique using MATLAB

    Directory of Open Access Journals (Sweden)

    Sasikumar Gurumurthy

    2013-06-01

    Full Text Available EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. MATLAB provides an interactive graphic user interface (GUI allowing users to flexiblyand interactively process their high-density EEG dataset and other brain signal data different techniques such as independent component analysis (ICA and/or time/frequency analysis (TFA, as well as standard averaging methods. We will be showing different brain signals by comparing, analysing and simulating datasets which is already loaded in the MATLAB software to process the EEG signals.

  9. Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis.

    Science.gov (United States)

    Wessel, Jan R

    2016-03-08

    Temporal independent component analysis (ICA) is applied to an electrophysiological signal mixture (such as an EEG recording) to disentangle the independent neural source signals-independent components-underlying said signal mixture. When applied to scalp EEG, ICA is most commonly used either as a pre-processing step (e.g., to isolate physiological processes from non-physiological artifacts), or as a data-reduction step (i.e., to focus on one specific neural process with increased signal-to-noise ratio). However, ICA can be used in an even more powerful way that fundamentally expands the inferential utility of scalp EEG. The core assumption of EEG-ICA-namely, that individual independent components represent separable neural processes-can be leveraged to derive the following inferential logic: If a specific independent component shows activity related to multiple psychological processes within the same dataset (e.g., elicited by different experimental events), it follows that those psychological processes involve a common, non-separable neural mechanism. As such, this logic allows testing a class of hypotheses that is beyond the reach of regular EEG analyses techniques, thereby crucially increasing the inferential utility of the EEG. In the current article, this logic will be referred to as the 'common independent process identification' (CIPI) approach. This article aims to provide a tutorial into the application of this powerful approach, targeted at researchers that have a basic understanding of standard EEG analysis. Furthermore, the article aims to exemplify the usage of CIPI by outlining recent studies that successfully applied this approach to test neural theories of mental functions.

  10. Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Yuan-Pin Lin

    2017-07-01

    Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

  11. Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis.

    Science.gov (United States)

    Lin, Yuan-Pin; Jao, Ping-Keng; Yang, Yi-Hsuan

    2017-01-01

    Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the

  12. Review on solving the forward problem in EEG source analysis

    Directory of Open Access Journals (Sweden)

    Vergult Anneleen

    2007-11-01

    Full Text Available Abstract Background The aim of electroencephalogram (EEG source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter. In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM, the finite element method (FEM and the finite difference method (FDM. In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative

  13. Dynamics and vibrations progress in nonlinear analysis

    CERN Document Server

    Kachapi, Seyed Habibollah Hashemi

    2014-01-01

    Dynamical and vibratory systems are basically an application of mathematics and applied sciences to the solution of real world problems. Before being able to solve real world problems, it is necessary to carefully study dynamical and vibratory systems and solve all available problems in case of linear and nonlinear equations using analytical and numerical methods. It is of great importance to study nonlinearity in dynamics and vibration; because almost all applied processes act nonlinearly, and on the other hand, nonlinear analysis of complex systems is one of the most important and complicated tasks, especially in engineering and applied sciences problems. There are probably a handful of books on nonlinear dynamics and vibrations analysis. Some of these books are written at a fundamental level that may not meet ambitious engineering program requirements. Others are specialized in certain fields of oscillatory systems, including modeling and simulations. In this book, we attempt to strike a balance between th...

  14. Prediction of advertisement preference by fusing EEG response and sentiment analysis.

    Science.gov (United States)

    Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

    2017-02-16

    This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data.

  15. QUANTITATIVE EEG COMPARATIVE ANALYSIS BETWEEN AUTISM SPECTRUM DISORDER (ASD AND ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD

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    Plamen D. Dimitrov

    2017-01-01

    Full Text Available Background: Autism is a mental developmental disorder, manifested in the early childhood. Attention deficit hyperactivity disorder is another psychiatric condition of the neurodevelopmental type. Both disorders affect information processing in the nervous system, altering the mechanisms which control how neurons and their synapses are connected and organized. Purpose: To examine if quantitative EEG assessment is sensitive and simple enough to differentiate autism from attention deficit hyperactivity disorder and neurologically typical children. Material and methods: Quantitative EEG is a type of electrophysiological assessment that uses computerized mathematical analysis to convert the raw waveform data into different frequency ranges. Each frequency range is averaged across a sample of data and quantified into mean amplitude (voltage in microvolts mV. We performed quantitative EEG analysis and compared 4 cohorts of children (aged from 3 to 7 years: with autism (high [n=27] and low [n=52] functioning, with attention deficit hyperactivity disorder [n=34], and with typical behavior [n75]. Results: Our preliminary results show that there are significant qEEG differences between the groups of patients and the control cohort. The changes affect the potential levels of delta-, theta-, alpha-, and beta- frequency spectrums. Conclusion: The present study shows some significant quantitative EEG findings in autistic patients. This is a step forward in our efforts, aimed at defining specific neurophysiologic changes, in order to develop and refine strategies for early diagnosis of autism spectrum disorders, differentiation from other development conditions in childhood, detection of specific biomarkers and early initiation of treatment.

  16. Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking.

    Directory of Open Access Journals (Sweden)

    Kristine Lynne Snyder

    2015-12-01

    Full Text Available There has been a recent surge in the use of electroencephalography (EEG as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

  17. Wavelet-based texture analysis of EEG signal for prediction of epileptic seizure

    Science.gov (United States)

    Petrosian, Arthur A.; Homan, Richard; Pemmaraju, Suryalakshmi; Mitra, Sunanda

    1995-09-01

    Electroencephalographic (EEG) signal texture content analysis has been proposed for early warning of an epileptic seizure. This approach was evaluated by investigating the interrelationship between texture features and basic signal informational characteristics, such as Kolmogorov complexity and fractal dimension. The comparison of several traditional techniques, including higher-order FIR digital filtering, chaos, autoregressive and FFT time- frequency analysis was also carried out on the same epileptic EEG recording. The purpose of this study is to investigate whether wavelet transform can be used to further enhance the developed methods for prediction of epileptic seizures. The combined consideration of texture and entropy characteristics extracted from subsignals decomposed by wavelet transform are explored for that purpose. Yet, the novel neuro-fuzzy clustering algorithm is performed on wavelet coefficients to segment given EEG recording into different stages prior to an actual seizure onset.

  18. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

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    Mariel Rosenblatt

    2014-11-01

    Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.

  19. Nonlinear analysis and modeling of cortical activation and deactivation patterns in the immature fetal electrocorticogram

    Science.gov (United States)

    Schwab, Karin; Groh, Tobias; Schwab, Matthias; Witte, Herbert

    2009-03-01

    An approach combining time-continuous nonlinear stability analysis and a parametric bispectral method was introduced to better describe cortical activation and deactivation patterns in the immature fetal electroencephalogram (EEG). Signal models and data-driven investigations were performed to find optimal parameters of the nonlinear methods and to confirm the occurrence of nonlinear sections in the fetal EEG. The resulting measures were applied to the in utero electrocorticogram (ECoG) of fetal sheep at 0.7 gestation when organized sleep states were not developed and compared to previous results at 0.9 gestation. Cycling of the nonlinear stability of the fetal ECoG occurred already at this early gestational age, suggesting the presence of premature sleep states. This was accompanied by cycling of the time-variant biamplitude which reflected ECoG synchronization effects during premature sleep states associated with nonrapid eye movement sleep later in gestation. Thus, the combined nonlinear and time-variant approach was able to provide important insights into the properties of the immature fetal ECoG.

  20. Nonlinear Finite Element Analysis of Ocean Cables

    Institute of Scientific and Technical Information of China (English)

    Nam-Il KIM; Sang-Soo JEON; Moon-Young KIM

    2004-01-01

    This study has focused on developing numerical procedures for the dynamic nonlinear analysis of cable structures subjected to wave forces and ground motions in the ocean. A geometrically nonlinear finite element procedure using the isoparametric curved cable element based on the Lagrangian formulation is briefly summarized. A simple and accurate method to determine the initial equilibrium state of cable systems associated with self-weights, buoyancy and the motion of end points is presented using the load incremental method combined with penalty method. Also the Newmark method is used for dynamic nonlinear analysis of ocean cables. Numerical examples are presented to validate the present numerical method.

  1. Cognitive neuroscience of creativity: EEG based approaches.

    Science.gov (United States)

    Srinivasan, Narayanan

    2007-05-01

    Cognitive neuroscience of creativity has been extensively studied using non-invasive electrical recordings from the scalp called electroencephalograms (EEGs) and event related potentials (ERPs). The paper discusses major aspects of performing research using EEG/ERP based experiments including the recording of the signals, removing noise, estimating ERP signals, and signal analysis for better understanding of the neural correlates of processes involved in creativity. Important factors to be kept in mind to record clean EEG signal in creativity research are discussed. The recorded EEG signal can be corrupted by various sources of noise and methodologies to handle the presence of unwanted artifacts and filtering noise are presented followed by methods to estimate ERPs from the EEG signals from multiple trials. The EEG and ERP signals are further analyzed using various techniques including spectral analysis, coherence analysis, and non-linear signal analysis. These analysis techniques provide a way to understand the spatial activations and temporal development of large scale electrical activity in the brain during creative tasks. The use of this methodology will further enhance our understanding the processes neural and cognitive processes involved in creativity.

  2. EEG Neurofeedback treatments in children with ADHD: An updated meta-analysis of Randomized Controlled Trials

    Directory of Open Access Journals (Sweden)

    Jean-Arthur eMicoulaud Franchi

    2014-11-01

    Full Text Available Objective We undertook a meta-analysis of published Randomized Controlled Trials (RCT with semi-active control and sham-NF groups to determine whether EEG-NF significantly improves the overall symptoms, inattention and hyperactivity/impulsivity dimensions for probably unblinded assessment (parent assessment and probably blinded assessment (teacher assessment in children with Attention Deficit Hyperactivity Disorder (ADHD.Data Sources A systematic review identified independent studies that were eligible for inclusion in a random effects meta-analysis.Data Extraction Effect sizes for ADHD symptoms were expressed as standardized mean differences (SMD with 95% confidence intervals.ResultsFive identified studies met eligibility criteria, 263 patients with ADHD were included, 146 patients were trained with EEG-NF. On parent assessment (probably unblinded assessment, the overall ADHD score (SMD=-0.49 [-0.74, -0.24], the inattention score (SMD=-0.46 [-0.76, -0.15] and the hyperactivity/impulsivity score (SMD=-0.34 [-0.59, -0.09] were significantly improved in patients receiving EEG-NF compared to controls. On teacher assessment (probably blinded assessment, only the inattention score was significantly improved in patients receiving EEG-NF compared to controls (SMD=-0.30 [-0.58, -0.03]. ConclusionsThis meta-analysis of EEG-NF in children with ADHD highlights improvement in the inattention dimension of ADHD symptoms. Future investigations should pay greater attention to adequately blinded studies and EEG-NF protocols that carefully control the implementation and embedding of training.

  3. Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

    Directory of Open Access Journals (Sweden)

    Jose Luis Rodríguez-Sotelo

    2014-12-01

    Full Text Available Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

  4. Detection and removal of ocular artifacts from EEG signals for an automated REM sleep analysis.

    Science.gov (United States)

    Betta, Monica; Gemignani, Angelo; Landi, Alberto; Laurino, Marco; Piaggi, Paolo; Menicucci, Danilo

    2013-01-01

    Rapid eye movements (REMs) are a prominent feature of REM sleep, and their distribution and time density over the night represent important physiological and clinical parameters. At the same time, REMs produce substantial distortions on the electroencephalographic (EEG) signals, which strongly affect the significance of normal REM sleep quantitative study. In this work a new procedure for a complete and automated analysis of REM sleep is proposed, which includes both a REMs detection algorithm and an ocular artifact removal system. The two steps, based respectively on Wavelet Transform and adaptive filtering, are fully integrated and their performance is evaluated using REM simulated signals. Thanks to the integration with the detection algorithm, the proposed artifact removal system shows an enhanced accuracy in the recovering of the true EEG signal, compared to a system based on the adaptive filtering only. Finally the artifact removal system is applied to physiological data and an estimation of the actual distortion induced by REMs on EEG signals is supplied.

  5. Analysis of EEG Sleep Spindle Parameters from Apnea Patients Using Massive Computing and Decision Tree

    Directory of Open Access Journals (Sweden)

    Gunther J. L. Gerhardt

    2014-08-01

    Full Text Available In this study, Matching Pursuit (MP procedure is applied to the detection and analysis of EEG sleep spindles in patients evaluated for suspected OSAS. Elements having the frequency of EEG sleep spindles are selected from different dictionary sizes, with and without a frequency modulation function (chirp for signal description. This procedure was done with high computational cost in order to find best parameters for real EEG data description. At the end we used the atom parameters as input for a decision tree-based classifier, making possible to obtain a classification according to apnea-hypopnea index group and allowing to see how atom parameters such as frequency and amplitude are affected by the presence of sleep apnea.

  6. Graph theoretical analysis of EEG functional connectivity during music perception.

    Science.gov (United States)

    Wu, Junjie; Zhang, Junsong; Liu, Chu; Liu, Dongwei; Ding, Xiaojun; Zhou, Changle

    2012-11-05

    The present study evaluated the effect of music on large-scale structure of functional brain networks using graph theoretical concepts. While most studies on music perception used Western music as an acoustic stimulus, Guqin music, representative of Eastern music, was selected for this experiment to increase our knowledge of music perception. Electroencephalography (EEG) was recorded from non-musician volunteers in three conditions: Guqin music, noise and silence backgrounds. Phase coherence was calculated in the alpha band and between all pairs of EEG channels to construct correlation matrices. Each resulting matrix was converted into a weighted graph using a threshold, and two network measures: the clustering coefficient and characteristic path length were calculated. Music perception was found to display a higher level mean phase coherence. Over the whole range of thresholds, the clustering coefficient was larger while listening to music, whereas the path length was smaller. Networks in music background still had a shorter characteristic path length even after the correction for differences in mean synchronization level among background conditions. This topological change indicated a more optimal structure under music perception. Thus, prominent small-world properties are confirmed in functional brain networks. Furthermore, music perception shows an increase of functional connectivity and an enhancement of small-world network organizations.

  7. Incorporating priors for EEG source imaging and connectivity analysis

    Directory of Open Access Journals (Sweden)

    Xu eLei

    2015-08-01

    Full Text Available Electroencephalography source imaging (ESI is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI, functional MRI (fMRI, and positron emission tomography (PET. The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, such as EEG-correlated fMRI, temporally coherent networks and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI and neuro-stimulation techniques (transcranial magnetic stimulation, TMS are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach towards the study of brain networks in cognitive and clinical neurosciences.

  8. Dipole source analysis for readiness potential and field using simultaneously measured EEG and MEG signals.

    Science.gov (United States)

    Mideksa, K G; Hellriegel, H; Hoogenboom, N; Krause, H; Schnitzler, A; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

    2013-01-01

    Various source localization techniques have indicated the generators of each identifiable component of movement-related cortical potentials, since the discovery of the surface negative potential prior to self-paced movement by Kornhuber and Decke. Readiness potentials and fields preceding self-paced finger movements were recorded simultaneously using multichannel electroencephalography (EEG) and magnetoencephalography (MEG) from five healthy subjects. The cortical areas involved in this paradigm are the supplementary motor area (SMA) (bilateral), pre-SMA (bilateral), and contralateral motor area of the moving finger. This hypothesis is tested in this paper using the dipole source analysis independently for only EEG, only MEG, and both combined. To localize the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for spherical head models consisting of a set of connected volumes, typically representing the scalp, skull, and brain. In the source reconstruction it is to be expected that EEG predominantly localizes radially oriented sources while MEG localizes tangential sources at the desired region of the cortex. The effect of MEG on EEG is also observed when analyzing both combined data. When comparing the two head models, the spherical and the realistic head models showed similar results. The significant points for this study are comparing the source analysis between the two modalities (EEG and MEG) so as to assure that EEG is sensitive to mostly radially orientated sources while MEG is only sensitive to only tangential sources, and comparing the spherical and individual head models.

  9. Parallel versus Serial Processing Dependencies in the Perisylvian Speech Network: A Granger Analysis of Intracranial EEG Data

    Science.gov (United States)

    Gow, David W., Jr.; Keller, Corey J.; Eskandar, Emad; Meng, Nate; Cash, Sydney S.

    2009-01-01

    In this work, we apply Granger causality analysis to high spatiotemporal resolution intracranial EEG (iEEG) data to examine how different components of the left perisylvian language network interact during spoken language perception. The specific focus is on the characterization of serial versus parallel processing dependencies in the dominant…

  10. EEG patterns in persons exposed to ionizing radiation as a result of the Chernobyl accident: part 1: conventional EEG analysis.

    Science.gov (United States)

    Loganovsky, K N; Yuryev, K L

    2001-01-01

    Prospective conventional EEG study was carried out 3-5 and 10-13 years after the Chernobyl accident (1986) in patients who had acute radiation sickness and in emergency workers in 1986 ("liquidators"). Control groups comprised healthy volunteers; veterans of the Afghanistan war with posttraumatic stress disorder; veterans with mild traumatic brain injury; and patients with dyscirculatory encephalopathy. In 3-5 years after irradiation, there were irritated EEG changes with paroxysmal activity shifted to the left frontotemporal region (cortical-limbic overactivation) that were transformed 10-13 years after irradiation toward a low-voltage EEG pattern with excess of fast (beta) and slow (delta) activity together with depression of alpha and theta activity (organic brain damage with inhibition of the cortical-limbic system). Quantitative EEG is likely to be very informative for investigation of dose-effect relationships.

  11. Nonlinear ion trap stability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Mihalcea, Bogdan M; Visan, Gina G, E-mail: bmihal@infim.r [Institute for Laser, Plasma and Radiation Physics (INFLPR), Atomistilor Str. Nr. 409, 077125 Magurele-Bucharest, Jud. Ilfov (Romania)

    2010-09-01

    This paper investigates the dynamics of an ion confined in a nonlinear Paul trap. The equation of motion for the ion is shown to be consistent with the equation describing a damped, forced Duffing oscillator. All perturbing factors are taken into consideration in the approach. Moreover, the ion is considered to undergo interaction with an external electromagnetic field. The method is based on numerical integration of the equation of motion, as the system under investigation is highly nonlinear. Phase portraits and Poincare sections show that chaos is present in the associated dynamics. The system of interest exhibits fractal properties and strange attractors. The bifurcation diagrams emphasize qualitative changes of the dynamics and the onset of chaos.

  12. Sex Differences in the Sleep EEG of Young Adults : Visual Scoring and Spectral Analysis

    NARCIS (Netherlands)

    Dijk, Derk Jan; Beersma, Domien G.M.; Bloem, Gerda M.

    1989-01-01

    Baseline sleep of 13 men (mean age of 23.5 years) and 15 women (21.9 years) was analyzed. Visual scoring of the electroencephalograms (EEGs) revealed no significant differences between the sexes in the amounts of slow-wave sleep and rapid-eye-movement (REM) sleep. Spectral analysis, however, detecte

  13. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    NARCIS (Netherlands)

    Oostenveld, R.; Fries, P.; Maris, E.; Schoffelen, J.M.

    2011-01-01

    This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimenta

  14. Combining Time Frequency Representation and Parametric Analysis for the Enhancement of Transients in Sleep EEG Signal

    Science.gov (United States)

    2007-11-02

    du Sommeil , Nice, FRANCE Abstract - The study of the electroencephalographic (EEG) sig- nal contributes to sleep analysis. In the...lis, 1999, France. [2] O.Meste, A. Amargos, G. Suisse, H. Rix, “Détection automatique de fuseaux de sommeil à l’aide de représentations temps

  15. All Night Spectral Analysis of EEG Sleep in Young Adult and Middle-Aged Male Subjects

    NARCIS (Netherlands)

    Dijk, Derk Jan; Beersma, Domien G.M.; Hoofdakker, Rutger H. van den

    1989-01-01

    The sleep EEGs of 9 young adult males (age 20-28 years) and 8 middle-aged males (42-56 years) were analyzed by visual scoring and spectral analysis. In the middle-aged subjects power density in the delta, theta and sigma frequencies were attenuated as compared to the young subjects. In both age grou

  16. Short analysis of the increase of the EEG apportionment 2013; Kurzanalyse des Anstiegs der EEG-Umlage 2013

    Energy Technology Data Exchange (ETDEWEB)

    Loreck, Charlotte; Matthes, Felix C.; Hermann, Hauke; Jung, Frederieke; Emele, Lukas

    2012-10-15

    At 15th October, 2012 the transmission system operators had published the EEG apportionment (EEG - Energy Economy Law). For the year 2013. This apportionment amounts 5,277 ct/kWh for non-privileged consumers in comparison to 3,59 ct/kWh for the year 2012. The ongoing enhancement of the renewable energies increases the EEG apportionment by an amount of 0.74 ct/kWh. With 0.26 ct/kWh the photovoltaics has the largest proportion in comparison to photovoltaics. The power generation from biomass as well as from wind energy at onshore sites contribute with 0.21 ct/kWh to the EEG apportionment. The greatest item of 0.48 ct/kWh is the debit balancing of the EEG account. The liquidity reserve for the year 2013 will be enhanced to 10% of the budget deficit. The expansion of the privileged status of the power consumption increases the EEG apportionment by 0.12 ct/kWh.

  17. Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Xun Chen

    2014-01-01

    Full Text Available Electroencephalogram (EEG recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD with multiset canonical correlation analysis (MCCA. We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.

  18. Translation of EEG spatial filters from resting to motor imagery using independent component analysis.

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    Full Text Available Electroencephalogram (EEG-based brain-computer interfaces (BCIs often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%, which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%. The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters.

  19. Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach.

    Science.gov (United States)

    Jäncke, Lutz; Alahmadi, Nsreen

    2016-01-01

    In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical

  20. Feature Extraction for the Analysis of Multi-Channel EEG Signals Using Hilbert- Huang Technique

    Directory of Open Access Journals (Sweden)

    Mahipal Singh

    2016-02-01

    Full Text Available This research article seeks to propose a Hilbert-Huang transform (HHT based novel feature extraction approach for the analysis of multi-channel EEG signals using its local time scale features. The applicability of this recently developed HHT based new features has been investigated in the analysis of multi-channel EEG signals for classifying a small set of non-motor cognitive task. HHT is combination of multivariate empirical mode decomposition (MEMD and Hilbert transform (HT. At the first stage, multi-channel EEG signals (6 channels per trial per task per subject corresponding to a small set of nonmotor mental task were decomposed by using MEMD algorithm. This gives rise to adaptive i.e. data driven decomposition of the data into twelve mono component oscillatory modes known as intrinsic mode functions (IMFs and one residue function. These generated intrinsic mode functions (IMFs are multivariate i.e. mode aligned and narrowband. From the generated IMFs, most sensitive IMF has been chosen by analysing their power spectrum. Since IMFs are amplitude and frequency modulated, the chosen IMF has been analysed through their instantaneous amplitude (IA and instantaneous frequency (IF i.e. local features extracted by applying Hilbert transform on them. Finally, the discriminatory power of these local features has been investigated through statistical significance test using paired t-test. The analysis results clearly support the potential of these local features for classifying different cognitive task in EEG based Brain –Computer Interface (BCI system.

  1. Electrical source imaging and connectivity analysis to localize the seizure-onset zone based on high-density ictal scalp EEG recordings

    OpenAIRE

    Staljanssens, Willeke; Strobbe, Gregor; Van Holen, Roel; Birot, Gwenael; Michel, Christophe; Seeck, Margitta; Vulliémoz, Serge; van Mierlo, Pieter

    2015-01-01

    Functional connectivity analysis of ictal intracranial EEG (icEEG) recordings can help with seizure-onset zone (SOZ) localization in patients with focal epilepsy1. However, it would be of high clinical value to be able to localize the SOZ based on non-invasive ictal EEG recordings to better target or avoid icEEG and improve surgical outcome. In this work, we propose an approach to localize the SOZ based on non-invasive ictal high- density EEG (hd-EEG) recordings. We considered retrospectiv...

  2. EEG based topography analysis in string recognition task

    Science.gov (United States)

    Ma, Xiaofei; Huang, Xiaolin; Shen, Yuxiaotong; Qin, Zike; Ge, Yun; Chen, Ying; Ning, Xinbao

    2017-03-01

    Vision perception and recognition is a complex process, during which different parts of brain are involved depending on the specific modality of the vision target, e.g. face, character, or word. In this study, brain activities in string recognition task compared with idle control state are analyzed through topographies based on multiple measurements, i.e. sample entropy, symbolic sample entropy and normalized rhythm power, extracted from simultaneously collected scalp EEG. Our analyses show that, for most subjects, both symbolic sample entropy and normalized gamma power in string recognition task are significantly higher than those in idle state, especially at locations of P4, O2, T6 and C4. It implies that these regions are highly involved in string recognition task. Since symbolic sample entropy measures complexity, from the perspective of new information generation, and normalized rhythm power reveals the power distributions in frequency domain, complementary information about the underlying dynamics can be provided through the two types of indices.

  3. Analysis of Nonlinear Directional Couplers

    Institute of Scientific and Technical Information of China (English)

    M. Liu P. Shum; N. Q. Ngo

    2003-01-01

    @@ 1 Introduction Since the coupled-mode theory in cylindrical optical-fiber systems was proposed in 1972, the optical coupling between parallel optical waveguides has been a matter of scientific concern. Two-core fiber couplers, especially, have been studied extensively since the success of producing a two-core fiber functioning as a directional coupler in 1980. The wavelength and polarization selectivity of two-core fibers can find many applications. The nonlinear properties of the two-core fiber coupler were also inspected with the realization of an ultrafast all-optical switch.

  4. Nonlinear frequency response analysis of structural vibrations

    Science.gov (United States)

    Weeger, Oliver; Wever, Utz; Simeon, Bernd

    2014-12-01

    In this paper we present a method for nonlinear frequency response analysis of mechanical vibrations of 3-dimensional solid structures. For computing nonlinear frequency response to periodic excitations, we employ the well-established harmonic balance method. A fundamental aspect for allowing a large-scale application of the method is model order reduction of the discretized equation of motion. Therefore we propose the utilization of a modal projection method enhanced with modal derivatives, providing second-order information. For an efficient spatial discretization of continuum mechanics nonlinear partial differential equations, including large deformations and hyperelastic material laws, we employ the concept of isogeometric analysis. Isogeometric finite element methods have already been shown to possess advantages over classical finite element discretizations in terms of higher accuracy of numerical approximations in the fields of linear vibration and static large deformation analysis. With several computational examples, we demonstrate the applicability and accuracy of the modal derivative reduction method for nonlinear static computations and vibration analysis. Thus, the presented method opens a promising perspective on application of nonlinear frequency analysis to large-scale industrial problems.

  5. EEG biofeedback

    OpenAIRE

    Dvořáček, Michael

    2010-01-01

    Vznik EEG aktivity v mozku, rozdělení EEG vln podle frekvence, způsob měření EEG, přístroje pro měření EEG. Dále popis biofeedback metody, její možnosti a návrh biofeedback her. Popis zpracování naměřených EEG signálů. EEG generation, brain rhythms, methods of recording EEG, EEG recorder. Description of biofeedback, potentialities of biofeedback, proposal of biofeedback games. Description of processing measured EEG signals. B

  6. EEG biofeedback

    OpenAIRE

    Dvořáček, Michael

    2010-01-01

    Vznik EEG aktivity v mozku, rozdělení EEG vln podle frekvence, způsob měření EEG, přístroje pro měření EEG. Dále popis biofeedback metody, její možnosti a návrh biofeedback her. Popis zpracování naměřených EEG signálů. EEG generation, brain rhythms, methods of recording EEG, EEG recorder. Description of biofeedback, potentialities of biofeedback, proposal of biofeedback games. Description of processing measured EEG signals. B

  7. A topological introduction to nonlinear analysis

    CERN Document Server

    Brown, Robert F

    2014-01-01

    This third edition of A Topological Introduction to Nonlinear Analysis is addressed to the mathematician or graduate student of mathematics - or even the well-prepared undergraduate - who would like, with a minimum of background and preparation, to understand some of the beautiful results at the heart of nonlinear analysis. Based on carefully-expounded ideas from several branches of topology, and illustrated by a wealth of figures that attest to the geometric nature of the exposition, the book will be of immense help in providing its readers with an understanding of the mathematics of the nonlinear phenomena that characterize our real world. For this third edition, several new chapters present the fixed point index and its applications. The exposition and mathematical content is improved throughout. This book is ideal for self-study for mathematicians and students interested in such areas of geometric and algebraic topology, functional analysis, differential equations, and applied mathematics. It is a sharply...

  8. Assessing severity of obstructive sleep apnea by fractal dimension sequence analysis of sleep EEG

    Science.gov (United States)

    Zhang, J.; Yang, X. C.; Luo, L.; Shao, J.; Zhang, C.; Ma, J.; Wang, G. F.; Liu, Y.; Peng, C.-K.; Fang, J.

    2009-10-01

    Different sleep stages are associated with distinct dynamical patterns in EEG signals. In this article, we explored the relationship between the sleep architecture and fractal dimension (FD) of sleep EEG. In particular, we applied the FD analysis to the sleep EEG of patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), which is characterized by recurrent oxyhemoglobin desaturation and arousals from sleep, a disease which received increasing public attention due to its significant potential impact on health. We showed that the variation of FD reflects the macrostructure of sleep. Furthermore, the fast fluctuation of FD, as measured by the zero-crossing rate of detrended FD (zDFD), is a useful indicator of sleep disturbance, and therefore, correlates with apnea-hypopnea index (AHI), and hourly number of blood oxygen saturation (SpO 2) decreases greater than 4%, as obstructive apnea/hypopnea disturbs sleep architecture. For practical purpose, a modified index combining zDFD of EEG and body mass index (BMI) may be useful for evaluating the severity of OSAHS symptoms.

  9. Quantitative analysis of the EEG posterior-dominant rhythm in healthy adolescents.

    Science.gov (United States)

    Marcuse, L V; Schneider, M; Mortati, K A; Donnelly, K M; Arnedo, V; Grant, A C

    2008-08-01

    Pivotal studies of the normal EEG posterior-dominant rhythm (PDR) typically relied on visual inspection of a few seconds of EEG data from a relatively small number of subjects in each age category. We sought to analyze and characterize the PDR in a large cohort of healthy 15-year-olds, and to determine if PDR characteristics mature over the following year. Seventy-nine healthy 15-year-olds free of neurologic and psychiatric disease underwent a resting-awake EEG, which was repeated 1 year later. In each study, PDR frequency was determined with fast Fourier transform analysis of a continuous 2-min EEG segment. t-Tests were used to compare relevant variables. From age 15 to 16 the mean PDR frequency increased from 9.9 to 10.0Hz, a small but statistically significant difference. The PDR frequency range at both ages was 8.9-11.0Hz, similar to values reported in prior studies on healthy young adults. There was no significant difference in PDR frequency between genders or hemispheres. Maturation of the PDR is nearly complete at age 16. The frequency range of the PDR in healthy adolescents and adults is substantially narrower than the alpha band. Based on this and prior studies, a PDR frequency of less than 8.5 or greater than 11.5Hz should be considered abnormal in adolescents and adults.

  10. A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

    Science.gov (United States)

    Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

    2016-08-01

    Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

  11. Nonlinear Fourier analysis with cnoidal waves

    Energy Technology Data Exchange (ETDEWEB)

    Osborne, A.R. [Dipt. di Fisica Generale dell`Universita, Torino (Italy)

    1996-12-31

    Fourier analysis is one of the most useful tools to the ocean engineer. The approach allows one to analyze wave data and thereby to describe a dynamical motion in terms of a linear superposition of ordinary sine waves. Furthermore, the Fourier technique allows one to compute the response function of a fixed or floating structure: each sine wave in the wave or force spectrum yields a sine wave in the response spectrum. The counting of fatigue cycles is another area where the predictable oscillations of sine waves yield procedures for the estimation of the fatigue life of structures. The ocean environment, however, is a source of a number of nonlinear effects which must also be included in structure design. Nonlinearities in ocean waves deform the sinusoidal shapes into other kinds of waves such as the Stokes wave, cnoidal wave or solitary wave. A key question is: Does there exist a generalization of linear Fourier analysis which uses nonlinear basis functions rather than the familiar sine waves? Herein addresses the dynamics of nonlinear wave motion in shallow water where the basis functions are cnoidal waves and discuss nonlinear Fourier analysis in terms of a linear superposition of cnoidal waves plus their mutual nonlinear interactions. He gives a number of simple examples of nonlinear Fourier wave motion and then analyzes an actual surface-wave time series obtained on an offshore platform in the Adriatic Sea. Finally, he briefly discusses application of the cnoidal wave spectral approach to the computation of the frequency response function of a floating vessel. The results given herein will prove useful in future engineering studies for the design of fixed, floating and complaint offshore structures.

  12. Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Joseph C. McBride

    2015-01-01

    Full Text Available Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI and early Alzheimer's disease (AD. The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years — 15 normal controls (NCs, 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the

  13. Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease.

    Science.gov (United States)

    McBride, Joseph C; Zhao, Xiaopeng; Munro, Nancy B; Jicha, Gregory A; Schmitt, Frederick A; Kryscio, Richard J; Smith, Charles D; Jiang, Yang

    2015-01-01

    Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of

  14. Analysis of EEG signal by Flicker Noise Spectroscopy: Identification of right/left hand movement imagination

    CERN Document Server

    Broniec, Anna

    2014-01-01

    Flicker Noise Spectroscopy (FNS) has been used for the analysis of electroencephalography (EEG) signal related to the movement imagination. The analysis of sensorimotor rhythms in time-frequency maps reveals the event-related desynchronization (ERD) and the post-movement event-related synchronization (ERS), observed mainly in the contralateral hemisphere to the hand moved for the motor imagery. The signal has been parameterized in accordance with FNS method. The significant changes of the FNS parameters, at the time when the subject imagines the movement, have been observed. The analysis of these parameters allows to distinguish between imagination of right and left hands movement. Our study shows that the flicker-noise spectroscopy can be an alternative method of analyzing EEG signal related to the imagination of movement in terms of a potential application in the brain-computer interface (BCI).

  15. Analysis of brain activity and response to colour stimuli during learning tasks: an EEG study

    Science.gov (United States)

    Folgieri, Raffaella; Lucchiari, Claudio; Marini, Daniele

    2013-02-01

    The research project intends to demonstrate how EEG detection through BCI device can improve the analysis and the interpretation of colours-driven cognitive processes through the combined approach of cognitive science and information technology methods. To this end, firstly it was decided to design an experiment based on comparing the results of the traditional (qualitative and quantitative) cognitive analysis approach with the EEG signal analysis of the evoked potentials. In our case, the sensorial stimulus is represented by the colours, while the cognitive task consists in remembering the words appearing on the screen, with different combination of foreground (words) and background colours. In this work we analysed data collected from a sample of students involved in a learning process during which they received visual stimuli based on colour variation. The stimuli concerned both the background of the text to learn and the colour of the characters. The experiment indicated some interesting results concerning the use of primary (RGB) and complementary (CMY) colours.

  16. Nonlinear principal component analysis and its applications

    CERN Document Server

    Mori, Yuichi; Makino, Naomichi

    2016-01-01

    This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...

  17. Nonlinear Dynamical Analysis of Fibrillation

    Science.gov (United States)

    Kerin, John A.; Sporrer, Justin M.; Egolf, David A.

    2013-03-01

    The development of spatiotemporal chaotic behavior in heart tissue, termed fibrillation, is a devastating, life-threatening condition. The chaotic behavior of electrochemical signals, in the form of spiral waves, causes the muscles of the heart to contract in an incoherent manner, hindering the heart's ability to pump blood. We have applied the mathematical tools of nonlinear dynamics to large-scale simulations of a model of fibrillating heart tissue to uncover the dynamical modes driving this chaos. By studying the evolution of Lyapunov vectors and exponents over short times, we have found that the fibrillating tissue is sensitive to electrical perturbations only in narrow regions immediately in front of the leading edges of spiral waves, especially when these waves collide, break apart, or hit the edges of the tissue sample. Using this knowledge, we have applied small stimuli to areas of varying sensitivity. By studying the evolution of the effects of these perturbations, we have made progress toward controlling the electrochemical patterns associated with heart fibrillation. This work was supported by the U.S. National Science Foundation (DMR-0094178) and Research Corporation.

  18. Nonlinear Time Series Analysis Since 1990:Some Personal Reflections

    Institute of Scientific and Technical Information of China (English)

    Howel Tong

    2002-01-01

    I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and chaos, the nonparametric/semiparametric approach, nonlinear state space modelling, financial time series and nonlinear modelling of panels of time series.

  19. Time-varying bispectral analysis of visually evoked multi-channel EEG

    Science.gov (United States)

    Chandran, Vinod

    2012-12-01

    Theoretical foundations of higher order spectral analysis are revisited to examine the use of time-varying bicoherence on non-stationary signals using a classical short-time Fourier approach. A methodology is developed to apply this to evoked EEG responses where a stimulus-locked time reference is available. Short-time windowed ensembles of the response at the same offset from the reference are considered as ergodic cyclostationary processes within a non-stationary random process. Bicoherence can be estimated reliably with known levels at which it is significantly different from zero and can be tracked as a function of offset from the stimulus. When this methodology is applied to multi-channel EEG, it is possible to obtain information about phase synchronization at different regions of the brain as the neural response develops. The methodology is applied to analyze evoked EEG response to flash visual stimulii to the left and right eye separately. The EEG electrode array is segmented based on bicoherence evolution with time using the mean absolute difference as a measure of dissimilarity. Segment maps confirm the importance of the occipital region in visual processing and demonstrate a link between the frontal and occipital regions during the response. Maps are constructed using bicoherence at bifrequencies that include the alpha band frequency of 8Hz as well as 4 and 20Hz. Differences are observed between responses from the left eye and the right eye, and also between subjects. The methodology shows potential as a neurological functional imaging technique that can be further developed for diagnosis and monitoring using scalp EEG which is less invasive and less expensive than magnetic resonance imaging.

  20. ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals

    Directory of Open Access Journals (Sweden)

    Pierre-Emmanuel Aguera

    2011-01-01

    Full Text Available The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG, magnetoencephalography (MEG, intracranial EEG, and local field potentials (LFPs. The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes.

  1. Robustness analysis for a class of nonlinear descriptor systems

    Institute of Scientific and Technical Information of China (English)

    吴敏; 张凌波; 何勇

    2004-01-01

    The robustness analysis problem of a class of nonlinear descriptor systems is studied. Nonlinear matrix inequality which has the good computation property of convex feasibility is employed to derive some sufficient conditions to guarantee that the nonlinear descriptor systems have robust disturbance attenuation performance, which avoids the computational difficulties in conversing nonlinear matrix and Hamilton-Jacobi inequality. The computation property of convex feasibility of nonlinear matrix inequality makes it possible to apply the results of nonlinear robust control to practice.

  2. Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data.

    Science.gov (United States)

    Kim, Hyun-Chul; Yoo, Seung-Schik; Lee, Jong-Hwan

    2015-01-01

    Electroencephalography (EEG) data simultaneously acquired with functional magnetic resonance imaging (fMRI) data are preprocessed to remove gradient artifacts (GAs) and ballistocardiographic artifacts (BCAs). Nonetheless, these data, especially in the gamma frequency range, can be contaminated by residual artifacts produced by mechanical vibrations in the MRI system, in particular the cryogenic pump that compresses and transports the helium that chills the magnet (the helium-pump). However, few options are available for the removal of helium-pump artifacts. In this study, we propose a recursive approach of EEG-segment-based principal component analysis (rsPCA) that enables the removal of these helium-pump artifacts. Using the rsPCA method, feature vectors representing helium-pump artifacts were successfully extracted as eigenvectors, and the reconstructed signals of the feature vectors were subsequently removed. A test using simultaneous EEG-fMRI data acquired from left-hand (LH) and right-hand (RH) clenching tasks performed by volunteers found that the proposed rsPCA method substantially reduced helium-pump artifacts in the EEG data and significantly enhanced task-related gamma band activity levels (p=0.0038 and 0.0363 for LH and RH tasks, respectively) in EEG data that have had GAs and BCAs removed. The spatial patterns of the fMRI data were estimated using a hemodynamic response function (HRF) modeled from the estimated gamma band activity in a general linear model (GLM) framework. Active voxel clusters were identified in the post-/pre-central gyri of motor area, only from the rsPCA method (uncorrected p<0.001 for both LH/RH tasks). In addition, the superior temporal pole areas were consistently observed (uncorrected p<0.001 for the LH task and uncorrected p<0.05 for the RH task) in the spatial patterns of the HRF model for gamma band activity when the task paradigm and movement were also included in the GLM.

  3. A distributed microcomputer-controlled system for data acquisition and power spectral analysis of EEG.

    Science.gov (United States)

    Vo, T D; Dwyer, G; Szeto, H H

    1986-04-01

    A relatively powerful and inexpensive microcomputer-based system for the spectral analysis of the EEG is presented. High resolution and speed is achieved with the use of recently available large-scale integrated circuit technology with enhanced functionality (INTEL Math co-processors 8087) which can perform transcendental functions rapidly. The versatility of the system is achieved with a hardware organization that has distributed data acquisition capability performed by the use of a microprocessor-based analog to digital converter with large resident memory (Cyborg ISAAC-2000). Compiled BASIC programs and assembly language subroutines perform on-line or off-line the fast Fourier transform and spectral analysis of the EEG which is stored as soft as well as hard copy. Some results obtained from test application of the entire system in animal studies are presented.

  4. Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal.

    Science.gov (United States)

    Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Kulish, Vladimir V

    2016-08-30

    One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.

  5. Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques

    OpenAIRE

    Jose Luis Rodríguez-Sotelo; Alejandro Osorio-Forero; Alejandro Jiménez-Rodríguez; David Cuesta-Frau; Eva Cirugeda-Roldán; Diego Peluffo

    2014-01-01

    Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms...

  6. Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis

    OpenAIRE

    Sidorov, Michael S.; Deck, Gina M.; Dolatshahi, Marjan; Thibert, Ronald L.; Bird, Lynne M.; Chu, Catherine J.; Philpot, Benjamin D.

    2017-01-01

    Background: Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker. Methods: We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state...

  7. Analysis of EEG activity during sleep - brain hemisphere symmetry of two classes of sleep spindles

    Science.gov (United States)

    Smolen, Magdalena M.

    2009-01-01

    This paper presents automatic analysis of some selected human electroencephalographic patterns during deep sleep using the Matching Pursuit (MP) algorithm. The periodicity of deep sleep EEG patterns was observed by calculating autocorrelation functions of their percentage contributions. The study confirmed the increasing trend of amplitude-weighted average frequency of sleep spindles from frontal to posterior derivations. The dominant frequencies from the left and the right brain hemisphere were strongly correlated.

  8. Stability analysis and non-linear behaviour of structural systems using the complex non-linear modal analysis (CNLMA)

    OpenAIRE

    Sinou, Jean-Jacques; Thouverez, Fabrice; Jezequel, Louis

    2006-01-01

    International audience; Herein, a novel non-linear procedure for producing non-linear behaviour and stable limit cycle amplitudes of non-linear systems subjected to super-critical Hopf bifurcation point is presented. This approach, called Complex Non-Linear Modal Analysis (CNLMA), makes use of the non-linear unstable mode which governs the non-linear dynamic of structural systems in unstable areas. In this study, the computational methodology of CNLMA is presented for the systematic estimatio...

  9. In-depth performance analysis of an EEG based neonatal seizure detection algorithm

    Science.gov (United States)

    Mathieson, S.; Rennie, J.; Livingstone, V.; Temko, A.; Low, E.; Pressler, R.M.; Boylan, G.B.

    2016-01-01

    Objective To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. Methods EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure). Seizures were annotated by an expert and characterized using a novel set of 10 criteria. ANSeR seizure detection algorithm (SDA) seizure annotations were compared to the expert to derive detected and non-detected seizures at three SDA sensitivity thresholds. Differences in seizure characteristics between groups were compared using univariate and multivariate analysis. False detections were characterized. Results The expert detected 421 seizures. The SDA at thresholds 0.4, 0.5, 0.6 detected 60%, 54% and 45% of seizures. At all thresholds, multivariate analyses demonstrated that the odds of detecting seizure increased with 4 criteria: seizure amplitude, duration, rhythmicity and number of EEG channels involved at seizure peak. Major causes of false detections included respiration and sweat artefacts or a highly rhythmic background, often during intermediate sleep. Conclusion This rigorous analysis allows estimation of how key seizure features are exploited by SDAs. Significance This study resulted in a beta version of ANSeR with significantly improved performance. PMID:27072097

  10. EEG spectral analysis of attention in ADHD: implications for neurofeedback training?

    Directory of Open Access Journals (Sweden)

    Hartmut eHeinrich

    2014-08-01

    Full Text Available Objective: In children with attention-deficit/hyperactivity disorder (ADHD, an increased theta/beta ratio in the resting EEG typically serves as a rationale to conduct theta/beta neurofeedback training. However, this finding is increasingly challenged. As neurofeedback may rather target an active than a passive state, we studied the EEG in a condition that requires attention.Methods: In children with ADHD of the DSM-IV combined type (ADHD-C; N=15 and of the predominantly inattentive type (ADHD-I; N=9 and in typically developing children (N=19, EEG spectral analysis was conducted for segments during the attention network test without processing of stimuli and overt behavior. Frontal (F3, Fz, F4, central (C3, Cz, C4 and parietal (P3, Pz, P4 electrodes were included in the statistical analysis. To investigate if EEG spectral parameters are related to performance measures, correlation coefficients were calculated.Results: Particularly in the ADHD-C group, higher theta and alpha activity was found with the most prominent effect in the upper-theta/lower-alpha (5.5-10.5 Hz range. In the ADHD-I group, a significantly higher theta/beta ratio was observed at single electrodes (F3, Fz and a tendency for a higher theta/beta ratio when considering all electrodes (large effect size. Higher 5.5-10.5 Hz activity was associated with higher reaction time variability with the effect most prominent in the ADHD-C group. A higher theta/beta ratio was associated with higher reaction times, particularly in the ADHD-I group.Conclusions: 1. In an attention demanding period, children with ADHD are characterized by an underactivated state in the EEG with subtype-specific differences. 2. The functional relevance of related EEG parameters is indicated by associations with performance (reaction time measures. 3. Findings provide a rationale for applying NF protocols targeting theta (and alpha activity and the theta/beta ratio in subgroups of children with ADHD.

  11. An EEMD-IVA framework for concurrent multidimensional EEG and unidimensional kinematic data analysis.

    Science.gov (United States)

    Chen, Xun; Liu, Aiping; McKeown, Martin J; Poizner, Howard; Wang, Z Jane

    2014-07-01

    Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG) and kinematic data jointly recorded during reaching movements. Existing JBSS approaches are designed to handle multidimensional datasets, yet to our knowledge, there is no existing means to examine common components that may be found across a unidimensional dataset and a multidimensional one. In this paper, we propose a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA). We demonstrate the performance of the proposed method through numerical simulations and application to data collected from reaching movements in Parkinson's disease. The proposed method is a promising JBSS tool for real-world biomedical signal processing applications.

  12. Approaches to verbal, visual and musical creativity by EEG coherence analysis.

    Science.gov (United States)

    Petsche, H

    1996-11-01

    Our approach to coherence analysis of the on-going EEG yields data on the cooperation between all possible electrode sites of the 10/20 system. This procedure compares epochs during mental activity with epochs of EEG at rest and takes into account only significant differences which are represented as lines between the respective electrodes on maps of the brain surface. The patterns thus obtained reflect the temporal average of the changes of the overall coherence pattern caused by any mental task. They are interpreted as reflecting differential attention required for the achievement of the mental task in question. Acts of creative thinking, be it verbally, visually or musically, are characterized by more coherence increases between occipital and frontopolar electrode sites than any other mental tasks. The results are interpreted by a stronger involvement of long cortico-cortical fibre systems in creative tasks.

  13. Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease.

    Science.gov (United States)

    Staudinger, Tyler; Polikar, Robi

    2011-01-01

    As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neurofibrillary plaques and tangles in the brain that leads to neuronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects--79 with AD, and 82 age-matched controls (CN)--are analyzed using several nonlinear signal complexity measures. These measures include: Higuchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60-66%, suggesting they contain some discriminatory information; however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.

  14. [Comparative analysis of spatial EEG organization on models of non-verbal divergent and convergent thinking].

    Science.gov (United States)

    Sviderskaia, N E; Antonov, A G; Butneva, L S

    2007-01-01

    Features of neurophysiological organization of two main thinking types playing different roles in creative processes, i.e., divergent and convergent were studied with participation of 30 right-handed male subjects at the age from 30 to 50 years. Two tests were presented: (1) creation of many visual images on the basis of two simple geometrical figures (the model of divergent thinking) and (2) classification of a figure element with one of the offered standard samples (convergent thinking). The number of created images or correctly classified elements for five minutes served a criterion of performance productivity. It was found that performance of the divergent test with high productivity (as compared to low productivity) was characterized by a greater increase in non-linear interactions between the cortical potentials, especially in the axis right frontal--left occipital areas. At the same time, under conditions of high productivity, the number of active narrow-frequency spectral-coherent EEG bands increased. The data confirm the notion of neurophysiological organization of creative processes, according to which creative processes require the intensification of retrieval operations (both conscious and unconscious), based on extensive interhemispheric interaction and involvement of a system of EEG coherent structures oscillating with different frequencies.

  15. EEG in the neonatal unit.

    Science.gov (United States)

    Lamblin, M D; de Villepin-Touzery, A

    2015-03-01

    The execution and interpretation of neonatal EEG adheres to strict and specific criteria related to this very early age. In preterm newborns, the dedicated healthcare staff needs to respect EEG indications and chronology of EEG recordings in order to diagnose and manage various pathologies, and use EEG in addition to cerebral imaging. EEG analysis focuses on a global vision of the recording according to the neonate's state of alertness and various age-related patterns. Monitoring of continuous conventional EEG and simplified EEG signal processing can help screen for seizures and monitor the effect of antiepileptic treatment, as well as appreciating changes in EEG background activity, for diagnostic and prognostic purposes. EEG reports should be highly explanatory to meet the expectations of the physician's clinical request.

  16. Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study.

    Science.gov (United States)

    Lima, Clodoaldo A M; Coelho, André L V; Eisencraft, Marcio

    2010-08-01

    The electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. The proper analysis of this biological signal plays an important role in the domain of brain-computer interface, which aims at the construction of communication channels between human brain and computers. In this paper, we investigate the application of least squares support vector machines (LS-SVM) to the task of epilepsy diagnosis through automatic EEG signal classification. More specifically, we present a sensitivity analysis study by means of which the performance levels exhibited by standard and least squares SVM classifiers are contrasted, taking into account the setting of the kernel function and of its parameter value. Results of experiments conducted over different types of features extracted from a benchmark EEG signal dataset evidence that the sensitivity profiles of the kernel machines are qualitatively similar, both showing notable performance in terms of accuracy and generalization. In addition, the performance accomplished by optimally configured LS-SVM models is also quantitatively contrasted with that obtained by related approaches for the same dataset. Copyright 2010 Elsevier Ltd. All rights reserved.

  17. Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

    Directory of Open Access Journals (Sweden)

    Vangelis Sakkalis

    2008-01-01

    Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.

  18. Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia

    Directory of Open Access Journals (Sweden)

    Mu-Tzu Shih

    2015-02-01

    Full Text Available Depth of anaesthesia (DoA is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD and the Hilbert–Huang transform (HHT. HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs. The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn are consistent with the expected differences between these stages based on the bispectral index (BIS, which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore

  19. [Discriminant analysis of graphic elements of the EEG. Application to the detection of episodes of diffuse spike-waves].

    Science.gov (United States)

    Pinon, J M; Rubel, P; Maraval, G; Mauguiere, F; Revol, M

    1982-05-01

    A database of EEG information was collected from EEG recordings performed in epileptic patients with diffuse spike-wave complex discharges. Normal activity, spike-waves, slow waves and artefacts were mixed up in these recordings. The analysis of EEGs stored in the database was performed, channel by channel, through a 2.56 s moving window. For each so defined EEG sequence, a set of 22 variables chosen for their discriminatory power was computed. A subset of 8 highly discriminating variables was selected by the means of a stepwise discriminant analysis. Each class of the learning set contained 40 up to 100 EEG sequences. A classifying algorithm that takes into account zones of uncertainty is proposed. It has been evaluated on a test set which was composed of 1981 EEG sequences issued from 15 different patients. The results have been checked by two neurologists. The agreement rate between each of them and the proposed algorithm was more than 92%; this result is comparable to the agreement rate between the two neurologists (94%). A contextual analysis algorithm, using bi-dimensional smoothing techniques, allowed to improve the agreement rates which exceeded 94%.

  20. Perturbation analysis of nonlinear matrix population models

    Directory of Open Access Journals (Sweden)

    Hal Caswell

    2008-03-01

    Full Text Available Perturbation analysis examines the response of a model to changes in its parameters. It is commonly applied to population growth rates calculated from linear models, but there has been no general approach to the analysis of nonlinear models. Nonlinearities in demographic models may arise due to density-dependence, frequency-dependence (in 2-sex models, feedback through the environment or the economy, and recruitment subsidy due to immigration, or from the scaling inherent in calculations of proportional population structure. This paper uses matrix calculus to derive the sensitivity and elasticity of equilibria, cycles, ratios (e.g. dependency ratios, age averages and variances, temporal averages and variances, life expectancies, and population growth rates, for both age-classified and stage-classified models. Examples are presented, applying the results to both human and non-human populations.

  1. Nonlinear analysis approximation theory, optimization and applications

    CERN Document Server

    2014-01-01

    Many of our daily-life problems can be written in the form of an optimization problem. Therefore, solution methods are needed to solve such problems. Due to the complexity of the problems, it is not always easy to find the exact solution. However, approximate solutions can be found. The theory of the best approximation is applicable in a variety of problems arising in nonlinear functional analysis and optimization. This book highlights interesting aspects of nonlinear analysis and optimization together with many applications in the areas of physical and social sciences including engineering. It is immensely helpful for young graduates and researchers who are pursuing research in this field, as it provides abundant research resources for researchers and post-doctoral fellows. This will be a valuable addition to the library of anyone who works in the field of applied mathematics, economics and engineering.

  2. Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG.

    Science.gov (United States)

    Zhou, Weidong; Liu, Yinxia; Yuan, Qi; Li, Xueli

    2013-12-01

    Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.

  3. Effect of mobile phone radiation on brain using EEG analysis by Higuichi's fractal dimension method

    Science.gov (United States)

    Smitha, C. K.; Narayanan, N. K.

    2013-01-01

    venient window on the mind, revealing synaptic action that is moderately to strongly co-relate with brain state. Fractal dimension, measure of signal complexity can be used to characterize the physiological conditions of the brain. As the EEG signal is non linear, non stationary and noisy, non linear methods will be suitable for the analysis. In this paper Higuichi's fractal method is applied to find the fractal dimension. EEGs of 5 volunteers were recorded at rest and on exposure to radiofrequency (RF) emissions from mobile phones having different SAR values. Mobiles were positioned near the ears and then near the cz position. Fractal dimensions for all conditions are calculated using Higuich's FD estimation algorithm. The result shows that there are some changes in the FD while using mobile phone. The change in FD of the signal varies from person to person. The changes in FD show the variations in EEG signal while using mobile phone, which demonstrate transformation in the activities of brain due to radiation.

  4. Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene

    Science.gov (United States)

    Rodriguez, Rene; Lopera, Francisco; Alvarez, Alfredo; Fernandez, Yuriem; Galan, Lidice; Quiroz, Yakeel; Bobes, Maria Antonieta

    2014-01-01

    To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D2) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D2, the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D2 using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function. PMID:24551475

  5. Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene.

    Science.gov (United States)

    Rodriguez, Rene; Lopera, Francisco; Alvarez, Alfredo; Fernandez, Yuriem; Galan, Lidice; Quiroz, Yakeel; Bobes, Maria Antonieta

    2014-01-01

    To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

  6. P2-15: EEG Analysis on Story Change in TV Drama

    Directory of Open Access Journals (Sweden)

    Chung-Yeon Lee

    2012-10-01

    Full Text Available The human brain naturally recognizes a change of environment or atmosphere without great effort, and this is essential for interactive communication in social life and a specific reaction in an emergency situation. Most studies have investigated change detection of the brain with conditional experimental paradigms rather than the performance of everyday tasks. However, naturally occurring sensory stimuli are multimodal and dynamic. In an effort to study the relationship between users' induced physiological responses and changes of environment and atmosphere under more naturalistic and ecological conditions, we performed a basic experiment using audio-visual movies and electroencephalogram (EEG measurement. 8 healthy subjects were asked to watch a television sitcom without any responses, and their EEG signals were recorded simultaneously with 126 electrodes mounted in an elastic electrode cap. Time-frequency analysis of EEG revealed distinctive neural oscillations at the point of story change in the movie. This result could be used for applications in brain-computer interfaces, and provides a reference to cognitive impairment studies such as Attention Deficit Disorder (ADD or Attention-Deficit/Hyperactivity Disorder (ADHD.

  7. The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

    Science.gov (United States)

    Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

    2015-01-01

    The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

  8. Disentangling Tinnitus Distress and Tinnitus Presence by Means of EEG Power Analysis

    Directory of Open Access Journals (Sweden)

    Martin Meyer

    2014-01-01

    Full Text Available The present study investigated 24 individuals suffering from chronic tinnitus (TI and 24 nonaffected controls (CO. We recorded resting-state EEG and collected psychometric data to obtain information about how chronic tinnitus experience affects the cognitive and emotional state of TI. The study was meant to disentangle TI with high distress from those who suffer less from persistent tinnitus based on both neurophysiological and behavioral data. A principal component analysis of psychometric data uncovers two distinct independent dimensions characterizing the individual tinnitus experience. These independent states are distress and presence, the latter is described as the perceived intensity of sound experience that increases with tinnitus duration devoid of any considerable emotional burden. Neuroplastic changes correlate with the two independent components. TI with high distress display increased EEG activity in the oscillatory range around 25 Hz (upper β-band that agglomerates over frontal recording sites. TI with high presence show enhanced EEG signal strength in the δ-, α-, and lower γ-bands (30–40 Hz over bilateral temporal and left perisylvian electrodes. Based on these differential patterns we suggest that the two dimensions, namely, distress and presence, should be considered as independent dimensions of chronic subjective tinnitus.

  9. A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification.

    Directory of Open Access Journals (Sweden)

    Blair Kaneshiro

    Full Text Available The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG study, we used single-trial classification to perform a Representational Similarity Analysis (RSA of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single

  10. Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.

    Directory of Open Access Journals (Sweden)

    Mahdi Jalili

    Full Text Available BACKGROUND: The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. METHODS/RESULTS: To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or

  11. Analysis of nonlinear damping properties of carbon

    Science.gov (United States)

    Kazakova, Olga I.; Smolin, Igor Yu.; Bezmozgiy, Iosif M.

    2016-11-01

    This paper describes research results of nonlinear damping properties of carbon fiber reinforced plastics. The experimental and computational research is performed on flat composite specimens with the gradual structure complication (from 1 to 12 layers). Specimens are subjected to three types of testing which are modal, harmonic and transient analyses. Relationships between the amplitude response and damping ratio are obtained by means of the analysis of variance as the result of this research.

  12. EEG spectral analysis and its clinical significance for patients with non-occupationalchronic mercury poisoning

    Directory of Open Access Journals (Sweden)

    Bin-bin SUN

    2015-03-01

    Full Text Available Objective To evaluate the features of EEG spectrum and its clinical significance for patients with non-occupational chronic mercury poisoning.  Methods Eighteen patients with chronic mercury poisoning were collected continuously as poisoning group at Affiliated Hospital of Academy of Military Medical Sciences from March 2012 to September 2013. At the same time, 12 age- and sex-matched healthy people were selected as control group. All patients underwent video EEG, and EEGLAB in Matlab 2013 software was used to analyze their EEG data. Relevant spectrum data of the 2 groups were compared and analyzed.  Results The frequency-energy curves of 12 normal subjects were similar to sine curve, with obvious energy peak at α band. The frequency-energy curves of 18 patients showed as follows: 5 cases had the peak at slow δ wave, and the energy curve decreased since δ band appeared, with α band peak disappearing. The curve of 10 cases had 2 peaks respectively at α and δ band, and δ peak was higher than α peak. The spectrum in other 3 cases was normal. The quantitative analysis of EEG revealed the proportion of δ band for the total energy. The proportion of δ band for total energy of the poisoning group in right middle temporal (P = 0.018 and left posterior temporal (P = 0.039 channel was significantly higher than that of the normal group, while the proportion of δ band in middle frontal (P = 0.003, right frontal (P = 0.016 and right anterior temporal (P = 0.024, left middle temporal (P = 0.036 and right posterior temporal (P = 0.031 was lower than that of the normal group. Conclusions EEG examination plays an important role in assessing the severity of brain injury for patients with non-occupational chronic mercury poisoning. Spectrum analysis is an intuitive and simple method, and can provide some help for clinical diagnosis and treatment. DOI: 10.3969/j.issn.1672-6731.2015.02.013

  13. The Analysis of the Strength, Distribution and Direction for the EEG Phase Synchronization by Musical Stimulus

    Science.gov (United States)

    Ogawa, Yutaro; Ikeda, Akira; Kotani, Kiyoshi; Jimbo, Yasuhiko

    In this study, we propose the EEG phase synchronization analysis including not only the average strength of the synchronization but also the distribution and directions under the conditions that evoked emotion by musical stimuli. The experiment is performed with the two different musical stimuli that evoke happiness or sadness for 150 seconds. It is found that the average strength of synchronization indicates no difference between the right side and the left side of the frontal lobe during the happy stimulus, the distribution and directions indicate significant differences. Therefore, proposed analysis is useful for detecting emotional condition because it provides information that cannot be obtained only by the average strength of synchronization.

  14. Denoising and robust nonlinear wavelet analysis

    Science.gov (United States)

    Bruce, Andrew G.; Donoho, David L.; Gao, Hong-Ye; Martin, R. D.

    1994-03-01

    In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistant wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transform, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the 'S+WAVELETS' object-oriented toolkit for wavelet analysis.

  15. NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation.However, PCA sometime is unfeasible to nonlinear feature analysis and limited in application to nonlinear industrial process.Kernel PCA (KPCA) is extension of PCA and can be used for nonlinear feature analysis.A nonlinear data reconciliation method based on KPCA is proposed.The basic idea of this method is that firstly original data are mapped to high dimensional feature space by nonlinear function, and PCA is implemented in the feature space.Then nonlinear feature analysis is implemented and data are reconstructed by using the kernel.The data reconciliation method based on KPCA is applied to ternary distillation column.Simulation results show that this method can filter the noise in measurements of nonlinear process and reconciliated data can represent the true information of nonlinear process.

  16. Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    C. R. Hema

    2008-01-01

    Full Text Available Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.

  17. Stability analysis of nonlinear systems with slope restricted nonlinearities.

    Science.gov (United States)

    Liu, Xian; Du, Jiajia; Gao, Qing

    2014-01-01

    The problem of absolute stability of Lur'e systems with sector and slope restricted nonlinearities is revisited. Novel time-domain and frequency-domain criteria are established by using the Lyapunov method and the well-known Kalman-Yakubovich-Popov (KYP) lemma. The criteria strengthen some existing results. Simulations are given to illustrate the efficiency of the results.

  18. Stability Analysis of Nonlinear Systems with Slope Restricted Nonlinearities

    Directory of Open Access Journals (Sweden)

    Xian Liu

    2014-01-01

    Full Text Available The problem of absolute stability of Lur’e systems with sector and slope restricted nonlinearities is revisited. Novel time-domain and frequency-domain criteria are established by using the Lyapunov method and the well-known Kalman-Yakubovich-Popov (KYP lemma. The criteria strengthen some existing results. Simulations are given to illustrate the efficiency of the results.

  19. [The mathematical rationale for the clinical EEG-frequency-bands. 1. Factor analysis with EEG-power estimations for determining frequency bands].

    Science.gov (United States)

    Herrmann, W M; Fichte, K; Kubicki, S

    1978-09-01

    In order to determine whether the clinically used frequency bands of the EEG can also be obtained by a mathematical system we did a factor analysis with 480 EEG recordings, 5 minutes each, in 60 healthy male volunteers. A power spectrum analysis was done and 57 frequency bands between 1.5 and 30.0 Hz in a half Hz steps were calculated. The factor structure obtained made the following frequency bands (Hz) reasonable: deltaF = 1.5 - 6.0, thetaF = 6.0 - 8.5, alpha1F = 8.5 - 10.5, alpha2F = 10.5 - 12.5, beta1F = 12.5 - 18.5, beta2F = 18.2 - 21.0, beta3F = 21.0 - 30.0. Except for alpha1F all other 6 frequency bands were represented by one general factor with the complexity 1. The variance of the alpha1F band is explained by several of the 6 factors. The clinically known and the by factor analysis obtained frequency bands in the beta-range are similar. The clinically alpha-band is subdivided into two frequency bands alpha1F and alpha2F by the factor analysis. The clinically known border line between delta- and theta-band of 3.5 Hz cannot be found by factor analysis.

  20. All Night Spectral Analysis of EEG Sleep in Young Adult and Middle-Aged Male Subjects

    OpenAIRE

    Dijk, Derk Jan; Beersma, Domien G. M.; Hoofdakker, Rutger H. van den

    1989-01-01

    The sleep EEGs of 9 young adult males (age 20-28 years) and 8 middle-aged males (42-56 years) were analyzed by visual scoring and spectral analysis. In the middle-aged subjects power density in the delta, theta and sigma frequencies were attenuated as compared to the young subjects. In both age groups power density in the delta and theta frequencies declined from NREM period 1 to 3. In the sigma frequencies, however, no systematic changes in power density were observed over the sleep episode....

  1. Spatiotemporal noise covariance model for MEG/EEG data source analysis

    CERN Document Server

    Plis, S M; Jun, S C; Pare-Blagoev, J; Ranken, D M; Schmidt, D M; Wood, C C

    2005-01-01

    A new method for approximating spatiotemporal noise covariance for use in MEG/EEG source analysis is proposed. Our proposed approach extends a parameterized one pair approximation consisting of a Kronecker product of a temporal covariance and a spatial covariance into 1) an unparameterized one pair approximation and then 2) into a multi-pair approximation. These models are motivated by the need to better describe correlated background and make estimation of these models more efficient. The effects of these different noise covariance models are compared using a multi-dipole inverse algorithm and simulated data consisting of empirical MEG background data as noise and simulated dipole sources.

  2. Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations

    Directory of Open Access Journals (Sweden)

    Adrian Rodriguez Aguinaga

    2015-06-01

    Full Text Available This paper proposes a methodology to perform emotional states classification by the analysis of EEG signals, wavelet decomposition and an electrode discrimination process, that associates electrodes of a 10/20 model to Brodmann regions and reduce computational burden. The classification process were performed by a Support Vector Machines Classification process, achieving a 81.46 percent of classification rate for a multi-class problem and the emotions modeling are based in an adjusted space from the Russell Arousal Valence Space and the Geneva model.

  3. EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

    Science.gov (United States)

    Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

    2017-08-01

    Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

  4. Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification.

    Science.gov (United States)

    Siuly, Siuly; Li, Yan

    2015-04-01

    The aim of this study is to design a robust feature extraction method for the classification of multiclass EEG signals to determine valuable features from original epileptic EEG data and to discover an efficient classifier for the features. An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data. As EEG data from different channels are correlated and huge in number, the optimum allocation (OA) scheme is used to discover the most favorable representatives with minimal variability from a large number of EEG data. The principal component analysis (PCA) is applied to construct uncorrelated components and also to reduce the dimensionality of the OA samples for an enhanced recognition. In order to choose a suitable classifier for the OA_PCA feature set, four popular classifiers: least square support vector machine (LS-SVM), naive bayes classifier (NB), k-nearest neighbor algorithm (KNN), and linear discriminant analysis (LDA) are applied and tested. Furthermore, our approaches are also compared with some recent research work. The experimental results show that the LS-SVM_1v1 approach yields 100% of the overall classification accuracy (OCA), improving up to 7.10% over the existing algorithms for the epileptic EEG data. The major finding of this research is that the LS-SVM with the 1v1 system is the best technique for the OA_PCA features in the epileptic EEG signal classification that outperforms all the recent reported existing methods in the literature. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. Market and system integration of renewable energy sources. A legal analysis of the regulations for direct marketing in the EEG 2012; Markt- und Systemintegration der Erneuerbaren-Energien. Eine rechtliche Analyse der Regeln zur Direktvermarktung im EEG 2012

    Energy Technology Data Exchange (ETDEWEB)

    Lehnert, Wieland [Kanzlei Becker Buettner Held, Berlin (Germany)

    2012-07-01

    The EEG 2012 presents optional funding instruments for direct marketing of EEG electricity, in particular a market bonus and a flexibility bonus. The author presents the new regulation for direct marketing in the EEG 2012, in consideration of the practice in energy law. Important aspects are a systematic integration of the new specifications in energy law and an analysis of their contents. Effects on the practice of energy management are investigated, as well as the chances and risks connected with implementation by the energy market partners.

  6. Correlation between videogame mechanics and executive functions through EEG analysis.

    Science.gov (United States)

    Mondéjar, Tania; Hervás, Ramón; Johnson, Esperanza; Gutierrez, Carlos; Latorre, José Miguel

    2016-10-01

    This paper addresses a different point of view of videogames, specifically serious games for health. This paper contributes to that area with a multidisciplinary perspective focus on neurosciences and computation. The experiment population has been pre-adolescents between the ages of 8 and 12 without any cognitive issues. The experiment consisted in users playing videogames as well as performing traditional psychological assessments; during these tasks the frontal brain activity was evaluated. The main goal was to analyse how the frontal lobe of the brain (executive function) works in terms of prominent cognitive skills during five types of game mechanics widely used in commercial videogames. The analysis was made by collecting brain signals during the two phases of the experiment, where the signals were analysed with an electroencephalogram neuroheadset. The validated hypotheses were whether videogames can develop executive functioning and if it was possible to identify which kind of cognitive skills are developed during each kind of typical videogame mechanic. The results contribute to the design of serious games for health purposes on a conceptual level, particularly in support of the diagnosis and treatment of cognitive-related pathologies.

  7. Mean-field modeling of thalamocortical dynamics and a model-driven approach to EEG analysis.

    Science.gov (United States)

    Victor, Jonathan D; Drover, Jonathan D; Conte, Mary M; Schiff, Nicholas D

    2011-09-13

    Higher brain function depends on task-dependent information flow between cortical regions. Converging lines of evidence suggest that interactions between cortical regions and the central thalamus play a key role in establishing the dynamic patterns of functional connectivity that normally support these processes. In patients with chronic disturbances of cognitive function due to severe brain injury, dysfunction of this circuitry likely plays a crucial role in pathogenesis. However, assaying thalamocortical interactions is challenging even in healthy subjects and more so in severely impaired patients. To approach this problem, we apply a dynamical-systems approach to motivate an analysis of the electroencephalogram (EEG). We begin with a model for a single thalamocortical module [Robinson PA, Rennie CJ, Rowe DL (2002) Phys Rev E Stat Nonlin Soft Matter Phys 65:041924; Robinson PA, Rennie CJ, Wright JJ, Bourke PD (1998) Phys Rev E Stat Nonlin Soft Matter Phys 58:3557-3571]. When two such modules interact via shared thalamic inhibition, multistable behavior emerges; each mode is characterized by a different pattern of coherence between cortical regions. This observation suggests that changing patterns of cortical coherence are a hallmark of normal thalamocortical dynamics. In a preliminary study, we test this idea by analyzing the EEG of a patient with chronic brain injury, who has a marked improvement in behavior and frontal brain metabolism in response to zolpidem. The analysis shows that following zolpidem administration, changing patterns of coherence are identified between the frontal lobes and between frontal and distant brain regions. These observations support the role of the central thalamus in the organization of patterns of cortical interactions and suggest how indexes of thalamocortical dynamics can be extracted from the EEG.

  8. Improving time-frequency domain sleep EEG classification via singular spectrum analysis.

    Science.gov (United States)

    Mahvash Mohammadi, Sara; Kouchaki, Samaneh; Ghavami, Mohammad; Sanei, Saeid

    2016-11-01

    Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as time-frequency (T-F) representations, there is still room for more improvement. To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVMs) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasise on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5±0.11%, 56.1±0.09% and 86.8±0.04% respectively. However, these values increase significantly to 83.6±0.07%, 70.6±0.14% and 90.8±0.03% after applying SSA. The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Comparative analysis of classifiers for developing an adaptive computer-assisted EEG analysis system for diagnosing epilepsy.

    Science.gov (United States)

    Ahmad, Malik Anas; Ayaz, Yasar; Jamil, Mohsin; Omer Gillani, Syed; Rasheed, Muhammad Babar; Imran, Muhammad; Khan, Nadeem Ahmed; Majeed, Waqas; Javaid, Nadeem

    2015-01-01

    Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system's incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.

  10. Nonlinear dynamic analysis of sandwich panels

    Science.gov (United States)

    Lush, A. M.

    1984-01-01

    Two analytical techniques applicable to large deflection dynamic response calculations for pressure loaded composite sandwich panels are demonstrated. One technique utilizes finite element modeling with a single equivalent layer representing the face sheets and core. The other technique utilizes the modal analysis computer code DEPROP which was recently modified to include transverse shear deformation in a core layer. The example problem consists of a simply supported rectangular sandwich panel. Included are comparisons of linear and nonlinear static response calculations, in addition to dynamic response calculations.

  11. Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals

    Science.gov (United States)

    Banerjee, Archi; Sanyal, Shankha; Patranabis, Anirban; Banerjee, Kaushik; Guhathakurta, Tarit; Sengupta, Ranjan; Ghosh, Dipak; Ghose, Partha

    2016-02-01

    Music has been proven to be a valuable tool for the understanding of human cognition, human emotion, and their underlying brain mechanisms. The objective of this study is to analyze the effect of Hindustani music on brain activity during normal relaxing conditions using electroencephalography (EEG). Ten male healthy subjects without special musical education participated in the study. EEG signals were acquired at the frontal (F3/F4) lobes of the brain while listening to music at three experimental conditions (rest, with music and without music). Frequency analysis was done for the alpha, theta and gamma brain rhythms. The finding shows that arousal based activities were enhanced while listening to Hindustani music of contrasting emotions (romantic/sorrow) for all the subjects in case of alpha frequency bands while no significant changes were observed in gamma and theta frequency ranges. It has been observed that when the music stimulus is removed, arousal activities as evident from alpha brain rhythms remain for some time, showing residual arousal. This is analogous to the conventional 'Hysteresis' loop where the system retains some 'memory' of the former state. This is corroborated in the non linear analysis (Detrended Fluctuation Analysis) of the alpha rhythms as manifested in values of fractal dimension. After an input of music conveying contrast emotions, withdrawal of music shows more retention as evidenced by the values of fractal dimension.

  12. Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing

    Directory of Open Access Journals (Sweden)

    Armando Freitas da Rocha

    2015-01-01

    Full Text Available Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (si of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(ei provided by each electrode of the 10/20 system about the identified si. H(ei Principal Component Analysis (PCA was used to study the temporal and spatial activation of these sources si. This analysis evidenced 4 different patterns of H(ei covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

  13. Nonlinear peculiar-velocity analysis and PCA

    Energy Technology Data Exchange (ETDEWEB)

    Dekel, A. [and others

    2001-02-20

    We allow for nonlinear effects in the likelihood analysis of peculiar velocities, and obtain {approximately}35%-lower values for the cosmological density parameter and for the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be of the flat {Lambda}CDM model (h = 0:65, n = 1) with only {Omega}{sub m} free. Since the likelihood is driven by the nonlinear regime, we break the power spectrum at k{sub b} {approximately} 0.2 (h{sup {minus}1} Mpc){sup {minus}1} and fit a two-parameter power-law at k > k{sub b} . This allows for an unbiased fit in the linear regime. Tests using improved mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark III and SFI data {Omega}{sub m} = 0.35 {+-} 0.09 with {sigma}{sub 8}{Omega}P{sub m}{sup 0.6} = 0.55 {+-} 0.10 (90% errors). When allowing deviations from {Lambda}CDM, we find an indication for a wiggle in the power spectrum in the form of an excess near k {approximately} 0.05 and a deficiency at k {approximately} 0.1 (h{sup {minus}1} Mpc){sup {minus}1}--a cold flow which may be related to a feature indicated from redshift surveys and the second peak in the CMB anisotropy. A {chi}{sup 2} test applied to principal modes demonstrates that the nonlinear procedure improves the goodness of fit. The Principal Component Analysis (PCA) helps identifying spatial features of the data and fine-tuning the theoretical and error models. We address the potential for optimal data compression using PCA.

  14. EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis

    Directory of Open Access Journals (Sweden)

    Ran eXiao

    2015-09-01

    Full Text Available Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g. hands and limbs, in electroencephalography (EEG signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91% can detect finger movements much better than classic mu/beta rhythms (75.6%. More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements, which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future from EEG will contribute significantly to the development of noninvasive brain computer interface (BCI and neuroprosthesis with intuitive and flexible controls.

  15. Analysis of Spectral Features of EEG during four different Cognitive Tasks

    Directory of Open Access Journals (Sweden)

    S.BAGYARAJ

    2014-05-01

    Full Text Available Cognition is a group of information processing activities that involves the visual attention, visual awareness, problem solving and decision making. Finding the cognitive task related regional cerebral activations are of great interest among researchers in cognitive neuroscience. In this study four different types of cognitive tasks, namely tracking pendulum movement and counting, red flash counting, sequential subtraction, spot the difference is performed by 32 subjects and the EEG signals are acquired by using 24 channels RMS EEG-32 Super Spec machine. The analyses of the EEG signal are done by using well known spectral methods. The band powers are calculated in the frequency domain by using the Welch method. The task- relaxes relative band power values and the ratios of theta band power/ beta band power are the two variables used to find the regional cerebral activations during the four different cognitive tasks. The statistical paired t test is used to evaluate the significant difference between the particular tasks related cerebral activations and relaxation. The statistical significance level is set at p< 0.05. During the tracking pendulum movement and counting task, the cerebral activations are found to be bilateral prefrontal, frontal, right central and temporal regions. Red flash counting task has activations in bilateral prefrontal, frontal, right central, right parietal and right occipital lobes. Bilateral prefrontal regions are activated during the sequence subtraction task. The spot the difference task has activations in the left and right prefrontal cortex. The unique and common activations regions for the selected four different cognitive tasks are found to be left and right prefrontal cortex. The pre frontal lobe electrodes namely Fp1 & Fp2 can be used as the recording electrodes for detailed cognitive task analysis were cerebral activations are observed when compared with the other cerebral regions.

  16. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    Science.gov (United States)

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2016-12-22

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  17. Analysis of Wave Nonlinear Dispersion Relation

    Institute of Scientific and Technical Information of China (English)

    LI Rui-jie; TAO Jian-fu

    2005-01-01

    The nonlinear dispersion relations and modified relations proposed by Kirby and Hedges have the limitation of intermediate minimum value. To overcome the shortcoming, a new nonlinear dispersion relation is proposed. Based on the summarization and comparison of existing nonlinear dispersion relations, it can be found that the new nonlinear dispersion relation not only keeps the advantages of other nonlinear dispersion relations, but also significantly reduces the relative errors of the nonlinear dispersion relations for a range of the relative water depth of 1<kh<1.5 and has sufficient accuracy for practical purposes.

  18. Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming.

    Science.gov (United States)

    Craddock, Matt; Martinovic, Jasna; Müller, Matthias M

    2016-04-01

    Neuronal activity in the gamma-band range was long considered a marker of object representation. However, scalp-recorded EEG activity in this range is contaminated by a miniature saccade-related muscle artifact. Independent component analysis (ICA) has been proposed as a method of removal of such artifacts. Alternatively, beamforming, a source analysis method in which potential sources of activity across the whole brain are scanned independently through the use of adaptive spatial filters, offers a promising method of accounting for the artifact without relying on its explicit removal. We present here the application of ICA-based correction to a previously published dataset. Then, using beamforming, we examine the effect of ICA correction on the scalp-recorded EEG signal and the extent to which genuine activity is recoverable before and after ICA correction. We find that beamforming attributes much of the scalp-recorded gamma-band signal before correction to deep frontal sources, likely the eye muscles, which generate the artifact related to each miniature saccade. Beamforming confirms that what is removed by ICA is predominantly this artifactual signal, and that what remains after correction plausibly originates in the visual cortex. Thus, beamforming allows researchers to confirm whether their removal procedures successfully removed the artifact. Our results demonstrate that ICA-based correction brings about general improvements in signal-to-noise ratio suggesting it should be used along with, rather than be replaced by, beamforming.

  19. Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications.

    Science.gov (United States)

    Wang, Yubo; Veluvolu, Kalyana C; Lee, Minho

    2013-11-25

    Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement.

  20. Comparison of Classifiers and Statistical Analysis for EEG Signals Used in Brain Computer Interface Motor Task Paradigm

    Directory of Open Access Journals (Sweden)

    Oana Diana Eva

    2015-01-01

    Full Text Available Using the EEG Motor Movement/Imagery database there is proposed an off-line analysis for a brain computer interface (BCI paradigm. The purpose of the quantitative research is to compare classifiers in order to determinate which of them has highest rates of classification. The power spectral density method is used to evaluated the (desynchronizations that appear on Mu rhythm. The features extracted from EEG signals are classified using linear discriminant classifier (LDA, quadratic classifier (QDA and classifier based on Mahalanobis distance (MD. The differences between LDA, QDA and MD are small, but the superiority of QDA was sustained by analysis of variance (ANOVA.

  1. Comparative Analysis of the Discriminative Capacity of EEG, Two ECG-Derived and Respiratory Signals in Automatic Sleep Staging

    Directory of Open Access Journals (Sweden)

    Farideh Ebrahimi

    2017-01-01

    Full Text Available Highly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG and Respiratory (Res signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excursion and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS polysomnography Database. Feature vectors were extracted from 0.5 min (standard epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine-Recursive Feature Elimination (SVM-RFE method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR=0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV, and ECG-Derived Respiration (EDR signals produced a CCR=0.54, while those features based on the combination of HRV and (thoracic Res signals resulted in a CCR=0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long epochs of cardiorespiratory signals, revealed that acceptable CCR=0.81 and discriminative capacity (Accuracy=89.32%, Specificity=92.88% and Sensitivity=78.64% were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals.

  2. Nonlinear pushover analysis of infilled concrete frames

    Institute of Scientific and Technical Information of China (English)

    Chao Hsun Huang; Yungting Alex Tuan; Ruo Yun Hsu

    2006-01-01

    Six reinforced concrete frames with or without masonry infills were constructed and tested under horizontal cyclic loads. All six frames had identical details in which the transverse reinforcement in columns was provided by rectangular hoops that did not meet current ACI specifications for ductile frames. For comparison purposes, the columns in three of these frames were jacketed by carbon-fiber-reinforced-polymer (CFRP) sheets to avoid possible shear failure. A nonlinear pushover analysis, in which the force-deformation relationships of individual elements were developed based on ACI 318, FEMA 356,and Chen's model, was carried out for these frames and compared to test results. Both the failure mechanisms and impact of infills on the behaviors of these frames were examined in the study. Conclusions from the present analysis provide structural engineers with valuable information for evaluation and design of infilled concrete frame building structures.

  3. Brain network analysis of EEG functional connectivity during imagery hand movements.

    Science.gov (United States)

    Demuru, Matteo; Fara, Francesca; Fraschini, Matteo

    2013-12-01

    The characterization of human neural activity during imaginary movement tasks represent an important challenge in order to develop effective applications that allow the control of a machine. Yet methods based on brain network analysis of functional connectivity have been scarcely investigated. As a result we use graph theoretic methods to investigate the functional connectivity and brain network measures in order to characterize imagery hand movements in a set of healthy subjects. The results of the present study show that functional connectivity analysis and minimum spanning tree (MST) parameters allow to successfully discriminate between imagery hand movements (both right and left) and resting state conditions. In conclusion, this paper shows that brain network analysis of EEG functional connectivity could represent an efficient alternative to more classical local activation based approaches. Furthermore, it also suggests the shift toward methods based on the characterization of a limited set of fundamental functional connections that disclose salient network topological features.

  4. Nonlinear Peculiar-Velocity Analysis and PCA

    CERN Document Server

    Dekel, A; Silberman, L; Zehavi, I

    2001-01-01

    We allow for nonlinear effects in the likelihood analysis of peculiar velocities, and obtain ~35%-lower values for the cosmological density parameter and for the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be of the flat LCDM model (h=0.65, n=1) with only Om_m free. Since the likelihood is driven by the nonlinear regime, we "break" the power spectrum at k_b=0.2 h/Mpc and fit a two-parameter power-law at k>k_b. This allows for an unbiased fit in the linear regime. Tests using improved mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark III and SFI data Om_m=0.35+-0.09$ with sigma_8*Om_m^0.6=0.55+-0.10 (90% errors). When allowing deviations from \\lcdm, we find an indication for a wiggle in the power spectrum in the form of an excess near k~0.05 and a deficiency at k~0.1 h/Mpc --- a "cold flow" which may be related to a feature indicated from redshift surveys and the second peak in the CMB anisotropy. A chi^2 test applied to principal ...

  5. Modeling analysis of the relationship between EEG rhythms and connectivity among different neural populations.

    Science.gov (United States)

    Ursino, Mauro; Zavaglia, Melissa

    2007-12-01

    In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses.

  6. Assessment of Nociceptive Responsiveness Levels during Sedation-Analgesia by Entropy Analysis of EEG

    Directory of Open Access Journals (Sweden)

    José F. Valencia

    2016-03-01

    Full Text Available The level of sedation in patients undergoing medical procedures is decided to assure unconsciousness and prevent pain. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG, have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work was to analyze the capability of prediction of nociceptive responses based on refined multiscale entropy (RMSE and auto mutual information function (AMIF applied to EEG signals recorded in 378 patients scheduled to undergo ultrasonographic endoscopy under sedation-analgesia. Two observed categorical responses after the application of painful stimulation were analyzed: the evaluation of the Ramsay Sedation Scale (RSS after nail bed compression and the presence of gag reflex (GAG during endoscopy tube insertion. In addition, bispectrum (BIS, heart rate (HR, predicted concentrations of propofol (CeProp and remifentanil (CeRemi were annotated with a resolution of 1 s. Results showed that functions based on RMSE, AMIF, HR and CeRemi permitted predicting different stimulation responses during sedation better than BIS.

  7. Comparative analysis of brain EEG signals generated from the right and left hand while writing

    Science.gov (United States)

    Sardesai, Neha; Jamali Mahabadi, S. E.; Meng, Qinglei; Choa, Fow-Sen

    2016-05-01

    This paper provides a comparative analysis of right handed people and left handed people when they write with both their hands. Two left handed and one right handed subject were asked to write their respective names on a paper using both, their left and right handed, and their brain signals were measured using EEG. Similarly, they were asked to perform simple mathematical calculations using both their hand. The data collected from the EEG from writing with both hands is compared. It is observed that though it is expected that the right brain only would contribute to left handed writing and vice versa, it is not so. When a right handed person writes with his/her left hand, the initial instinct is to go for writing with the right hand. Hence, both parts of the brain are active when a subject writes with the other hand. However, when the activity is repeated, the brain learns to expect to write with the other hand as the activity is repeated and then only the expected part of the brain is active.

  8. Toward an EEG-based recognition of music liking using time-frequency analysis.

    Science.gov (United States)

    Hadjidimitriou, Stelios K; Hadjileontiadis, Leontios J

    2012-12-01

    Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects' electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time-frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao-Atlas-Marks distribution and Hilbert-Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, k-nearest neighbors (k -NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76)%} was achieved using k-NN and HHS-based feature vectors ( FVs) representing a bilateral average activity, referred to a resting period, in β (13-30 Hz) and γ (30-49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this study provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.

  9. [How to evaluate recirculation effect on brain function after global ischemia--broad spectral EEG analysis by Fourier method].

    Science.gov (United States)

    Nakata, M

    1987-03-01

    EEG alterations after 5 or 10 minutes of global ischemia were investigated for 6 hours of postischemic period in 18 adult cats, together with biophysiological parameters such as cerebral blood flow, intracranial pressure, systemic blood pressure, heart rate, and blood gases. Our EEG analytical system is composed of high fidelity pre-amplifier, AA 6 MK II (Medelec Limited, England) and signal processor 7T 08 (NEC-SanEi, Japan). It is qualified to analyze frequencies up to 20 kHz within 3 dB cut-off. Particular features of our EEG analytical method are focused on Fourier analysis about broad frequency bands, frequency and amplitude spectra to be expressed on bi-logarithmic graph and direct EEG recordings from various structures of the brain. On the basis of fluctuation theory following 3 types were divided; Type f which corresponds to 1/f fluctuation, Type L which corresponds to Lorentzian fluctuation, Type f+L which is the sum of Type f and L. The distribution of these types in the central nervous system corresponds with cortical structures, spinal cord and brain stem respectively. In conclusion, there was a good correlation between EEG and blood flow in the motor cortex. The functional reversibility after ischemia was different according to the types. Type f structures, namely the motor cortex, hippocampus and amygdala were vulnerable and Type f+L structures namely ventrolateral nucleus of the thalamus and midbrain reticular formation tended to recover or stay in preservation.

  10. EEG非线性特征参数的研究%Study on Non-linear Characteristic Parameter of EEG

    Institute of Scientific and Technical Information of China (English)

    陈冬冰; 吴平东; 毕路拯; 韩巍; 王刚; 刘莹

    2006-01-01

    脑电图(EEG)记录了神经元群的电活动,为脑信息处理特征的研究提供重要的信息.基于相空间重构思想的时间序列分维算法(G-P算法)提取EEG信号的特征参数,讨论了G-P算法的三个重要参数,即无标度域、嵌入维数和延时的确定规则,记录大脑在不同状态下的EEG信号并计算其关联维数.实验结果表明,EEG关联维数可以有效地区分大脑不同状态的特征,关联维数可以作为脑信息处理的非线性特征参数.

  11. Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

    Science.gov (United States)

    Moosmann, Matthias; Eichele, Tom; Nordby, Helge; Hugdahl, Kenneth; Calhoun, Vince D

    2008-03-01

    An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

  12. Source Separation and Higher-Order Causal Analysis of MEG and EEG

    CERN Document Server

    Zhang, Kun

    2012-01-01

    Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.

  13. Quantitative Analysis of the Effects of Slow Wave Sleep Deprivation During the First 3 h of Sleep on Subsequent EEG Power Density

    NARCIS (Netherlands)

    Dijk, Derk Jan; Beersma, Domien G.M.; Daan, Serge; Bloem, Gerda M.; Hoofdakker, Rutger H. van den

    1987-01-01

    The relation between EEG power density during slow wave sleep (SWS) deprivation and power density during subsequent sleep was investigated. Nine young male adults slept in the laboratory for 3 consecutive nights. Spectral analysis of the EEG on the 2nd (baseline) night revealed an exponential

  14. Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Maharatna, Koushik; Pan, Indranil; Kuyucu, Doga

    2015-09-01

    Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3-5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating toward different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modeling the functional brain connectivity through the synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration

  15. Coherent source and connectivity analysis on simultaneously measured EEG and MEG data during isometric contraction.

    Science.gov (United States)

    Muthuraman, M; Hellriegel, H; Hoogenboom, N; Anwar, A R; Mideksa, K G; Krause, H; Schnitzler, A; Raethjen, J; Deuschl, G

    2014-01-01

    The most well-known non-invasive electric and magnetic field measurement modalities are the electroencephalography (EEG) and magnetoencephalography (MEG). The first aim of the study was to implement the recently developed realistic head model which uses an integrative approach for both the modalities. The second aim of this study was to find the network of coherent sources and the modes of interactions within this network during isometric contraction (ISC) at (15-30 Hz) in healthy subjects. The third aim was to test the effective connectivity revealed by both the modalities analyzing them separately and combined. The Welch periodogram method was used to estimate the coherence spectrum between the EEG and the electromyography (EMG) signals followed by the realistic head modelling and source analysis method dynamic imaging of coherent sources (DICS) to find the network of coherent sources at the individual peak frequency within the beta band in healthy subjects. The last step was to identify the effective connectivity between the identified sources using the renormalized partial directed coherence method. The cortical and sub-cortical network comprised of the primary sensory motor cortex (PSMC), secondary motor area (SMA), and the cerebellum (C). The cortical and sub-cortical network responsible for the isometric contraction was similar in both the modalities when analysing them separately and combined. The SNR was not significantly different between the two modalities separately and combined. However, the coherence values were significantly higher in the combined modality in comparison to each of the modality separately. The effective connectivity analysis revealed plausible additional connections in the combined modality analysis.

  16. Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis.

    Science.gov (United States)

    Sidorov, Michael S; Deck, Gina M; Dolatshahi, Marjan; Thibert, Ronald L; Bird, Lynne M; Chu, Catherine J; Philpot, Benjamin D

    2017-01-01

    Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker. We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state-specific, and developmental changes in delta rhythms in a patient population. Delta power was broadly increased and more dynamic in both the Angelman syndrome mouse model, relative to wild-type littermates, and in children with Angelman syndrome, relative to age-matched neurotypical controls. Enhanced delta oscillations in children with Angelman syndrome were present during wakefulness and sleep, were generalized across the neocortex, and were more pronounced at earlier ages. Delta rhythmicity phenotypes can serve as reliable biomarkers for Angelman syndrome in both preclinical and clinical settings.

  17. Extraversion and fronto-posterior EEG spectral power gradient: an independent component analysis.

    Science.gov (United States)

    Knyazev, Gennady G; Bocharov, Andrey V; Pylkova, Liudmila V

    2012-02-01

    Several studies show that the fronto-posterior EEG spectral power gradient is a stable individual characteristic related to personality. Whether this characteristic is specifically related to agentic extraversion and theta band of frequencies or is associated with a broader set of personality traits and frequency bands is a matter of debate, as well as the specific cortical regions contributing to this effect. To clarify these questions, we used group independent component analysis (ICA) and source localization techniques. Agentic extraversion was associated with higher theta activity in the default mode network's (DMN) posterior hub and lower theta activity in the orbitofrontal cortex (OFC). Regression analyses showed that theta activity predicted agentic extraversion better than other frequency bands and agentic extraversion predicted posterior versus frontal activity better than other personality dimensions. These results are taken to indicate higher tonic activity in OFC and lower activity in DMN in extraverts as compared to introverts.

  18. Spectral theory and nonlinear analysis with applications to spatial ecology

    CERN Document Server

    Cano-Casanova, S; Mora-Corral , C

    2005-01-01

    This volume details some of the latest advances in spectral theory and nonlinear analysis through various cutting-edge theories on algebraic multiplicities, global bifurcation theory, non-linear Schrödinger equations, non-linear boundary value problems, large solutions, metasolutions, dynamical systems, and applications to spatial ecology. The main scope of the book is bringing together a series of topics that have evolved separately during the last decades around the common denominator of spectral theory and nonlinear analysis - from the most abstract developments up to the most concrete applications to population dynamics and socio-biology - in an effort to fill the existing gaps between these fields.

  19. Nonlinear analysis and prediction of time series in multiphase reactors

    CERN Document Server

    Liu, Mingyan

    2014-01-01

    This book reports on important nonlinear aspects or deterministic chaos issues in the systems of multi-phase reactors. The reactors treated in the book include gas-liquid bubble columns, gas-liquid-solid fluidized beds and gas-liquid-solid magnetized fluidized beds. The authors take pressure fluctuations in the bubble columns  as time series for nonlinear analysis, modeling and forecasting. They present qualitative and quantitative non-linear analysis tools which include attractor phase plane plot, correlation dimension, Kolmogorov entropy and largest Lyapunov exponent calculations and local non-linear short-term prediction.

  20. Nonlinear Forced Vibration Analysis for Thin Rectangular Plate on Nonlinear Elastic Foundation

    Directory of Open Access Journals (Sweden)

    Zhong Zhengqiang

    2013-02-01

    Full Text Available Nonlinear forced vibration is analyzed for thin rectangular plate with four free edges on nonlinear elastic foundation. Based on Hamilton variation principle, equations of nonlinear vibration motion for thin rectangular plate under harmonic loads on nonlinear elastic foundation are established. In the case of four free edges, viable expressions of trial functions for this specification are proposed, satisfying all boundary conditions. Then, equations are transformed to a system of nonlinear algebraic equations by using Galerkin method and are solved by using harmonic balance method. In the analysis of numerical computations, the effect on the amplitude-frequency characteristic curve due to change of the structural parameters of plate, parameters of foundation and parameters of excitation force are discussed.

  1. Nonlinear filtering properties of detrended fluctuation analysis

    Science.gov (United States)

    Kiyono, Ken; Tsujimoto, Yutaka

    2016-11-01

    Detrended fluctuation analysis (DFA) has been widely used for quantifying long-range correlation and fractal scaling behavior. In DFA, to avoid spurious detection of scaling behavior caused by a nonstationary trend embedded in the analyzed time series, a detrending procedure using piecewise least-squares fitting has been applied. However, it has been pointed out that the nonlinear filtering properties involved with detrending may induce instabilities in the scaling exponent estimation. To understand this issue, we investigate the adverse effects of the DFA detrending procedure on the statistical estimation. We show that the detrending procedure using piecewise least-squares fitting results in the nonuniformly weighted estimation of the root-mean-square deviation and that this property could induce an increase in the estimation error. In addition, for comparison purposes, we investigate the performance of a centered detrending moving average analysis with a linear detrending filter and sliding window DFA and show that these methods have better performance than the standard DFA.

  2. Spectral analysis of the EEG during halothane anaesthesia: Input-output relations

    NARCIS (Netherlands)

    Silva, F.H. Lopes da; Smith, N. Ty; Zwart, Aart; Nichols, W.W.

    1. 1. The “Halothane-brain compartment” system was investigated in dogs. The input was the inspired concentration of Halothane. The output was the intensity of EEG spectral components. The EEG was analysed by a hybrid system (analogue filters and digital integration in a small computer). For the

  3. Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

    NARCIS (Netherlands)

    Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.

    2008-01-01

    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, w

  4. Reduction of TMS induced artefacts in EEG using principal component analysis

    NARCIS (Netherlands)

    Braack, ter Esther M.; Jonge, Benjamin; Putten, van Michel J.A.M.

    2013-01-01

    Co-registration of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a new, promising method for assessing cortical excitability and connectivity. Using this technique, a TMS evoked potential (TEP) can be induced and registered with the EEG. However, the TEP contains an ear

  5. Memory load effect in auditory-verbal short-term memory task: EEG fractal and spectral analysis.

    Science.gov (United States)

    Stokić, Miodrag; Milovanović, Dragan; Ljubisavljević, Miloš R; Nenadović, Vanja; Čukić, Milena

    2015-10-01

    The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

  6. The Correlation Dimension of Absence-like Phenomena in The EEGS of Rats

    NARCIS (Netherlands)

    Rijn, C.M. van; Broek, P.L.C. van den; Dirksen, R.; Egmond, J. van; Coenen, A.M.L.

    2000-01-01

    Spike-Wave Discharges (SWDs) occur spontaneously in the EEG of WAG/Rij rats. These SWDs are a model for human absence seizures. In order to examine whether non-linear analysis methods can contribute to the analysis of SWDs, we estimated the correlation dimension of the SWDs and compared the value to

  7. Solving the forward problem in EEG source analysis by spherical and fdm head modeling: a comparative analysis - biomed 2009

    NARCIS (Netherlands)

    Vatta, F.; Meneghini, F.; Esposito, F.; Mininel, S.; Di Salle, F.

    2009-01-01

    Neural source localization techniques based on electroencephalography (EEG) use scalp potential data to infer the location of underlying neural activity. This procedure entails modeling the sources of EEG activity and modeling the head volume conduction process to link the modeled sources to the EEG

  8. 脑电信号的分形截距特征分析及在癫痫检测中的应用%Fractal Intercept Analysis of EEG and its Application for Seizure Detection

    Institute of Scientific and Technical Information of China (English)

    王玉; 周卫东; 李淑芳; 袁琦; 耿淑娟

    2011-01-01

    脑电信号的非线性特征会随癫痫发作而改变,脑电信号的特征分析和检测对癫痫的诊断和治疗具有重要意义.提出对癫痫脑电信号进行毯子维和分形截距的特征分析,并将分形截距应用于癫痫脑电信号的检测.首先提取脑电信号的分形截距和毯子维特征,并对两种特征的均值和方差进行比较,最后使用支持向量机分类器,实现脑电信号的分类检测.发现癫痫发作时脑电信号的分形截距显著高于发作间期,而脑电信号的毯子维在发作前后变化规律则不明显.将分形截距作为分类特征,能有效地区分癫痫脑电与间歇期脑电,具有较强的癫痫脑电检测性能,分类检测的准确率达到96%以上.%Nonlinear features of electroencephalogram ( EEC) vary with epileptic seizure, and the feature analysis and detection of epileptic EEC are significant in diagnosis and therapy of epilepsy. This paper presents an epileptic EEC analysis approach based on blanket dimension and fractal intercept features, and applies fractal intercept to epileptic EEG detection. We extract fractal intercept and blanket dimension features of EEG, and compare the mean and variance of those two features. Then, a support vector machine is applied to classify epileptic EEC signals. It is found that the fractal intercept features of EEG during epileptic seizure are significantly higher than interictal EEG' s, while the blanket dimension features of EEG show no significant differences before and after seizures. The fractal intercept as a classification feature could be used to distinguish epileptic EEG from interical EEG with high performance for seizure detection, and the classification accuracy is up to 96%.

  9. Temporal dynamics of sensorimotor integration in speech perception and production: Independent component analysis of EEG data

    Directory of Open Access Journals (Sweden)

    David eJenson

    2014-07-01

    Full Text Available Activity in premotor and sensorimotor cortices is found in speech production and some perception tasks. Yet, how sensorimotor integration supports these functions is unclear due to a lack of data examining the timing of activity from these regions. Beta (~20Hz and alpha (~10Hz spectral power within the EEG µ rhythm are considered indices of motor and somatosensory activity, respectively. In the current study, perception conditions required discrimination (same/different of syllables pairs (/ba/ and /da/ in quiet and noisy conditions. Production conditions required covert and overt syllable productions and overt word production. Independent component analysis was performed on EEG data obtained during these conditions to 1 identify clusters of µ components common to all conditions and 2 examine real-time event-related spectral perturbations (ERSP within alpha and beta bands. 17 and 15 out of 20 participants produced left and right µ-components, respectively, localized to precentral gyri. Discrimination conditions were characterized by significant (pFDR<.05 early alpha event-related synchronization (ERS prior to and during stimulus presentation and later alpha event-related desynchronization (ERD following stimulus offset. Beta ERD began early and gained strength across time. Differences were found between quiet and noisy discrimination conditions. Both overt syllable and word productions yielded similar alpha/beta ERD that began prior to production and was strongest during muscle activity. Findings during covert production were weaker than during overt production. One explanation for these findings is that µ-beta ERD indexes early predictive coding (e.g., internal modeling and/or overt and covert attentional / motor processes. µ-alpha ERS may index inhibitory input to the premotor cortex from sensory regions prior to and during discrimination, while µ-alpha ERD may index re-afferent sensory feedback during speech rehearsal and production.

  10. Time-course of motor inhibition during hypnotic paralysis: EEG topographical and source analysis.

    Science.gov (United States)

    Cojan, Yann; Archimi, Aurélie; Cheseaux, Nicole; Waber, Lakshmi; Vuilleumier, Patrik

    2013-02-01

    Cognitive hypotheses of hypnotic phenomena have proposed that executive attentional systems may be either inhibited or overactivated to produce a selective alteration or disconnection of some mental operations. Recent brain imaging studies have reported changes in activity in both medial (anterior cingulate) and lateral (inferior) prefrontal areas during hypnotically induced paralysis, overlapping with areas associated with attentional control as well as inhibitory processes. To compare motor inhibition mechanisms responsible for paralysis during hypnosis and those recruited by voluntary inhibition, we used electroencephalography (EEG) to record brain activity during a modified bimanual Go-Nogo task, which was performed either in a normal baseline condition or during unilateral paralysis caused by hypnotic suggestion or by simulation (in two groups of participants, each tested once with both hands valid and once with unilateral paralysis). This paradigm allowed us to identify patterns of neural activity specifically associated with hypnotically induced paralysis, relative to voluntary inhibition during simulation or Nogo trials. We used a topographical EEG analysis technique to investigate both the spatial organization and the temporal sequence of neural processes activated in these different conditions, and to localize the underlying anatomical generators through minimum-norm methods. We found that preparatory activations were similar in all conditions, despite left hypnotic paralysis, indicating preserved motor intentions. A large P3-like activity was generated by voluntary inhibition during voluntary inhibition (Nogo), with neural sources in medial prefrontal areas, while hypnotic paralysis was associated with a distinctive topography activity during the same time-range and specific sources in right inferior frontal cortex. These results add support to the view that hypnosis might act by enhancing executive control systems mediated by right prefrontal areas, but

  11. Nonlinear damage detection in composite structures using bispectral analysis

    Science.gov (United States)

    Ciampa, Francesco; Pickering, Simon; Scarselli, Gennaro; Meo, Michele

    2014-03-01

    Literature offers a quantitative number of diagnostic methods that can continuously provide detailed information of the material defects and damages in aerospace and civil engineering applications. Indeed, low velocity impact damages can considerably degrade the integrity of structural components and, if not detected, they can result in catastrophic failure conditions. This paper presents a nonlinear Structural Health Monitoring (SHM) method, based on ultrasonic guided waves (GW), for the detection of the nonlinear signature in a damaged composite structure. The proposed technique, based on a bispectral analysis of ultrasonic input waveforms, allows for the evaluation of the nonlinear response due to the presence of cracks and delaminations. Indeed, such a methodology was used to characterize the nonlinear behaviour of the structure, by exploiting the frequency mixing of the original waveform acquired from a sparse array of sensors. The robustness of bispectral analysis was experimentally demonstrated on a damaged carbon fibre reinforce plastic (CFRP) composite panel, and the nonlinear source was retrieved with a high level of accuracy. Unlike other linear and nonlinear ultrasonic methods for damage detection, this methodology does not require any baseline with the undamaged structure for the evaluation of the nonlinear source, nor a priori knowledge of the mechanical properties of the specimen. Moreover, bispectral analysis can be considered as a nonlinear elastic wave spectroscopy (NEWS) technique for materials showing either classical or non-classical nonlinear behaviour.

  12. Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals.

    Science.gov (United States)

    Mideksa, K G; Hellriegel, H; Hoogenboom, N; Krause, H; Schnitzler, A; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

    2012-01-01

    The sources of somatosensory evoked potentials (SEPs) and fields (SEFs), which is a standard paradigm, is investigated using multichannel EEG and MEG simultaneous recordings. The hypothesis that SEP & SEF sources are generated in the posterior bank of the central sulcus is tested, and analyses are compared based on EEG only, MEG only, bandpass filtered MEG, and both combined. To locate the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for averaged head models consisting of a set of connected volumes, typically representing the skull, scalp, and brain. The location of each dipole is then estimated using fixed MUSIC and current-density-reconstruction (CDR) algorithms. For both analyses, the results demonstrate that the band-pass filtered MEG can localize the sources accurately at the desired region as compared to only EEG and unfiltered MEG. For CDR analysis, it looks like MEG affects EEG during the combined analyses. The MUSIC algorithm gives better results than CDR, and when comparing the two head models, the averaged and the realistic head models showed the same result.

  13. Space distribution of EEG responses to hanoi-moving visual and auditory stimulation with Fourier Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Shijun eLi

    2015-07-01

    Full Text Available Background and objective: The relationship between EEG source signals and action-related visual and auditory stimulation is still not well understood. The objective of this study was to identify EEG source signals and their associated action-related visual and auditory responses, especially independent components of EEG.Methods: A hand-moving-Hanoi video paradigm was used to study neural correlates of the action-related visual and auditory information processing determined by mu rhythm (8-12 Hz in 16 healthy young subjects. Independent component analysis (ICA was applied to identify separate EEG sources, and further computed in the frequency domain by applying-Fourier transform ICA (F-ICA.Results: F-ICA found more sensory stimuli-related independent components located within the sensorimotor region than ICA did. The total number of independent components of interest from F-ICA was 768, twice that of 384 from traditional time-domain ICA (p0.05.Conclusions: These results support the hypothesis that mu rhythm was sensitive to detection of the cognitive expression, which could be reflected by the function in the parietal lobe sensory-motor region. The results of this study could potentially be applied into early diagnosis for those with visual and hearing impairments in the future.

  14. Feature analysis for correlation studies of simultaneous EEG-fMRI data: A proof of concept for neurofeedback approaches.

    Science.gov (United States)

    Simoes, Simões; Lima, João; Direito, Bruno; Castelhano, João; Ferreira, Carlos; Carvalho, Paulo; Castelo-Branco, Miguel

    2015-01-01

    The identification and interpretation of facial expressions is an important feature of social cognition. This characteristic is often impaired in various neurodevelopmental disorders. Recent therapeutic approaches to intervene in social communication impairments include neurofeedback (NF). In this study, we present a NF real-time functional Magnetic Resonance Imaging (rt-fMRI), combined with electroencephalography (EEG) to train social communication skills. In this sense, we defined the right Superior Temporal Sulcus as our target region-of-interest. To analyze the correlation between the fMRI regions of interest and the EEG data, we transposed the sources located at the nearest cortical location to the target region. We extracted a set of 75 features from EEG segments and performed a correlation analysis with the brain activations extracted from rt-fMRI in the right pSTS region. The finding of significant correlations of simultaneously measured signals in distinct modalities (EEG and fMRI) is promising. Future studies should address whether the observed correlation levels between local brain activity and scalp measures are sufficient to implement NF approaches.

  15. Non-linear finite element analysis in structural mechanics

    CERN Document Server

    Rust, Wilhelm

    2015-01-01

    This monograph describes the numerical analysis of non-linearities in structural mechanics, i.e. large rotations, large strain (geometric non-linearities), non-linear material behaviour, in particular elasto-plasticity as well as time-dependent behaviour, and contact. Based on that, the book treats stability problems and limit-load analyses, as well as non-linear equations of a large number of variables. Moreover, the author presents a wide range of problem sets and their solutions. The target audience primarily comprises advanced undergraduate and graduate students of mechanical and civil engineering, but the book may also be beneficial for practising engineers in industry.

  16. Contribution to stability analysis of nonlinear control systems

    Directory of Open Access Journals (Sweden)

    Švarc Ivan

    2003-12-01

    Full Text Available The Popov criterion for the stability of nonlinear control systems is considered. The Popov criterion gives sufficient conditions for stability of nonlinear systems in the frequency domain. It has a direct graphical interpretation and is convenient for both design and analysis. In the article presented, a table of transfer functions of linear parts of nonlinear systems is constructed. The table includes frequency response functions and offers solutions to the stability of the given systems. The table makes a direct stability analysis of selected nonlinear systems possible. The stability analysis is solved analytically and graphically.Then it is easy to find out if the nonlinear system is or is not stable; the task that usually ranks among the difficult task in engineering practice.

  17. Stability analysis of embedded nonlinear predictor neural generalized predictive controller

    Directory of Open Access Journals (Sweden)

    Hesham F. Abdel Ghaffar

    2014-03-01

    Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

  18. Analysis of Nonlinear Fractional Nabla Difference Equations

    Directory of Open Access Journals (Sweden)

    Jagan Mohan Jonnalagadda

    2015-01-01

    Full Text Available In this paper, we establish sufficient conditions on global existence and uniqueness of solutions of nonlinear fractional nabla difference systems and investigate the dependence of solutions on initial conditions and parameters.

  19. Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses.

    Science.gov (United States)

    Esposito, Fabrizio; Singer, Neomi; Podlipsky, Ilana; Fried, Itzhak; Hendler, Talma; Goebel, Rainer

    2013-02-01

    Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. Similarity Analysis of EEG Data Based on Self Organizing Map Neural Network

    Directory of Open Access Journals (Sweden)

    Ibrahim Salem Jahan

    2014-01-01

    Full Text Available The Electroencephalography (EEG is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5~Hz to 60~Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly.

  1. Stability Analysis for Class of Switched Nonlinear Systems

    DEFF Research Database (Denmark)

    Shaker, Hamid Reza; How, Jonathan P.

    2010-01-01

    Stability analysis for a class of switched nonlinear systems is addressed in this paper. Two linear matrix inequality (LMI) based sufficient conditions for asymptotic stability are proposed for switched nonlinear systems. These conditions are analogous counterparts for switched linear systems which...

  2. Modal analysis of nonlinear mechanical systems

    CERN Document Server

    2014-01-01

    The book first introduces the concept of nonlinear normal modes (NNMs) and their two main definitions. The fundamental differences between classical linear normal modes (LNMs) and NNMs are explained and illustrated using simple examples. Different methods for computing NNMs from a mathematical model are presented. Both advanced analytical and numerical methods are described. Particular attention is devoted to the invariant manifold and normal form theories. The book also discusses nonlinear system identification.

  3. NONLINEAR DYNAMIC ANALYSIS OF FLEXIBLE MULTIBODY SYSTEM

    Institute of Scientific and Technical Information of China (English)

    A.Y.T.Leung; WuGuorong; ZhongWeifang

    2004-01-01

    The nonlinear dynamic equations of a multibody system composed of flexible beams are derived by using the Lagrange multiplier method. The nonlinear Euler beam theory with inclusion of axial deformation effect is employed and its deformation field is described by exact vibration modes. A numerical procedure for solving the dynamic equations is presented based on the Newmark direct integration method combined with Newton-Raphson iterative method. The results of numerical examples prove the correctness and efficiency of the method proposed.

  4. Integrity of central nervous function in diabetes mellitus assessed by resting state EEG frequency analysis and source localization

    DEFF Research Database (Denmark)

    Frøkjær, Jens B; Graversen, Carina; Brock, Christina;

    2016-01-01

    localization analysis identified sources with reduced activity in the left postcentral gyrus for the gamma band and in right superior parietal lobule for the alpha1 (8-10Hz) band. DM patients with clinical signs of autonomic dysfunction and gastrointestinal symptoms had evidence of altered resting state......Diabetes mellitus (DM) is associated with structural and functional changes of the central nervous system. We used electroencephalography (EEG) to assess resting state cortical activity and explored associations to relevant clinical features. Multichannel resting state EEG was recorded in 27...... healthy controls and 24 patients with longstanding DM and signs of autonomic dysfunction. The power distribution based on wavelet analysis was summarized into frequency bands with corresponding topographic mapping. Source localization analysis was applied to explore the electrical cortical sources...

  5. Nonlinear physical systems spectral analysis, stability and bifurcations

    CERN Document Server

    Kirillov, Oleg N

    2013-01-01

    Bringing together 18 chapters written by leading experts in dynamical systems, operator theory, partial differential equations, and solid and fluid mechanics, this book presents state-of-the-art approaches to a wide spectrum of new and challenging stability problems.Nonlinear Physical Systems: Spectral Analysis, Stability and Bifurcations focuses on problems of spectral analysis, stability and bifurcations arising in the nonlinear partial differential equations of modern physics. Bifurcations and stability of solitary waves, geometrical optics stability analysis in hydro- and magnetohydrodynam

  6. Identifying nonlinear biomechanical models by multicriteria analysis

    Science.gov (United States)

    Srdjevic, Zorica; Cveticanin, Livija

    2012-02-01

    In this study, the methodology developed by Srdjevic and Cveticanin (International Journal of Industrial Ergonomics 34 (2004) 307-318) for the nonbiased (objective) parameter identification of the linear biomechanical model exposed to vertical vibrations is extended to the identification of n-degree of freedom (DOF) nonlinear biomechanical models. The dynamic performance of the n-DOF nonlinear model is described in terms of response functions in the frequency domain, such as the driving-point mechanical impedance and seat-to-head transmissibility function. For randomly generated parameters of the model, nonlinear equations of motion are solved using the Runge-Kutta method. The appropriate data transformation from the time-to-frequency domain is performed by a discrete Fourier transformation. Squared deviations of the response functions from the target values are used as the model performance evaluation criteria, thus shifting the problem into the multicriteria framework. The objective weights of criteria are obtained by applying the Shannon entropy concept. The suggested methodology is programmed in Pascal and tested on a 4-DOF nonlinear lumped parameter biomechanical model. The identification process over the 2000 generated sets of parameters lasts less than 20 s. The model response obtained with the imbedded identified parameters correlates well with the target values, therefore, justifying the use of the underlying concept and the mathematical instruments and numerical tools applied. It should be noted that the identified nonlinear model has an improved accuracy of the biomechanical response compared to the accuracy of a linear model.

  7. Sort entropy-based for the analysis of EEG during anesthesia

    Science.gov (United States)

    Ma, Liang; Huang, Wei-Zhi

    2010-08-01

    The monitoring of anesthetic depth is an absolutely necessary procedure in the process of surgical operation. To judge and control the depth of anesthesia has become a clinical issue which should be resolved urgently. EEG collected wiil be processed by sort entrop in this paper. Signal response of the surface of the cerebral cortex is determined for different stages of patients in the course of anesthesia. EEG is simulated and analyzed through the fast algorithm of sort entropy. The results show that discipline of phasic changes for EEG is very detected accurately,and it has better noise immunity in detecting the EEG anaesthetized than approximate entropy. In conclusion,the computing of Sort entropy algorithm requires shorter time. It has high efficiency and strong anti-interference.

  8. Detecting epileptic seizure activity in the EEG by independent component analysis

    NARCIS (Netherlands)

    Hoeve, Maarten-Jan; van der Zwaag, B.J.; van Burik, M.J.; Slump, Cornelis H.; Jones, Richard

    Manually reviewing EEG (Electroencephalogram) recordings, for detection of electrographical patterns, is a time consuming business. Therefore, the ability to automate the classification of interesting electrographical patterns is a good supplement to the wide range of detection algorithms currently

  9. Kernel-Based Nonlinear Discriminant Analysis for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    LIU QingShan (刘青山); HUANG Rui (黄锐); LU HanQing (卢汉清); MA SongDe (马颂德)

    2003-01-01

    Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method - Fisher Linear Discriminant Analysis (FLDA).First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.

  10. Brainstorm: A User-Friendly Application for MEG/EEG Analysis

    Directory of Open Access Journals (Sweden)

    François Tadel

    2011-01-01

    Full Text Available Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG and electroencephalography (EEG data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI.

  11. On-line analysis of AEP and EEG for monitoring depth of anaesthesia

    DEFF Research Database (Denmark)

    Capitanio, L; Jensen, E W; Filligoi, G C

    1997-01-01

    ) their characteristics reflect the way in which the brain reacts to a stimulus. However, AEP is embedded in noise from the ongoing EEG background activity. Hence, processing is needed to improve the signal to noise ratio. The methods applied were moving time averaging (MTA) and ARX-modeling. The EEG was collected from.......e., cessation of eye-lash reflex). The MTA extracted AEP was significantly slower in tracing the transition from consciousness to unconsciousness....

  12. Ambulatory video-EEG-EMG monitoring and analysis during cataplexy in narcolepsy

    Directory of Open Access Journals (Sweden)

    Bei HUANG

    2017-09-01

    Full Text Available Objective To comprehensively analyze the clinical and electroneurophysiological characteristics during the process of cataplexy by dynamic video?EEG?EMG monitoring. Methods Six narcolepsy type 1 patients with typical cataplexy were enrolled and 2 of them were diagnosed as status cataplecticus. All patients underwent polysomnography (PSG and daytime Multiple Sleep Latency Test (MSLT to clarify the diagnosis. Cataplexy was triggered by emotional stimulus and recorded under dynamic video-EEG-EMG monitoring. EEG characteristics during cataplexy were further compared and analyzed. Objective To comprehensively analyze the clinical and electroneurophysiological characteristics during the process of cataplexy by dynamic video-EEG-EMG monitoring. Methods Six narcolepsy type 1 patients with typical cataplexy were enrolled and 2 of them were diagnosed as status cataplecticus. All patients underwent polysomnography (PSG and daytime Multiple Sleep Latency Test (MSLT to clarify the diagnosis. Cataplexy was triggered by emotional stimulus and recorded under dynamic video-EEG-EMG monitoring. EEG characteristics during cataplexy were further compared and analyzed. Results Fourteen cataplectic attacks in 6 patients were recorded. According to the clinical and video- EMG characteristics, cataplectic attack was divided into 4 stages, including triggering phase (CA1, resisting phase (CA2, atonic phase (CA3 and recovering phase (CA4. EEG frequency and amplitude varied from one stage to another and hypersynchronous paroxysmal theta (HSPT was observed in early resisting phase (CA2, which was supposed to be a distinctive EEG characteristic during the onset of cataplexy. Conclusions Generalized cataplectic ttack contain 4 stages, which indicate a complicated and dynamic process in clinical and electroneurophysiology. Moreover, it's highly possible that HSPT during resisting phase (CA2 is critical in the mechanism of cataplexy. DOI: 10.3969/j.issn.1672-6731.2017.09.006

  13. Brainstorm: a user-friendly application for MEG/EEG analysis.

    Science.gov (United States)

    Tadel, François; Baillet, Sylvain; Mosher, John C; Pantazis, Dimitrios; Leahy, Richard M

    2011-01-01

    Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).

  14. Dynamic Analysis of Vibrating Systems with Nonlinearities

    Science.gov (United States)

    M. Kalami, Yazdi; Ahmadian, H.; Mirzabeigy, A.; Yildirim, A.

    2012-02-01

    The max-min approach is applied to mathematical models of some nonlinear oscillations. The models are regarding to three different forms that are governed by nonlinear ordinary differential equations. In this context, the strongly nonlinear Duffing oscillator with third, fifth, and seventh powers of the amplitude, the pendulum attached to a rotating rigid frame and the cubic Duffing oscillator with discontinuity are taken into consideration. The obtained results via the approach are compared with ones achieved utilizing other techniques. The results indicate that the approach has a good agreement with other well-known methods. He's max-min approach is a promising technique and can be successfully exerted to a lot of practical engineering and physical problems.

  15. Nonlinear Analysis of Airship Envelop Aerolasticity

    Science.gov (United States)

    Liu, J. M.; Lu, C. J.; Xue, L. P.

    The large airship in flow field is a flexible body with low rigidity. The distribution of the peripheral flow field around the airship is closely related to its shape. It is essentially one of the Fluid-structure Interaction problems. Based on this, this paper aims at the numerical simulation of nonlinear airship envelop aeroelasticity by means of coupling aerodynamics and structure using an iteration method. The three-dimensional flow around the airship was studied numerically by means of SIMPLE method based on the Finite Volume Method. Two approaches, the linear method whose equilibrium equations are based on the membrance theory of thin shell and the nonlinear method which uses a nonlinear finite element method to account for the large deformation of the airship envelop, are introduced for geometrically deformation of the airship shape. A thin plate spline method is adopted as the interface of exchanging information between the fluid and structure models.

  16. Nonlinear Analysis of Physiological Time Series

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-fang; PENG Yu-hua; XUE Yu-li; HAN Min

    2007-01-01

    Abstract.The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In this paper we use the nonlinear detection method to detect the nonlinear deterministic component in the physiological time series by a single variable series and two variables series respectively, and use the conditional information entropy to analyze the correlation between the heart rate, the respiration and the blood oxygen concentration. The conclusions are that there is the nonlinear deterministic component in the heart rate data and respiration data, and the heart rate and the respiration are two variables originating from the same underlying dynamics.

  17. Revealing action representation processes in audio perception using fractal EEG analysis.

    Science.gov (United States)

    Hadjidimitriou, Stelios K; Zacharakis, Asteris I; Doulgeris, Panagiotis C; Panoulas, Konstantinos J; Hadjileontiadis, Leontios J; Panas, Stavros M

    2011-04-01

    Electroencephalogram (EEG) recordings, and especially the Mu-rhythm over the sensorimotor cortex that relates to the activation of the mirror neuron system (MNS), were acquired from two subject groups (orchestral musicians and nonmusicians), in order to explore action representation processes involved in the perception and performance of musical pieces. Two types of stimuli were used, i.e., an auditory one consisting of an excerpt of Beethoven's fifth symphony and a visual one presenting a conductor directing an orchestra performing the same excerpt of the piece. Three tasks were conducted including auditory stimulation, audiovisual stimulation, and visual stimulation only, and the acquired signals were processed using fractal [time-dependent fractal dimension (FD) estimation] and statistical analysis (analysis of variance, Mann-Whitney). Experimental results showed significant differences between the two groups while desychronization of the Mu-rhythm, which can be linked to MNS activation, was observed during all tasks for the musicians' group, as opposed to the nonmusicians' group who exhibited similar response only when the visual stimulus was present. The mobility of the conductor was also correlated to the estimated FD signals, showing significantly higher correlation for the case of musicians compared to nonmusicians' one. The present study sheds light upon the difference in action representation in auditory perception between musicians and nonmusicians and paves the way for better comprehension of the underlying mechanisms of the MNS.

  18. Weighted-Permutation Entropy Analysis of Resting State EEG from Diabetics with Amnestic Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Zhijie Bian

    2016-08-01

    Full Text Available Diabetes is a significant public health issue as it increases the risk for dementia and Alzheimer’s disease (AD. In this study, we aim to investigate whether weighted-permutation entropy (WPE and permutation entropy (PE of resting-state EEG (rsEEG could be applied as potential objective biomarkers to distinguish type 2 diabetes patients with amnestic mild cognitive impairment (aMCI from those with normal cognitive function. rsEEG series were acquired from 28 patients with type 2 diabetes (16 aMCI patients and 12 controls, and neuropsychological assessments were performed. The rsEEG signals were analysed using WPE and PE methods. The correlations between the PE or WPE of the rsEEG and the neuropsychological assessments were analysed as well. The WPE in the right temporal (RT region of the aMCI diabetics was lower than the controls, and the WPE was significantly positively correlated to the scores of the Auditory Verbal Learning Test (AVLT (AVLT-Immediate recall, AVLT-Delayed recall, AVLT-Delayed recognition and the Wechsler Adult Intelligence Scale Digit Span Test (WAIS-DST. These findings were not obtained with PE. We concluded that the WPE of rsEEG recordings could distinguish aMCI diabetics from normal cognitive function diabetic controls among the current sample of diabetic patients. Thus, the WPE could be a potential index for assisting diagnosis of aMCI in type 2 diabetes.

  19. BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis

    Science.gov (United States)

    Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E.; Robbins, Kay A.

    2017-01-01

    Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

  20. BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

    Science.gov (United States)

    Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E; Robbins, Kay A

    2017-01-01

    Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

  1. Nonlinear Dimensionality Reduction Methods in Climate Data Analysis

    CERN Document Server

    Ross, Ian

    2008-01-01

    Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Nino/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble. The three methods used here are a nonlinear principal component analysis (NLPCA) approach based on neural networks, the Isomap isometric mappin...

  2. SEISMIC RANDOM VIBRATION ANALYSIS OF LOCALLY NONLINEAR STRUCTURES

    Institute of Scientific and Technical Information of China (English)

    ZhaoYan; LinJiahao; ZhangYahui; AnWei

    2003-01-01

    A nonlinear seismic analysis method for complex frame structures subjected to stationary random ground excitations is proposed. The nonlinear elasto-plastic behaviors may take place only on a small part of the structure. The Bouc-Wen differential equation model is used to model the hysteretic characteristics of the nonlinear components. The Pseudo Excitation Method (PEM) is used in solving the linearized random differential equations to replace the solution of the less efficient Lyapunov equation. Numerical results of a real bridge show that .the method proposed is effective for practical engineering analysis.

  3. Online Fault Diagnosis Method Based on Nonlinear Spectral Analysis

    Institute of Scientific and Technical Information of China (English)

    WEI Rui-xuan; WU Li-xun; WANG Yong-chang; HAN Chong-zhao

    2005-01-01

    The fault diagnosis based on nonlinear spectral analysis is a new technique for the nonlinear fault diagnosis, but its online application could be limited because of the enormous compution requirements for the estimation of general frequency response functions. Based on the fully decoupled Volterra identification algorithm, a new online fault diagnosis method based on nonlinear spectral analysis is presented, which can availably reduce the online compution requirements of general frequency response functions. The composition and working principle of the method are described, the test experiments have been done for damping spring of a vehicle suspension system by utilizing the new method, and the results indicate that the method is efficient.

  4. Vibration Analysis of Timoshenko Beams on a Nonlinear Elastic Foundation

    Institute of Scientific and Technical Information of China (English)

    MO Yihua; OU Li; ZHONG Hongzhi

    2009-01-01

    The vibrations of beams on a nonlinear elastic foundation were analyzed considering the effects of transverse shear deformation and the rotational inertia of beams. A weak form quadrature element method (QEM) is used for the vibration analysis. The fundamental frequencies of beams are presented for various slenderness ratios and nonlinear foundation parameters for both slender and short beams. The results for slender beams compare well with finite element results. The analysis shows that the transverse shear de-formation and the nonlinear foundation parameter significantly affect the fundamental frequency of the beams.

  5. Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC

    Directory of Open Access Journals (Sweden)

    Mohd Maroof Siddiqui

    2016-07-01

    Full Text Available Insomnia is a sleep disorder in which the subject encounters problems in sleeping. The aim of this study is to identify insomnia events from normal or effected person using time frequency analysis of PSD approach applied on EEG signals using channel ROC-LOC. In this research article, attributes and waveform of EEG signals of Human being are examined. The aim of this study is to draw the result in the form of signal spectral analysis of the changes in the domain of different stages of sleep. The analysis and calculation is performed in all stages of sleep of PSD of each EEG segment. Results indicate the possibility of recognizing insomnia events based on delta, theta, alpha and beta segments of EEG signals.

  6. Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC.

    Science.gov (United States)

    Siddiqui, Mohd Maroof; Srivastava, Geetika; Saeed, Syed Hasan

    2016-01-01

    Insomnia is a sleep disorder in which the subject encounters problems in sleeping. The aim of this study is to identify insomnia events from normal or effected person using time frequency analysis of PSD approach applied on EEG signals using channel ROC-LOC. In this research article, attributes and waveform of EEG signals of Human being are examined. The aim of this study is to draw the result in the form of signal spectral analysis of the changes in the domain of different stages of sleep. The analysis and calculation is performed in all stages of sleep of PSD of each EEG segment. Results indicate the possibility of recognizing insomnia events based on delta, theta, alpha and beta segments of EEG signals.

  7. Modified Homotopy Analysis Method for Nonlinear Fractional Partial Differential Equations

    Directory of Open Access Journals (Sweden)

    D. Ziane

    2017-05-01

    Full Text Available In this paper, a combined form of natural transform with homotopy analysis method is proposed to solve nonlinear fractional partial differential equations. This method is called the fractional homotopy analysis natural transform method (FHANTM. The FHANTM can easily be applied to many problems and is capable of reducing the size of computational work. The fractional derivative is described in the Caputo sense. The results show that the FHANTM is an appropriate method for solving nonlinear fractional partial differentia equation.

  8. Nonlinear transient analysis of joint dominated structures

    Science.gov (United States)

    Chapman, J. M.; Shaw, F. H.; Russell, W. C.

    1987-01-01

    A residual force technique is presented that can perform the transient analyses of large, flexible, and joint dominated structures. The technique permits substantial size reduction in the number of degrees of freedom describing the nonlinear structural models and can account for such nonlinear joint phenomena as free-play and hysteresis. In general, joints can have arbitrary force-state map representations but these are used in the form of residual force maps. One essential feature of the technique is to replace the arbitrary force-state maps describing the nonlinear joints with residual force maps describing the truss links. The main advantage of this replacement is that the incrementally small relative displacements and velocities across a joint are not monitored directly thereby avoiding numerical difficulties. Instead, very small and 'soft' residual forces are defined giving a numerically attractive form for the equations of motion and thereby permitting numerically stable integration algorithms. The technique was successfully applied to the transient analyses of a large 58 bay, 60 meter truss having nonlinear joints. A method to perform link testing is also presented.

  9. A Second Generation Nonlinear Factor Analysis.

    Science.gov (United States)

    Etezadi-Amoli, Jamshid; McDonald, Roderick P.

    1983-01-01

    Nonlinear common factor models with polynomial regression functions, including interaction terms, are fitted by simultaneously estimating the factor loadings and common factor scores, using maximum likelihood and least squares methods. A Monte Carlo study gives support to a conjecture about the form of the distribution of the likelihood ratio…

  10. Analysis of nonlinear transient responses of piezoelectric resonators.

    Science.gov (United States)

    Hagiwara, Manabu; Takahashi, Seita; Hoshina, Takuya; Takeda, Hiroaki; Tsurumi, Takaaki

    2011-09-01

    The electric transient response method is an effective technique to evaluate material constants of piezoelectric ceramics under high-power driving. In this study, we tried to incorporate nonlinear piezoelectric behaviors in the analysis of transient responses. As a base for handling the nonlinear piezoelectric responses, we proposed an assumption that the electric displacement is proportional to the strain without phase lag, which could be described by a real and constant piezoelectric e-coefficient. Piezoelectric constitutive equations including nonlinear responses were proposed to calculate transient responses of a piezoelectric resonator. The envelopes and waveforms of current and vibration velocity in transient responses observed in some piezoelectric ceramics could be fitted with the calculation including nonlinear responses. The procedure for calculation of mechanical quality factor Q(m) for piezoelectric resonators with nonlinear behaviors was also proposed.

  11. Nonlinear Finite Strain Consolidation Analysis with Secondary Consolidation Behavior

    Directory of Open Access Journals (Sweden)

    Jieqing Huang

    2014-01-01

    Full Text Available This paper aims to analyze nonlinear finite strain consolidation with secondary consolidation behavior. On the basis of some assumptions about the secondary consolidation behavior, the continuity equation of pore water in Gibson’s consolidation theory is modified. Taking the nonlinear compressibility and nonlinear permeability of soils into consideration, the governing equation for finite strain consolidation analysis is derived. Based on the experimental data of Hangzhou soft clay samples, the new governing equation is solved with the finite element method. Afterwards, the calculation results of this new method and other two methods are compared. It can be found that Gibson’s method may underestimate the excess pore water pressure during primary consolidation. The new method which takes the secondary consolidation behavior, the nonlinear compressibility, and nonlinear permeability of soils into consideration can precisely estimate the settlement rate and the final settlement of Hangzhou soft clay sample.

  12. Nonlinear fault diagnosis method based on kernel principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu; Zhang Chunkai; Shao Huihe

    2005-01-01

    To ensure the system run under working order, detection and diagnosis of faults play an important role in industrial process. This paper proposed a nonlinear fault diagnosis method based on kernel principal component analysis (KPCA). In proposed method, using essential information of nonlinear system extracted by KPCA, we constructed KPCA model of nonlinear system under normal working condition. Then new data were projected onto the KPCA model. When new data are incompatible with the KPCA model, it can be concluded that the nonlinear system isout of normal working condition. Proposed method was applied to fault diagnosison rolling bearings. Simulation results show proposed method provides an effective method for fault detection and diagnosis of nonlinear system.

  13. Employment of CB models for non-linear dynamic analysis

    Science.gov (United States)

    Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.

    1990-01-01

    The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.

  14. Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

    Science.gov (United States)

    Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

    2006-07-01

    Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

  15. EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis.

    Science.gov (United States)

    Valdés-Sosa, Pedro A; Vega-Hernández, Mayrim; Sánchez-Bornot, José Miguel; Martínez-Montes, Eduardo; Bobes, María Antonieta

    2009-06-01

    This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.

  16. REVIEW: Previous Deception detection methods and New proposed method using independent component analysis of EEG signals.

    Directory of Open Access Journals (Sweden)

    Roshni D. Tale

    2014-04-01

    Full Text Available Deception detection has important legal and medical applications, but the reliability of methods for the differentiation between truthful and deceptive responses is still limited. Deception detection can be more accurately achieved by measuring the brain correlates of lying in an individual. For the evaluation of the method, several participants were gone through the designed concealed information test paradigm and their respective brain signals were recorded. The electroencephalogram (EEG signals were recorded and separated into many single trials. To enhance signal noise ratio (SNR of P3 components, the independent component analysis (ICA method was adopted to separate non-P3 (i.e. artifacts and P3 components from every single trial. Then the P3 waveforms with high SNR were reconstructed. And then group of features based on time, frequency, and amplitude were extracted from the reconstructed P3 waveforms. Finally, two different class of feature samples were used to train a support vector machine (SVM classifier because it has higher performance compared with several other classifiers. The method presented in this paper improves the efficiency of CIT and deception detection in comparison with previous reported methods.

  17. Extensions of nonlinear error propagation analysis for explicit pseudodynamic testing

    Institute of Scientific and Technical Information of China (English)

    Shuenn-Yih Chang

    2009-01-01

    Two important extensions of a technique to perform a nonlinear error propagation analysis for an explicit pseudodynamic algorithm (Chang, 2003) are presented. One extends the stability study from a given time step to a complete step-by-step integration procedure. It is analytically proven that ensuring stability conditions in each time step leads to a stable computation of the entire step-by-step integration procedure. The other extension shows that the nonlinear error propagation results, which are derived for a nonlinear single degree of freedom (SDOF) system, can be applied to a nonlinear multiple degree of freedom (MDOF) system. This application is dependent upon the determination of the natural frequencies of the system in each time step, since all the numerical properties and error propagation properties in the time step are closely related to these frequencies. The results are derived from the step degree of nonlinearity. An instantaneous degree of nonlinearity is introduced to replace the step degree of nonlinearity and is shown to be easier to use in practice. The extensions can be also applied to the results derived from a SDOF system based on the instantaneous degree of nonlinearity, and hence a time step might be appropriately chosen to perform a pseudodynamic test prior to testing.

  18. Abnormal cortical functional connections in Alzheimer's disease: analysis of inter- and intra-hemispheric EEG coherence

    Institute of Scientific and Technical Information of China (English)

    JIANG Zheng-yan

    2005-01-01

    To investigate inter- and intra-hemispheric electroencephalography (EEG) coherence at rest and during photic stimulation of patients with Alzheimer's disease (AD). Thirty-five patients (12 males, 23 females; 52~64 y) and 33 sex- and age-matched controls (12 males, 21 females; 56~65 y) were recruited in the present study. EEG signals from C3-C4, P3-P4, T5-T6and O1-O2 electrode pairs resulted from the inter-hemispheric action, and EEG signals from C3-P3, C4-P4, P3-O1, P4-O2, C3-O1,C4-O2, T5-O 1 and T6-O2 electrode pairs resulted from the intra-hemispheric action. The influence of inter- and intra-hemispheric coherence on EEG activity with eyes closed was examined, using fast Fourier transformation from the 16 sampled channels. The frequencies of photic stimulation were fixed at 5, 10 and 15 Hz, respectively. The general decrease of AD patients in inter- and intra-hemispheric EEG coherence was more significant than that of the normal controls at the resting EEG, with most striking decrease observed in the alpha-1 (8.0-9.0 Hz) and alpha-2 (9.5-12.5 Hz) bands. During photic stimulation, inter- and intra-hemispheric EEG coherences of the AD patients having lower values in the alpha (9.5-10.5 Hz) band than those of the control group. It suggests that under stimulated and non-stimulated conditions, AD patients had impaired inter- and intra-hemispheric functional connections, indicating failure of brain activation in alpha-related frequency.

  19. Application of empirical mode decomposition and Teager energy operator to EEG signals for mental task classification.

    Science.gov (United States)

    Kaleem, M F; Sugavaneswaran, L; Guergachi, A; Krishnan, S

    2010-01-01

    This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information.

  20. Theoretical and software considerations for nonlinear dynamic analysis

    Science.gov (United States)

    Schmidt, R. J.; Dodds, R. H., Jr.

    1983-01-01

    In the finite element method for structural analysis, it is generally necessary to discretize the structural model into a very large number of elements to accurately evaluate displacements, strains, and stresses. As the complexity of the model increases, the number of degrees of freedom can easily exceed the capacity of present-day software system. Improvements of structural analysis software including more efficient use of existing hardware and improved structural modeling techniques are discussed. One modeling technique that is used successfully in static linear and nonlinear analysis is multilevel substructuring. This research extends the use of multilevel substructure modeling to include dynamic analysis and defines the requirements for a general purpose software system capable of efficient nonlinear dynamic analysis. The multilevel substructuring technique is presented, the analytical formulations and computational procedures for dynamic analysis and nonlinear mechanics are reviewed, and an approach to the design and implementation of a general purpose structural software system is presented.

  1. Ground motion estimation and nonlinear seismic analysis

    Energy Technology Data Exchange (ETDEWEB)

    McCallen, D.B.; Hutchings, L.J.

    1995-08-14

    Site specific predictions of the dynamic response of structures to extreme earthquake ground motions are a critical component of seismic design for important structures. With the rapid development of computationally based methodologies and powerful computers over the past few years, engineers and scientists now have the capability to perform numerical simulations of many of the physical processes associated with the generation of earthquake ground motions and dynamic structural response. This paper describes application of a physics based, deterministic, computational approach for estimation of earthquake ground motions which relies on site measurements of frequently occurring small (i.e. M < 3 ) earthquakes. Case studies are presented which illustrate application of this methodology for two different sites, and nonlinear analyses of a typical six story steel frame office building are performed to illustrate the potential sensitivity of nonlinear response to site conditions and proximity to the causative fault.

  2. Nonlinear Finite Element Analysis of Sloshing

    Directory of Open Access Journals (Sweden)

    Siva Srinivas Kolukula

    2013-01-01

    Full Text Available The disturbance on the free surface of the liquid when the liquid-filled tanks are excited is called sloshing. This paper examines the nonlinear sloshing response of the liquid free surface in partially filled two-dimensional rectangular tanks using finite element method. The liquid is assumed to be inviscid, irrotational, and incompressible; fully nonlinear potential wave theory is considered and mixed Eulerian-Lagrangian scheme is adopted. The velocities are obtained from potential using least square method for accurate evaluation. The fourth-order Runge-Kutta method is employed to advance the solution in time. A regridding technique based on cubic spline is employed to avoid numerical instabilities. Regular harmonic excitations and random excitations are used as the external disturbance to the container. The results obtained are compared with published results to validate the numerical method developed.

  3. Nonlinear analysis of RED - a comparative study

    Energy Technology Data Exchange (ETDEWEB)

    Jiang Kai; Wang Xiaofan E-mail: xfwang@sjtu.edu.cn; Xi Yugeng

    2004-09-01

    Random Early Detection (RED) is an active queue management (AQM) mechanism for routers on the Internet. In this paper, performance of RED and Adaptive RED are compared from the viewpoint of nonlinear dynamics. In particular, we reveal the relationship between the performance of the network and its nonlinear dynamical behavior. We measure the maximal Lyapunov exponent and Hurst parameter of the average queue length of RED and Adaptive RED, as well as the throughput and packet loss rate of the aggregate traffic on the bottleneck link. Our simulation scenarios include FTP flows and Web flows, one-way and two-way traffic. In most situations, Adaptive RED has smaller maximal Lyapunov exponents, lower Hurst parameters, higher throughput and lower packet loss rate than that of RED. This confirms that Adaptive RED has better performance than RED.

  4. EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

    Science.gov (United States)

    Falk, Tiago H.; Fraga, Francisco J.; Trambaiolli, Lucas; Anghinah, Renato

    2012-12-01

    Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

  5. Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy

    Science.gov (United States)

    Rosso, O. A.; Hyslop, W.; Gerlach, R.; Smith, R. L. L.; Rostas, J. A. P.; Hunter, M.

    2005-10-01

    This study aimed to examine the background electroencephalography (EEG) in children with childhood absence epilepsy, a condition whose presentation has strong developmental links. EEG hallmarks of absence seizure activity are widely accepted and there is recognition that the bulk of inter-ictal EEG in this group is normal to the naked eye. This multidisciplinary study aimed to use the normalized total wavelet entropy (NTWS) (Signal Processing 83 (2003) 1275) to examine the background EEG of those patients demonstrating absence seizure activity, and compare it with children without absence epilepsy. This calculation can be used to define the degree of order in a system, with higher levels of entropy indicating a more disordered (chaotic) system. Results were subjected to further statistical analyses of significance. Entropy values were calculated for patients versus controls. For all channels combined, patients with absence epilepsy showed (statistically significant) lower entropy values than controls. The size of the difference in entropy values was not uniform, with certain EEG electrodes consistently showing greater differences than others.

  6. Nonlinear analysis of ring oscillator circuits

    KAUST Repository

    Ge, Xiaoqing

    2010-06-01

    Using nonlinear systems techniques, we analyze the stability properties and synchronization conditions for ring oscillator circuits, which are essential building blocks in digital systems. By making use of its cyclic structure, we investigate local and global stability properties of an n-stage ring oscillator. We present a sufficient condition for global asymptotic stability of the origin and obtain necessity if the ring oscillator consists of identical inverter elements. We then give a synchronization condition for identical interconnected ring oscillators.

  7. Nonsmooth analysis of doubly nonlinear evolution equations

    CERN Document Server

    Mielke, Alexander; Savare', Giuseppe

    2011-01-01

    In this paper we analyze a broad class of abstract doubly nonlinear evolution equations in Banach spaces, driven by nonsmooth and nonconvex energies. We provide some general sufficient conditions, on the dissipation potential and the energy functional,for existence of solutions to the related Cauchy problem. We prove our main existence result by passing to the limit in a time-discretization scheme with variational techniques. Finally, we discuss an application to a material model in finite-strain elasticity.

  8. Nonlinear Progressive Collapse Analysis Including Distributed Plasticity

    OpenAIRE

    Mohamed Osama Ahmed; Imam Zubair Syed; Khattab Rania

    2016-01-01

    This paper demonstrates the effect of incorporating distributed plasticity in nonlinear analytical models used to assess the potential for progressive collapse of steel framed regular building structures. Emphasis on this paper is on the deformation response under the notionally removed column, in a typical Alternate Path (AP) method. The AP method employed in this paper is based on the provisions of the Unified Facilities Criteria – Design of Buildings to Resist Progressive Collapse, develop...

  9. Analysis and design of robust decentralized controllers for nonlinear systems

    Energy Technology Data Exchange (ETDEWEB)

    Schoenwald, D.A.

    1993-07-01

    Decentralized control strategies for nonlinear systems are achieved via feedback linearization techniques. New results on optimization and parameter robustness of non-linear systems are also developed. In addition, parametric uncertainty in large-scale systems is handled by sensitivity analysis and optimal control methods in a completely decentralized framework. This idea is applied to alleviate uncertainty in friction parameters for the gimbal joints on Space Station Freedom. As an example of decentralized nonlinear control, singular perturbation methods and distributed vibration damping are merged into a control strategy for a two-link flexible manipulator.

  10. Reproducing Kernel Particle Method for Non-Linear Fracture Analysis

    Institute of Scientific and Technical Information of China (English)

    Cao Zhongqing; Zhou Benkuan; Chen Dapeng

    2006-01-01

    To study the non-linear fracture, a non-linear constitutive model for piezoelectric ceramics was proposed, in which the polarization switching and saturation were taken into account. Based on the model, the non-linear fracture analysis was implemented using reproducing kernel particle method (RKPM). Using local J-integral as a fracture criterion, a relation curve of fracture loads against electric fields was obtained. Qualitatively, the curve is in agreement with the experimental observations reported in literature. The reproducing equation, the shape function of RKPM, and the transformation method to impose essential boundary conditions for meshless methods were also introduced. The computation was implemented using object-oriented programming method.

  11. Comparative Convergence Analysis of Nonlinear AMLI-Cycle Multigrid

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Xiaozhe [Pennsylvania State Univ., University Park, PA (United States). Dept. of Mathematics; Vassilevski, Panayot S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Xu, Jinchao [Pennsylvania State Univ., University Park, PA (United States). Dept. of Mathematics

    2013-04-30

    The purpose of our paper is to provide a comprehensive convergence analysis of the nonlinear algebraic multilevel iteration (AMLI)-cycle multigrid (MG) method for symmetric positive definite problems. We show that the nonlinear AMLI-cycle MG method is uniformly convergent, based on classical assumptions for approximation and smoothing properties. Furthermore, under only the assumption that the smoother is convergent, we show that the nonlinear AMLI-cycle method is always better (or not worse) than the respective V-cycle MG method. Finally, numerical experiments are presented to illustrate the theoretical results.

  12. Looking Back at the Gifi System of Nonlinear Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Peter G. M. van der Heijden

    2016-09-01

    Full Text Available Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper discusses the so-called Gifi system of nonlinear multivariate analysis, that entails homogeneity analysis (which is closely related to multiple correspondence analysis and generalizations. The history is discussed, giving attention to the scientific philosophy of this group, and links to machine learning are indicated.

  13. A hybrid transfinite element approach for nonlinear transient thermal analysis

    Science.gov (United States)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1987-01-01

    A new computational approach for transient nonlinear thermal analysis of structures is proposed. It is a hybrid approach which combines the modeling versatility of contemporary finite elements in conjunction with transform methods and classical Bubnov-Galerkin schemes. The present study is limited to nonlinearities due to temperature-dependent thermophysical properties. Numerical test cases attest to the basic capabilities and therein validate the transfinite element approach by means of comparisons with conventional finite element schemes and/or available solutions.

  14. Center for Analysis of Heterogeneous and Nonlinear Media

    Science.gov (United States)

    1989-10-14

    computation of singular solutions of the nonlinear Schroedinger equation) Xue Xin (nonlinear homogenization) Jing-Yi Zhu (Ph.D. 1989, adaptive vortex method...numerical analysis of the vortex method for vortex sheets were carried out by Krasny and by Caflisch and Lowengrub. 2. Exact singular solutions of the...restriction to analytic functions. - 18- Singularities - Examples and the Generic Form of Singularities Singular solutions of the Birkhoff-Rott equation (1

  15. NOLB: Nonlinear Rigid Block Normal Mode Analysis Method

    OpenAIRE

    Hoffmann, Alexandre; Grudinin, Sergei

    2017-01-01

    International audience; We present a new conceptually simple and computationally efficient method for non-linear normal mode analysis called NOLB. It relies on the rotations-translations of blocks (RTB) theoretical basis developed by Y.-H. Sanejouand and colleagues. We demonstrate how to physically interpret the eigenvalues computed in the RTB basis in terms of angular and linear velocities applied to the rigid blocks and how to construct a non-linear extrapolation of motion out of these velo...

  16. Nonlinear Dynamic Analysis of the Whole Vehicle on Bumpy Road

    Institute of Scientific and Technical Information of China (English)

    王威; 李瑰贤; 宋玉玲

    2010-01-01

    Through the research into the characteristics of 7-DoF high dimensional nonlinear dynamics of a vehicle on bumpy road, the periodic movement and chaotic behavior of the vehicle were found.The methods of nonlinear frequency response analysis, global bifurcation, frequency chart and Poincaré maps were used simultaneously to derive strange super chaotic attractor.According to Lyapunov exponents calculated by Gram-Schmidt method, the unstable region was compartmentalized and the super chaotic characteristic of ...

  17. An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records.

    Science.gov (United States)

    Garces Correa, Agustina; Laciar Leber, Eric

    2010-01-01

    An algorithm to detect automatically drowsiness episodes has been developed. It uses only one EEG channel to differentiate the stages of alertness and drowsiness. In this work the vectors features are building combining Power Spectral Density (PDS) and Wavelet Transform (WT). The feature extracted from the PSD of EEG signal are: Central frequency, the First Quartile Frequency, the Maximum Frequency, the Total Energy of the Spectrum, the Power of Theta and Alpha bands. In the Wavelet Domain, it was computed the number of Zero Crossing and the integrated from the scale 3, 4 and 5 of Daubechies 2 order WT. The classifying of epochs is being done with neural networks. The detection results obtained with this technique are 86.5 % for drowsiness stages and 81.7% for alertness segment. Those results show that the features extracted and the classifier are able to identify drowsiness EEG segments.

  18. Double symbolic joint entropy in nonlinear dynamic complexity analysis

    Science.gov (United States)

    Yao, Wenpo; Wang, Jun

    2017-07-01

    Symbolizations, the base of symbolic dynamic analysis, are classified as global static and local dynamic approaches which are combined by joint entropy in our works for nonlinear dynamic complexity analysis. Two global static methods, symbolic transformations of Wessel N. symbolic entropy and base-scale entropy, and two local ones, namely symbolizations of permutation and differential entropy, constitute four double symbolic joint entropies that have accurate complexity detections in chaotic models, logistic and Henon map series. In nonlinear dynamical analysis of different kinds of heart rate variability, heartbeats of healthy young have higher complexity than those of the healthy elderly, and congestive heart failure (CHF) patients are lowest in heartbeats' joint entropy values. Each individual symbolic entropy is improved by double symbolic joint entropy among which the combination of base-scale and differential symbolizations have best complexity analysis. Test results prove that double symbolic joint entropy is feasible in nonlinear dynamic complexity analysis.

  19. Spectral Analysis of EEG in Familial Alzheimer’s Disease with E280A Presenilin-1 Mutation Gene

    Directory of Open Access Journals (Sweden)

    Rene Rodriguez

    2014-01-01

    Full Text Available To evaluate the hypothesis that quantitative EEG (qEEG analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects, an asymptomatic carrier (ACr group (21 subjects, with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D2 was calculated between groups. To evaluate the diagnostic efficiency of this statistic D2, the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D2 using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89 and between AD probable and ACr groups (area ROC = 0.91. This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

  20. Multi-modal causality analysis of eyes-open and eyes-closed data from simultaneously recorded EEG and MEG.

    Science.gov (United States)

    Anwar, Abdul Rauf; Mideska, Kidist Gebremariam; Hellriegel, Helge; Hoogenboom, Nienke; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan; Heute, Ulrich; Muthuraman, Muthuraman

    2014-01-01

    Owing to the recent advances in multi-modal data analysis, the aim of the present study was to analyze the functional network of the brain which remained the same during the eyes-open (EO) and eyes-closed (EC) resting task. The simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) were used for this study, recorded from five distinct cortical regions of the brain. We focused on the 'alpha' functional network, corresponding to the individual peak frequency in the alpha band. The total data set of 120 seconds was divided into three segments of 18 seconds each, taken from start, middle, and end of the recording. This segmentation allowed us to analyze the evolution of the underlying functional network. The method of time-resolved partial directed coherence (tPDC) was used to assess the causality. This method allowed us to focus on the individual peak frequency in the 'alpha' band (7-13 Hz). Because of the significantly higher power in the recorded EEG in comparison to MEG, at the individual peak frequency of the alpha band, results rely only on EEG. The MEG was used only for comparison. Our results show that different regions of the brain start to `disconnect' from one another over the course of time. The driving signals, along with the feedback signals between different cortical regions start to recede over time. This shows that, with the course of rest, brain regions reduce communication with each another.

  1. Review on Analysis of EEG Signals with the Effect of Meditation

    Directory of Open Access Journals (Sweden)

    Prajakta Fulpatil

    2014-06-01

    Full Text Available Meditation is proving out to be one of the most universally feasible solutions to the modern day stressful conditions. Varied positive physiological, psychological and spiritual benefits are known to be achieved through meditation. Many researchers previously investigated the effect of meditation on stress relief and disease improvement. The present study deals with the effect of meditation on human brain using electroencephalographic signals (EEG. To obtain new insights into the nature of EEG during meditation, the recorded signals are to be analyzed using wavelet transform.

  2. PyEEG: an open source Python module for EEG/MEG feature extraction.

    Science.gov (United States)

    Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

    2011-01-01

    Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

  3. Earthquake analysis of structures using nonlinear models

    OpenAIRE

    Cemalovic, Miran

    2015-01-01

    Throughout the governing design codes, several different methods are presented for the evaluation of seismic problems. This thesis assesses the non-linear static and dynamic procedures presented in EN 1998-1 through the structural response of a RC wall-frame building. The structure is designed in detail according to the guidelines for high ductility (DCH) in EN 1998-1. The applied procedures are meticulously evaluated and the requirements in EN 1998-1 are reviewed. In addition, the finite ele...

  4. Geometric and material nonlinear analysis of tensegrity structures

    Institute of Scientific and Technical Information of China (English)

    Hoang Chi Tran; Jaehong Lee

    2011-01-01

    A numerical method is presented for the large deflection in elastic analysis of tensegrity structures including both geometric and material nonlinearities.The geometric nonlinearity is considered based on both total Lagrangian and updated Lagrangian formulations,while the material nonlinearity is treated through elastoplastic stressstrain relationship.The nonlinear equilibrium equations are solved using an incremental-iterative scheme in conjunction with the modified Newton-Raphson method.A computer program is developed to predict the mechanical responses of tensegrity systems under tensile,compressive and flexural loadings.Numerical results obtained are compared with those reported in the literature to demonstrate the accuracy and efficiency of the proposed program.The flexural behavior of the double layer quadruplex tensegrity grid is sufficiently good for lightweight large-span structural applications.On the other hand,its bending strength capacity is not sensitive to the self-stress level.

  5. Brain wave correlates of attentional states: Event related potentials and quantitative EEG analysis during performance of cognitive and perceptual tasks

    Science.gov (United States)

    Freeman, Frederick G.

    1993-01-01

    presented target stimulus. In addition to the task requirements, irrelevant tones were presented in the background. Research has shown that even though these stimuli are not attended, ERP's to them can still be elicited. The amplitude of the ERP waves has been shown to change as a function of a person's level of alertness. ERP's were also collected and analyzed for the target stimuli for each task. Brain maps were produced based on the ERP voltages for the different stimuli. In addition to the ERP's, a quantitative EEG (QEEG) was performed on the data using a fast Fourier technique to produce a power spectral analysis of the EEG. This analysis was conducted on the continuous EEG while the subjects were performing the tasks. Finally, a QEEG was performed on periods during the task when subjects indicated that they were in an altered state of awareness. During the tasks, subjects were asked to indicate by pressing a button when they realized their level of task awareness had changed. EEG epochs were collected for times just before and just after subjects made this reponse. The purpose of this final analysis was to determine whether or not subjective indices of level of awareness could be correlated with different patterns of EEG.

  6. Comparison of eyeblink monitoring and EEG signal analysis for mental fatigue assessment

    Science.gov (United States)

    Królak, Aleksandra; Strumiłło, Paweł

    2008-01-01

    Mental fatigue in humans is a major cause of accidents in occupations requiring constant attention. The most promising indicators of fatigue are eyeblink dynamics and electroencephalography. This paper presents the results of a study aimed at establishing the dependence between eyeblink dynamics and EEG signal changes during transition to mental fatigue.

  7. A Multilevel Nonlinear Profile Analysis Model for Dichotomous Data

    Science.gov (United States)

    Culpepper, Steven Andrew

    2009-01-01

    This study linked nonlinear profile analysis (NPA) of dichotomous responses with an existing family of item response theory models and generalized latent variable models (GLVM). The NPA method offers several benefits over previous internal profile analysis methods: (a) NPA is estimated with maximum likelihood in a GLVM framework rather than…

  8. EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

    Science.gov (United States)

    Ashari, Rehab Bahaaddin

    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only

  9. An analysis of nonlinear behavior in delta-sigma modulators

    Science.gov (United States)

    Ardalan, Sasan H.; Paulos, John J.

    1987-06-01

    The paper introduces a new method of analysis for delta-sigma modulators based on modeling the nonlinear quantizer with a linearized gain, obtained by minimizing a mean-square-error criterion, followed by an additive noise source representing distortion components. In the paper, input signal amplitude dependencies of delta-sigma modulator stability and signal-to-noise ratio are analyzed. It is shown that due to the nonlinearity of the quantizer, the signal-to-noise ratio of the modulator may decrease as the input amplitude increases prior to saturation. Also, a stable third-order delta-sigma modulator may become unstable by increasing the input amplitude beyond a certain threshold. Both of these phenomena are explained by the nonlinear analysis of this paper. The analysis is carried out for both dc and sinusoidal excitations.

  10. Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

    Institute of Scientific and Technical Information of China (English)

    GE Fei; CAO Yang; WANG Yuan-ni

    2005-01-01

    The main research motive is to analysis and to verify the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/fβ and periodic characteristics. The principal components analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts. So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear behavior and can be characterized by its power spectral density, principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

  11. An analysis of electroencephalogram (EEG) On Children’s mental retardation QUAN Yan%精神发育迟滞患儿的脑电图分析

    Institute of Scientific and Technical Information of China (English)

    全琰

    2013-01-01

      目的:通过观察和分析精神发育迟滞患儿脑电图(EEG)的表现,探讨脑电图检查对精神发育迟滞的诊断意义。方法:回顾性分析55例临床确诊为精神发育迟滞患儿的 EEG 改变。结果:精神发育迟滞脑电图有背景脑波频率偏慢,弥漫性高幅δ波或θ波,睡眠纺锤波改变,痫样放电及持续性低电压,异常率73%。结论:EEG 客观反映了精神发育迟滞患儿的脑功能情况,对儿童精神发育迟滞具有重要的诊断价值。%Objective:To discuss the diagnostic value of electroencephalogram (EEG) on Children’s mental retardation with the analysis of its EEG presentations. Method:Retrospective analyzes the EEG of 55 cases of mental retardation child patients. Results: EEG findings of mental retardation were characterized by the slow background rhythm, the diffuse high delta wave or theta wave, the variation of sleep spindle, the epileptiform discharges and the sustained low voltage. The abnormal rate was 73%.Conclusion: EEG reflects the mental function of MR Child patients objectively, and it is valuable in the diagnosis of mental retardation.

  12. A pipeline VLSI design of fast singular value decomposition processor for real-time EEG system based on on-line recursive independent component analysis.

    Science.gov (United States)

    Huang, Kuan-Ju; Shih, Wei-Yeh; Chang, Jui Chung; Feng, Chih Wei; Fang, Wai-Chi

    2013-01-01

    This paper presents a pipeline VLSI design of fast singular value decomposition (SVD) processor for real-time electroencephalography (EEG) system based on on-line recursive independent component analysis (ORICA). Since SVD is used frequently in computations of the real-time EEG system, a low-latency and high-accuracy SVD processor is essential. During the EEG system process, the proposed SVD processor aims to solve the diagonal, inverse and inverse square root matrices of the target matrices in real time. Generally, SVD requires a huge amount of computation in hardware implementation. Therefore, this work proposes a novel design concept for data flow updating to assist the pipeline VLSI implementation. The SVD processor can greatly improve the feasibility of real-time EEG system applications such as brain computer interfaces (BCIs). The proposed architecture is implemented using TSMC 90 nm CMOS technology. The sample rate of EEG raw data adopts 128 Hz. The core size of the SVD processor is 580×580 um(2), and the speed of operation frequency is 20MHz. It consumes 0.774mW of power during the 8-channel EEG system per execution time.

  13. Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

    Science.gov (United States)

    Lie, Octavian V; van Mierlo, Pieter

    2017-01-01

    The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

  14. Analysis of linear and nonlinear genotype × environment interaction

    Directory of Open Access Journals (Sweden)

    Rong-Cai eYang

    2014-07-01

    Full Text Available The usual analysis of genotype × environment interaction (GxE is based on the linear regression of genotypic performance on environmental changes (e.g., classic stability analysis. This linear model may often lead to lumping together of the nonlinear responses to the whole range of environmental changes from suboptimal and superoptimal conditions, thereby lowering the power of detecting GxE variation. On the other hand, the GxE is present when the magnitude of the genetic effect differs across the range of environmental conditions regardless of whether the response to environmental changes is linear or nonlinear. The objectives of this study are: (i explore the use of four commonly used nonlinear functions (logistic, parabola, normal and Cauchy functions for modeling nonlinear genotypic responses to environmental changes and (ii to investigate the difference in the magnitude of estimated genetic effects under different environmental conditions. The use of nonlinear functions was illustrated through the analysis of one data set taken from barley cultivar trials in Alberta, Canada (Data A and the examination of change in effect sizes is through the analysis another data set taken from the North America Barley Genome Mapping Project (Data B. The analysis of Data A showed that the Cauchy function captured an average of >40% of total GxE variation whereas the logistic function captured less GxE variation than the linear function. The analysis of Data B showed that genotypic responses were largely linear and that strong QTL × environment interaction existed as the positions, sizes and directions of QTL detected differed in poor vs. good environments. We conclude that (i the nonlinear functions should be considered when analyzing multi-environmental trials with a wide range of environmental variation and (ii QTL × environment interaction can arise from the difference in effect sizes across environments.

  15. Nonlinear analysis of NPP safety against the aircraft attack

    Energy Technology Data Exchange (ETDEWEB)

    Králik, Juraj, E-mail: juraj.kralik@stuba.sk [Faculty of Civil Engineering, STU in Bratislava, Radlinského 11, 813 68 Bratislava (Slovakia); Králik, Juraj, E-mail: kralik@fa.stuba.sk [Faculty of Architecture, STU in Bratislava, Námestie Slobody 19, 812 45 Bratislava (Slovakia)

    2016-06-08

    The paper presents the nonlinear probabilistic analysis of the reinforced concrete buildings of nuclear power plant under the aircraft attack. The dynamic load is defined in time on base of the airplane impact simulations considering the real stiffness, masses, direction and velocity of the flight. The dynamic response is calculated in the system ANSYS using the transient nonlinear analysis solution method. The damage of the concrete wall is evaluated in accordance with the standard NDRC considering the spalling, scabbing and perforation effects. The simple and detailed calculations of the wall damage are compared.

  16. Nonlinear analysis of NPP safety against the aircraft attack

    Science.gov (United States)

    Králik, Juraj; Králik, Juraj

    2016-06-01

    The paper presents the nonlinear probabilistic analysis of the reinforced concrete buildings of nuclear power plant under the aircraft attack. The dynamic load is defined in time on base of the airplane impact simulations considering the real stiffness, masses, direction and velocity of the flight. The dynamic response is calculated in the system ANSYS using the transient nonlinear analysis solution method. The damage of the concrete wall is evaluated in accordance with the standard NDRC considering the spalling, scabbing and perforation effects. The simple and detailed calculations of the wall damage are compared.

  17. Asymptotic analysis of a coupled nonlinear parabolic system

    Institute of Scientific and Technical Information of China (English)

    Lan QIAO; Sining ZHENG

    2008-01-01

    This paper deals with asymptotic analysis of a parabolic system with inner absorptions and coupled nonlinear boundary fluxes. Three simultaneous blow-up rates are established under different dominations of nonlinearities, and simply represented in a characteristic algebraic system introduced for the problem. In particular, it is observed that two of the multiple blow-up rates are absorption-related. This is substantially different from those for nonlinear parabolic problems with absorptions in all the previous literature, where the blow-up rates were known as absorption-independent. The results of the paper rely on the scaling method with a complete classification for the nonlinear parameters of the model. The first example of absorption-related blow-up rates was recently proposed by the authors for a coupled parabolic system with mixed type nonlinearities. The present paper shows that the newly observed phenomena of absorption-related blow-up rates should be due to the coupling mechanism, rather than the mixed type nonlinearities.

  18. Linear and nonlinear subspace analysis of hand movements during grasping.

    Science.gov (United States)

    Cui, Phil Hengjun; Visell, Yon

    2014-01-01

    This study investigated nonlinear patterns of coordination, or synergies, underlying whole-hand grasping kinematics. Prior research has shed considerable light on roles played by such coordinated degrees-of-freedom (DOF), illuminating how motor control is facilitated by structural and functional specializations in the brain, peripheral nervous system, and musculoskeletal system. However, existing analyses suppose that the patterns of coordination can be captured by means of linear analyses, as linear combinations of nominally independent DOF. In contrast, hand kinematics is itself highly nonlinear in nature. To address this discrepancy, we sought to to determine whether nonlinear synergies might serve to more accurately and efficiently explain human grasping kinematics than is possible with linear analyses. We analyzed motion capture data acquired from the hands of individuals as they grasped an array of common objects, using four of the most widely used linear and nonlinear dimensionality reduction algorithms. We compared the results using a recently developed algorithm-agnostic quality measure, which enabled us to assess the quality of the dimensional reductions that resulted by assessing the extent to which local neighborhood information in the data was preserved. Although qualitative inspection of this data suggested that nonlinear correlations between kinematic variables were present, we found that linear modeling, in the form of Principle Components Analysis, could perform better than any of the nonlinear techniques we applied.

  19. Synthesized quantitative assessment of human mental fatigue with EEG and HRV

    Science.gov (United States)

    Han, Qingpeng; Wang, Li; Wang, Ping; Wen, Bangchun

    2005-12-01

    The electroencephalograph (EEG) signals and heart rate variable (HRV) signals, which are relative to human body mental stress, are analyzed with the nonlinear dynamics and chaos. Based on calculated three nonlinear parameters, a synthesized quantitative criterion is proposed to assess the body's mental fatigue states. Firstly, the HRV and α wave of EEG from original signals are extracted based on wavelet transform technique. Then, the Largest Lyapunov Exponents, Complexity and Approximate Entropy, are calculated for both HRV and α wave. The three nonlinear parameters reflect quantitatively human physiological activities and can be used to evaluate the mental workload degree. Based on the computation and statistical analysis of practical EEG and HRV data, a synthesized quantitative assessment criterion is induced for mental fatigues with three nonlinear parameters of the above two rhythms. For the known 10 measured data of EEG and HRV signals, the assessment results are obtained with the above laws for different metal fatigue states. To compare with the practical cases, the identification accuracy of mental fatigue or not is up to 100 percent. Furthermore, the accuracies of weak fatigue, middle fatigue and serious fatigue mental workload are all relatively higher; they are about 94.44, 88.89, and 83.33 percent, respectively.

  20. Special section on analysis, design and optimization of nonlinear circuits

    Science.gov (United States)

    Okumura, Kohshi

    Nonlinear theory plays an indispensable role in analysis, design and optimization of electric/electronic circuits because almost all circuits in the real world are modeled by nonlinear systems. Also, as the scale and complexity of circuits increase, more effective and systematic methods for the analysis, design and optimization are desired. The goal of this special section is to bring together research results from a variety of perspectives and academic disciplines related to nonlinear electric/electronic circuits.This special section includes three invited papers and six regular papers. The first invited paper by Kennedy entitled “Recent advances in the analysis, design and optimization of digital delta-sigma modulators” gives an overview of digital delta-sigma modulators and some techniques for improving their efficiency. The second invited paper by Trajkovic entitled “DC operating points of transistor circuits” surveys main theoretical results on the analysis of DC operating points of transistor circuits and discusses numerical methods for calculating them. The third invited paper by Nishi et al. entitled “Some properties of solution curves of a class of nonlinear equations and the number of solutions” gives several new theorems concerning solution curves of a class of nonlinear equations which is closely related to DC operating point analysis of nonlinear circuits. The six regular papers cover a wide range of areas such as memristors, chaos circuits, filters, sigma-delta modulators, energy harvesting systems and analog circuits for solving optimization problems.The guest editor would like to express his sincere thanks to the authors who submitted their papers to this special section. He also thanks the reviewers and the editorial committee members of this special section for their support during the review process. Last, but not least, he would also like to acknowledge the editorial staff of the NOLTA journal for their continuous support of this

  1. Real-time Detection of Precursors to Epileptic Seizures: Non-Linear Analysis of System Dynamics.

    Science.gov (United States)

    Nesaei, Sahar; Sharafat, Ahmad R

    2014-04-01

    We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (iEEG), which is based on the analysis of system dynamics. In the proposed scheme, the largest Lyapunov exponent of the discrete wavelet packet transform (DWPT) of the segmented EEG signals is considered as the discriminating features. Such features are processed by a support vector machine (SVM) classifier to identify whether the corresponding segment of the EEG signal contains a precursor to an epileptic seizure. When consecutive EEG segments contain such precursors, a decision is made that a precursor is in fact detected. The proposed scheme is applied to the Freiburg dataset, and the results show that seizure precursors are detected in a time frame that unlike other existing schemes is very much convenient to patients, with sensitivity of 100% and negligible false positive detection rates.

  2. EEGLAB Analysis of EEG Signals%用 EEGLAB 分析脑电信号磁

    Institute of Scientific and Technical Information of China (English)

    程学梅; 崔园

    2014-01-01

    EEGLAB 是一种基于 Matlab 的工具箱。它主要用于处理连续记录的脑电信号(EEG)脑磁信号(MEG)和其它电生理数据。它运用的方法主要有独立分量分析(ICA )、时间-频率分析[1]、绘制 ERP 图、排除伪迹和几种有用的可视化模式(对于求平均和单次提取数据)等。 EEGLAB 还为从事神经信号处理方法研究的开发人员提供了一个可扩展的开源的平台,他们利用邮件列表和世界各地的研究人员一起讨论新方法,研究出更多的 EEGLAB 的新插件。 EEGLAB 的插件可以通过下载设置后,直接融入并出现在用户菜单。 EEGLAB 可用于研究各种脑电信号,这些研究有助于对人类情绪探知和生理病理情况下的脑机制做研究,有助于了解人脑的工作原理,找到更有效的治疗精神疾病的方法。%EEGLAB is a toolbox based on Matlab .It is mainly used for processing continuous related brain electric sig -nal ,brain magnetic signal and other electrophysiological data .Its main methods include independent component analysis (ICA) ,time-frequency analysis(IAF)[1] ,the map of ERP ,artifact rejection and several useful visual models (for the average and single-trial data) ,etc .For creative research programmers and methods developers ,EEGLAB offers an extensible ,open-source platform through which they can share new methods with the world research community by publishing EEGLAB 'plug-in'functions that appear automatically in the EEGLAB menu .EEGLAB can be used to study a variety of EEG signals .These studies based on EEGLAB will help the detection of human emotions and the study to the pathogenesis of mental illness , which will find more effective methods for the treatment of mental illness .

  3. Independent EEG sources are dipolar

    National Research Council Canada - National Science Library

    Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott

    2012-01-01

    Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG...

  4. Discretization analysis of bifurcation based nonlinear amplifiers

    Science.gov (United States)

    Feldkord, Sven; Reit, Marco; Mathis, Wolfgang

    2017-09-01

    Recently, for modeling biological amplification processes, nonlinear amplifiers based on the supercritical Andronov-Hopf bifurcation have been widely analyzed analytically. For technical realizations, digital systems have become the most relevant systems in signal processing applications. The underlying continuous-time systems are transferred to the discrete-time domain using numerical integration methods. Within this contribution, effects on the qualitative behavior of the Andronov-Hopf bifurcation based systems concerning numerical integration methods are analyzed. It is shown exemplarily that explicit Runge-Kutta methods transform the truncated normalform equation of the Andronov-Hopf bifurcation into the normalform equation of the Neimark-Sacker bifurcation. Dependent on the order of the integration method, higher order terms are added during this transformation.A rescaled normalform equation of the Neimark-Sacker bifurcation is introduced that allows a parametric design of a discrete-time system which corresponds to the rescaled Andronov-Hopf system. This system approximates the characteristics of the rescaled Hopf-type amplifier for a large range of parameters. The natural frequency and the peak amplitude are preserved for every set of parameters. The Neimark-Sacker bifurcation based systems avoid large computational effort that would be caused by applying higher order integration methods to the continuous-time normalform equations.

  5. Nonlinear Progressive Collapse Analysis Including Distributed Plasticity

    Directory of Open Access Journals (Sweden)

    Mohamed Osama Ahmed

    2016-01-01

    Full Text Available This paper demonstrates the effect of incorporating distributed plasticity in nonlinear analytical models used to assess the potential for progressive collapse of steel framed regular building structures. Emphasis on this paper is on the deformation response under the notionally removed column, in a typical Alternate Path (AP method. The AP method employed in this paper is based on the provisions of the Unified Facilities Criteria – Design of Buildings to Resist Progressive Collapse, developed and updated by the U.S. Department of Defense [1]. The AP method is often used for to assess the potential for progressive collapse of building structures that fall under Occupancy Category III or IV. A case study steel building is used to examine the effect of incorporating distributed plasticity, where moment frames were used on perimeter as well as the interior of the three dimensional structural system. It is concluded that the use of moment resisting frames within the structural system will enhance resistance to progressive collapse through ductile deformation response and that it is conserative to ignore the effects of distributed plasticity in determining peak displacement response under the notionally removed column.

  6. Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

    Science.gov (United States)

    Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

    2017-09-01

    This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert

  7. Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

    Directory of Open Access Journals (Sweden)

    Abhijit Bhattacharyya

    2017-03-01

    Full Text Available This paper analyses the complexity of multivariate electroencephalogram (EEG signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT. In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy

  8. A cortical source localization analysis of resting EEG data after remifentanil infusion

    DEFF Research Database (Denmark)

    Khodayari-Rostamabad, Ahmad; Graversen, Carina; Malver, Lasse P;

    2015-01-01

    OBJECTIVE: To explore changes in current source density locations after remifentanil infusion in healthy volunteers using source localization of the electroencephalography (EEG). METHODS: EEG data was collected from 21 males using a 62-electrode system. Additionally, cognitive performance.......1-18Hz), and beta2 (18.1-30Hz) frequency bands. RESULTS: Pre-treatment recordings demonstrated reproducible source characteristics. The alterations (i.e., pre- versus post-treatment) due to remifentanil were significantly and robustly different from placebo infusions. The results indicated that neurons...... in several brain areas including inferior frontal gyrus and insula at frontal lobe oscillated more strongly after remifentanil infusion compared to placebo. Furthermore, the source activity at delta band was correlated with continuous reaction time index. CONCLUSIONS: These results indicate that alterations...

  9. Induced Gamma-Band Activity During Voluntary Movement: EEG Analysis for Clinical Purposes.

    Science.gov (United States)

    Amo, Carlos; Del Castillo, Miguel Ortiz; Barea, Rafael; de Santiago, Luis; Martínez-Arribas, Alejandro; Amo-López, Pedro; Boquete, Luciano

    2016-10-01

    Propose a simplified method applicable in routine clinical practice that uses EEG to assess induced gamma-band activity (GBA) in the 30-90 Hz frequency range in cerebral motor areas. EEG recordings (25 healthy subjects) of cerebral activity (at rest, motor task). GBA was obtained as power spectral density (PSD). GBA - defined as the gamma index (Iγ) - was calculated using the basal GBA (γB) and motor GBA (γMOV) PSD values. The mean values of Iγ were (IγR (right hand) = 1.30, IγL (left hand) = 1.22). Manual laterality showed a correlation with Iγ. Iγ may provide a useful way of indirectly assessing operation of activated motor neuronal circuits. It could be applied to diagnosis of motor area pathologies and as follow up in rehabilitation processes. Likewise, Iγ could enable the assessment of motor capacity, physical training and manual laterality in sport medicine.

  10. Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

    Science.gov (United States)

    Prinzel, Lawrence J., III; Pope, Alan T.; Freeman, Frederick G.; Scerbo, Mark W.; Mikulka, Peter J.

    2001-01-01

    The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

  11. A Novel Analysis of Performance Classification and Workload Prediction Using Electroencephalography (EEG) Frequency Data

    Science.gov (United States)

    2015-03-26

    the EEG frequency data in the HUMAN Lab study were that of the frontal region (F7, F8, Fz). The other four came from the Parietal , Occipital, and...from the Surveillance and Tracking tasks in the HUMAN Lab study, combined with the high ratio of frontal lobe region features and its sensitivity to...frontal node sites. It may be beneficial to only include Frontal lobe node sights to truly test their responsiveness to sensorimotor information

  12. Realistic and Spherical Head Modeling for EEG Forward Problem Solution: A Comparative Cortex-Based Analysis

    Science.gov (United States)

    Vatta, Federica; Meneghini, Fabio; Esposito, Fabrizio; Mininel, Stefano; Di Salle, Francesco

    2010-01-01

    The accuracy of forward models for electroencephalography (EEG) partly depends on head tissues geometry and strongly affects the reliability of the source reconstruction process, but it is not yet clear which brain regions are more sensitive to the choice of different model geometry. In this paper we compare different spherical and realistic head modeling techniques in estimating EEG forward solutions from current dipole sources distributed on a standard cortical space reconstructed from Montreal Neurological Institute (MNI) MRI data. Computer simulations are presented for three different four-shell head models, two with realistic geometry, either surface-based (BEM) or volume-based (FDM), and the corresponding sensor-fitted spherical-shaped model. Point Spread Function (PSF) and Lead Field (LF) cross-correlation analyses were performed for 26 symmetric dipole sources to quantitatively assess models' accuracy in EEG source reconstruction. Realistic geometry turns out to be a relevant factor of improvement, particularly important when considering sources placed in the temporal or in the occipital cortex. PMID:20169107

  13. Nonlinear eigenvalue approach to differential Riccati equations for contraction analysis

    NARCIS (Netherlands)

    Kawano, Yu; Ohtsuka, Toshiyuki

    2017-01-01

    In this paper, we extend the eigenvalue method of the algebraic Riccati equation to the differential Riccati equation (DRE) in contraction analysis. One of the main results is showing that solutions to the DRE can be expressed as functions of nonlinear eigenvectors of the differential Hamiltonian ma

  14. Nonlinear reaction coordinate analysis in the reweighted path ensemble

    NARCIS (Netherlands)

    Lechner, W.; Rogal, J.; Juraszek, J.; Ensing, B.; Bolhuis, P.G.

    2010-01-01

    We present a flexible nonlinear reaction coordinate analysis method for the transition path ensemble based on the likelihood maximization approach developed by Peters and Trout [J. Chem. Phys. 125, 054108 (2006)] . By parametrizing the reaction coordinate by a string of images in a collective variab

  15. Lower Bound Limit Analysis Of Slabs With Nonlinear Yield Criteria

    DEFF Research Database (Denmark)

    Krabbenhøft, Kristian; Damkilde, Lars

    2002-01-01

    A finite element formulation of the limit analysis of perfectly plastic slabs is given. An element with linear moment fields for which equilibrium is satisfied exactly is used in connection with an optimization algorithm taking into account the full nonlinearity of the yield criteria. Both load...

  16. Looking back at the gifi system of nonlinear multivariate analysis

    NARCIS (Netherlands)

    van der Heijden, Peter G M; van Buuren, Stef

    2016-01-01

    Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper

  17. Nonlinear Thermo-mechanical Finite Element Analysis of Polymer Foam Cored Sandwich Structures including Geometrical and Material Nonlinearity

    DEFF Research Database (Denmark)

    Palleti, Hara Naga Krishna Teja; Thomsen, Ole Thybo; Taher, Siavash Talebi;

    In this paper, polymer foam cored sandwich structures with fibre reinforced composite face sheets subjected to combined mechanical and thermal loads will be analysed using the commercial FE code ABAQUS® incorporating both material and geometrical nonlinearity. Large displacements and rotations ar...... are included in the analysis. The full nonlinear stress-strain curves up to failure will be considered for the polymer foams at different temperatures to study the effect of material nonlinearity in detail....

  18. AD GALERKIN ANALYSIS FOR NONLINEAR PSEUDO-HYPERBOLIC EQUATIONS

    Institute of Scientific and Technical Information of China (English)

    Xia Cui

    2003-01-01

    AD (Alternating direction) Galerkin schemes for d-dimensional nonlinear pseudo-hyperbolic equations are studied. By using patch approximation technique, AD procedure is realized,and calculation work is simplified. By using Galerkin approach, highly computational accuracy is kept. By using various priori estimate techniques for differential equations,difficulty coming from non-linearity is treated, and optimal H1 and L2 convergence properties are demonstrated. Moreover, although all the existed AD Galerkin schemes using patch approximation are limited to have only one order accuracy in time increment, yet the schemes formulated in this paper have second order accuracy in it. This implies an essential advancement in AD Galerkin analysis.

  19. Nonlinear analysis of flexible plates lying on elastic foundation

    Directory of Open Access Journals (Sweden)

    Trushin Sergey

    2017-01-01

    Full Text Available This article describes numerical procedures for analysis of flexible rectangular plates lying on elastic foundation. Computing models are based on the theory of plates with account of transverse shear deformations. The finite difference energy method of discretization is used for reducing the initial continuum problem to finite dimensional problem. Solution procedures for nonlinear problem are based on Newton-Raphson method. This theory of plates and numerical methods have been used for investigation of nonlinear behavior of flexible plates on elastic foundation with different properties.

  20. Practical Soil-Shallow Foundation Model for Nonlinear Structural Analysis

    Directory of Open Access Journals (Sweden)

    Moussa Leblouba

    2016-01-01

    Full Text Available Soil-shallow foundation interaction models that are incorporated into most structural analysis programs generally lack accuracy and efficiency or neglect some aspects of foundation behavior. For instance, soil-shallow foundation systems have been observed to show both small and large loops under increasing amplitude load reversals. This paper presents a practical macroelement model for soil-shallow foundation system and its stability under simultaneous horizontal and vertical loads. The model comprises three spring elements: nonlinear horizontal, nonlinear rotational, and linear vertical springs. The proposed macroelement model was verified using experimental test results from large-scale model foundations subjected to small and large cyclic loading cases.

  1. Modeling and stability analysis of the nonlinear reactive sputtering process

    Directory of Open Access Journals (Sweden)

    György Katalin

    2011-12-01

    Full Text Available The model of the reactive sputtering process has been determined from the dynamic equilibrium of the reactive gas inside the chamber and the dynamic equilibrium of the sputtered metal atoms which form the compound with the reactive gas atoms on the surface of the substrate. The analytically obtained dynamical model is a system of nonlinear differential equations which can result in a histeresis-type input/output nonlinearity. The reactive sputtering process has been simulated by integrating these differential equations. Linearization has been applied for classical analysis of the sputtering process and control system design.

  2. EEG based image encryption via quantum walks.

    Science.gov (United States)

    Rawat, N; Shin, Y; Balasingham, I

    2016-08-01

    An electroencephalogram (EEG) based image encryption combined with Quantum walks (QW) is encoded in Fresnel domain. The computational version of EEG randomizes the original plaintext whereas QW can serve as an excellent key generator due to its inherent nonlinear chaotic dynamic behavior. First, a spatially coherent monochromatic laser beam passes through an SLM, which introduces an arbitrary EEG phase-only mask. The modified beam is collected by a CCD. Further, the intensity is multiply with the QW digitally. EEG shows high sensitivity to system parameters and capable of encrypting and transmitting the data whereas QW has unpredictability, stability and non-periodicity. Only applying the correct keys, the original image can be retrieved successfully. Simulations and comparisons show the proposed method to be secure enough for image encryption and outperforms prior works. The proposed method opens the door towards introducing EEG and quantum computation into image encryption and promotes the convergence between our approach and image processing.

  3. Like/dislike analysis using EEG: determination of most discriminative channels and frequencies.

    Science.gov (United States)

    Yılmaz, Bülent; Korkmaz, Sümeyye; Arslan, Dilek Betül; Güngör, Evrim; Asyalı, Musa H

    2014-02-01

    In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of "like" decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, …, 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4-19 Hz and high frequency (HF) 20-40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential

  4. Multichannel EEG Visualization

    NARCIS (Netherlands)

    Caat, Michael ten

    2008-01-01

    Electroencephalography (EEG) measures electrical brain activity by electrodes attached to the scalp. Multichannel EEG refers to a measurement with a large number of electrodes. EEG has clinical as well as scientific applications, including neurology, psychology, pharmacy, linguistics, and biology.

  5. Homotopy analysis approach for nonlinear piezoelectric vibration energy harvesting

    Directory of Open Access Journals (Sweden)

    Shahlaei-Far Shahram

    2016-01-01

    Full Text Available Piezoelectric energy harvesting from a vertical geometrically nonlinear cantilever beam with a tip mass subject to transverse harmonic base excitations is analyzed. One piezoelectric patch is placed on the slender beam to convert the tension and compression into electrical voltage. Applying the homotopy analysis method to the coupled electromechanical governing equations, we derive analytical solutions for the horizontal displacement of the tip mass and consequently the output voltage from the piezoelectric patch. Analytical approximation for the frequency response and phase of the geometrically forced nonlinear vibration system are also obtained. The research aims at a rigorous analytical perspective on a nonlinear problem which has previously been solely investigated by numerical and experimental methods.

  6. Nonlinear systems techniques for dynamical analysis and control

    CERN Document Server

    Lefeber, Erjen; Arteaga, Ines

    2017-01-01

    This treatment of modern topics related to the control of nonlinear systems is a collection of contributions celebrating the work of Professor Henk Nijmeijer and honoring his 60th birthday. It addresses several topics that have been the core of Professor Nijmeijer’s work, namely: the control of nonlinear systems, geometric control theory, synchronization, coordinated control, convergent systems and the control of underactuated systems. The book presents recent advances in these areas, contributed by leading international researchers in systems and control. In addition to the theoretical questions treated in the text, particular attention is paid to a number of applications including (mobile) robotics, marine vehicles, neural dynamics and mechanical systems generally. This volume provides a broad picture of the analysis and control of nonlinear systems for scientists and engineers with an interest in the interdisciplinary field of systems and control theory. The reader will benefit from the expert participan...

  7. Stability Analysis and Design for Nonlinear Singular Systems

    CERN Document Server

    Yang, Chunyu; Zhou, Linna

    2013-01-01

    Singular systems which are also referred to as descriptor systems, semi-state systems, differential- algebraic systems or generalized state-space systems have attracted much attention because of their extensive applications in the Leontief dynamic model, electrical and mechanical models, etc. This monograph presented up-to-date research developments and references on stability analysis and design of nonlinear singular systems. It investigated the problems of practical stability, strongly absolute stability, input-state stability and observer design for nonlinear singular systems and the problems of absolute stability and multi-objective control for nonlinear singularly perturbed systems by using Lyapunov stability theory, comparison principle, S-procedure and linear matrix inequality (LMI), etc. Practical stability, being quite different from stability in the sense of Lyapunov, is a significant performance specification from an engineering point of view. The basic concepts and results on practical stability f...

  8. Asymptotic analysis of a vibrating cantilever with a nonlinear boundary

    Institute of Scientific and Technical Information of China (English)

    C.; W.; LIM

    2009-01-01

    Nonlinear vibration of a cantilever in a contact atomic force microscope is analyzed via an asymptotic approach. The asymptotic solution is sought for a beam equation with a nonlinear boundary condition. The steady-state responses are determined in primary resonance and subharmonic resonance. The relations between the response amplitudes and the excitation frequencies and amplitudes are derived from the solvability condition. Multivaluedness occurs in the relations as a consequence of the nonlinearity. The stability of steady-state responses is analyzed by use of the Lyapunov linearized stability theory. The stability analysis predicts the jumping phenomenon for certain parameters. The curves of the response amplitudes changing with the excitation frequencies are numerically compared with those obtained via the method of multiple scales. The calculation results demonstrate that the two methods predict the same varying tendencies while there are small quantitative differences.

  9. Asymptotic analysis of a vibrating cantilever with a nonlinear boundary

    Institute of Scientific and Technical Information of China (English)

    CHEN LiQun; C.W.LIM; HU QingQuan; DING Hu

    2009-01-01

    Nonlinear vibration of a cantilever in a contact atomic force microscope is analyzed via an asymptotic approach.The asymptotic solution is sought for a beam equation with a nonlinear boundary condition.The steady-state responses are determined in primary resonance and subharmonic resonance.The relations between the response amplitudes and the excitation frequencies and amplitudes are derived from the solvability condition.Multivaluedness occurs in the relations as a consequence of the nonlinearity.The stability of steady-state responses is analyzed by use of the Lyapunov linearized sta-bility theory.The stability analysis predicts the jumping phenomenon for certain parameters.The curves of the response amplitudes changing with the excitation frequencies are numerically compared with those obtained via the method of multiple scales.The calculation results demonstrate that the two methods predict the same varying tendencies while there are small quantitative differences.

  10. Asymptotic analysis of a vibrating cantilever with a nonlinear boundary

    Science.gov (United States)

    Chen, Liqun; Lim, C. W.; Hu, Qingquan; Ding, Hu

    2009-09-01

    Nonlinear vibration of a cantilever in a contact atomic force microscope is analyzed via an asymptotic approach. The asymptotic solution is sought for a beam equation with a nonlinear boundary condition. The steady-state responses are determined in primary resonance and subharmonic resonance. The relations between the response amplitudes and the excitation frequencies and amplitudes are derived from the solvability condition. Multivaluedness occurs in the relations as a consequence of the nonlinearity. The stability of steady-state responses is analyzed by use of the Lyapunov linearized stability theory. The stability analysis predicts the jumping phenomenon for certain parameters. The curves of the response amplitudes changing with the excitation frequencies are numerically compared with those obtained via the method of multiple scales. The calculation results demonstrate that the two methods predict the same varying tendencies while there are small quantitative differences.

  11. Analysis of Nonlinear Dynamics by Square Matrix Method

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Li Hua [Brookhaven National Lab. (BNL), Upton, NY (United States). Energy and Photon Sciences Directorate. National Synchrotron Light Source II

    2016-07-25

    The nonlinear dynamics of a system with periodic structure can be analyzed using a square matrix. In this paper, we show that because the special property of the square matrix constructed for nonlinear dynamics, we can reduce the dimension of the matrix from the original large number for high order calculation to low dimension in the first step of the analysis. Then a stable Jordan decomposition is obtained with much lower dimension. The transformation to Jordan form provides an excellent action-angle approximation to the solution of the nonlinear dynamics, in good agreement with trajectories and tune obtained from tracking. And more importantly, the deviation from constancy of the new action-angle variable provides a measure of the stability of the phase space trajectories and their tunes. Thus the square matrix provides a novel method to optimize the nonlinear dynamic system. The method is illustrated by many examples of comparison between theory and numerical simulation. Finally, in particular, we show that the square matrix method can be used for optimization to reduce the nonlinearity of a system.

  12. A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier.

    Science.gov (United States)

    Nasehi, Saadat; Pourghassem, Hossein

    2012-08-01

    This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature.

  13. Locating the STN-DBS electrodes and resolving their subsequent networks using coherent source analysis on EEG.

    Science.gov (United States)

    Muthuraman, M; Paschen, S; Hellriegel, H; Groppa, S; Deuschl, G; Raethjen, J

    2012-01-01

    The deep brain stimulation (DBS) of the subthalamic nucleus (STN) is the most effective surgical therapy for Parkinson's disease (PD). The first aim of the study was to locate the STN-DBS electrode by applying source analysis on EEG. Secondly, to identify tremor related areas which are associated with the STN. The Dynamic imaging of coherent sources (DICS) was used to find the coherent sources in the brain. The capability of the source analysis to detect deep sources like STN in the brain using EEG data was tested with two model dipole simulations. The simulations were concentrated on two aspects, the angle of the dipole orientation and the disturbance of the cortical areas on locating subcortical regions. In all the DBS treated Parkinsonian tremor patients the power spectrum showed a clear peak at the stimulated frequency and followed by there harmonics. The DBS stimulated frequency constituted a network of primary sensory motor cortex, supplementary motor area, prefrontal cortex, diencephalon, cerebellum and brainstem. Thus the STN was located in the region of the diencephalon. The resolved network may give better understanding to the pathophysiology of the effected tremor network in PD patients with STN-DBS.

  14. Analysis of nonlinear elastic behavior in miniature pneumatic artificial muscles

    Science.gov (United States)

    Hocking, Erica G.; Wereley, Norman M.

    2013-01-01

    Pneumatic artificial muscles (PAMs) are well known for their excellent actuator characteristics, including high specific work, specific power, and power density. Recent research has focused on miniaturizing this pneumatic actuator technology in order to develop PAMs for use in small-scale mechanical systems, such as those found in robotic or aerospace applications. The first step in implementing these miniature PAMs was to design and characterize the actuator. To that end, this study presents the manufacturing process, experimental characterization, and analytical modeling of PAMs with millimeter-scale diameters. A fabrication method was developed to consistently produce low-cost, high performance, miniature PAMs using commercially available materials. The quasi-static behavior of these PAMs was determined through experimentation on a single actuator with an active length of 39.16 mm (1.54 in) and a diameter of 4.13 mm (0.1625 in). Testing revealed the PAM’s full evolution of force with displacement for operating pressures ranging from 207 to 552 kPa (30-80 psi in 10 psi increments), as well as the blocked force and free contraction at each pressure. Three key nonlinear phenomena were observed: nonlinear PAM stiffness, hysteresis of the force versus displacement response for a given pressure, and a pressure deadband. To address the analysis of the nonlinear response of these miniature PAMs, a nonlinear stress versus strain model, a hysteresis model, and a pressure bias are introduced into a previously developed force balance analysis. Parameters of these nonlinear model refinements are identified from the measured force versus displacement data. This improved nonlinear force balance model is shown to capture the full actuation behavior of the miniature PAMs at each operating pressure and reconstruct miniature PAM response with much more accuracy than previously possible.

  15. EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes

    Directory of Open Access Journals (Sweden)

    Zhendong Mu

    2016-12-01

    Full Text Available Person authentication, based on electroencephalography (EEG signals, is one of the directions possible in the study of EEG signals. In this paper, a method for the selection of EEG electrodes and features in a discriminative manner is proposed. Given that EEG signals are unstable and non-linear, a non-linear analysis method, i.e., fuzzy entropy, is more appropriate. In this paper, unlike other methods using different signal sources and patterns, such as rest state and motor imagery, a novel paradigm using the stimuli of self-photos and non-self-photos is introduced. Ten subjects are selected to take part in this experiment, and fuzzy entropy is used as a feature to select the minimum number of electrodes that identifies individuals. The experimental results show that the proposed method can make use of two electrodes (FP1 and FP2 in the frontal area, while the classification accuracy is greater than 87.3%. The proposed biometric system, based on EEG signals, can provide each subject with a unique key and is capable of human recognition.

  16. 基于递归量化分析与支持向量机的癫痫脑电自动检测方法%Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM

    Institute of Scientific and Technical Information of China (English)

    孟庆芳; 陈珊珊; 陈月辉; 冯志全

    2014-01-01

    癫痫脑电信号的自动检测对癫痫的临床诊断与治疗具有重要意义.基于递归图(recurrence plot)的递归量化分析(recurrence quantification analysis, RQA)重现了非线性时间序列的动力学行为,分析了其递归特性,本文提出了基于RQA的癫痫脑电信号特征提取方法.实验结果表明:直接基于RQA特征的癫痫脑电的检测准确率较高,其中直接基于确定率DET的分类准确率可达到90.25%.本文还把提取的RQA特征值和变化系数、波动指数相结合组成特征向量,输入到SVM分类器,实现癫痫脑电信号的自动检测;实验结果表明:该方法的分类准确率可达到99%.%Automatic detection and classification of epileptic EEG signals have been a significance method for the clinical diagnosis and treatment of epilepsy. The recurrence quantification analysis (RQA) based on the recurrence plot could visualize the recurrence behaviors of dynamical systems from the nonlinear time series and analysis of the recurrence properties. This paper presents a new feature extraction method for epileptic EEG signals based on the recurrence quantification analysis. Experimental results show that the seizure detection directly based on recurrence quantification analysis features has a higher detection performance; especially the classification accuracy based on the deterministic feature can be up to 90.25%. This paper also combines the RQA features with the variation coefficient and fluctuation index, and then puts the feature vectors into a support vector machine (SVM) to automatically detect the epileptic EEG from EEG recordings. Experimental results shows that the proposed methods could achieve a great classification accuracy of 99%.

  17. Analysis and design of nonlinear resonances via singularity theory

    CERN Document Server

    Cirillo, G I; Kerschen, G; Sepulchre, R

    2016-01-01

    Bifurcation theory and continuation methods are well-established tools for the analysis of nonlinear mechanical systems subject to periodic forcing. We illustrate the added value and the complementary information provided by singularity theory with one distinguished parameter. While tracking bifurcations reveals the qualitative changes in the behaviour, tracking singularities reveals how structural changes are themselves organised in parameter space. The complementarity of that information is demonstrated in the analysis of detached resonance curves in a two-degree-of-freedom system.

  18. Analysis and design of nonlinear resonances via singularity theory

    Science.gov (United States)

    Cirillo, G. I.; Habib, G.; Kerschen, G.; Sepulchre, R.

    2017-03-01

    Bifurcation theory and continuation methods are well-established tools for the analysis of nonlinear mechanical systems subject to periodic forcing. We illustrate the added value and the complementary information provided by singularity theory with one distinguished parameter. While tracking bifurcations reveals the qualitative changes in the behaviour, tracking singularities reveals how structural changes are themselves organised in parameter space. The complementarity of that information is demonstrated in the analysis of detached resonance curves in a two-degree-of-freedom system.

  19. Complexity of EEG-signal in Time Domain - Possible Biomedical Application

    Science.gov (United States)

    Klonowski, Wlodzimierz; Olejarczyk, Elzbieta; Stepien, Robert

    2002-07-01

    Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG-signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG-signal can help to find `the signatures' of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.

  20. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  1. The Nonlinear World Conceptual Analysis and Phenomenology

    CERN Document Server

    Oono, Yoshitsugu

    2013-01-01

    The most important characteristic of the “world filled with nonlinearity” is the existence of scale interference: disparate space–time scales interfere with each other. Thus, the effects of unknowable scales invade the world that we can observe directly. This leads to various peculiar phenomena such as chaos, critical phenomena, and complex biological phenomena, among others. Conceptual analysis and phenomenology are the keys to describe and understand phenomena that are subject to scale interference, because precise description of unfamiliar phenomena requires precise concepts and their phenomenological description. The book starts with an illustration of conceptual analysis in terms of chaos and randomness, and goes on to explain renormalization group philosophy as an approach to phenomenology. Then, abduction is outlined as a way to express what we have understood about the world. The book concludes with discussions on how we can approach genuinely complex phenomena, including biological phenomena. T...

  2. Nonlinear Stochastic PDEs: Analysis and Approximations

    Science.gov (United States)

    2016-05-23

    Distribution free Skorokhod-Malliavian Calculus , Stochastic And Partial Differential Equations: Analysis and Computations, (06 2016): 319. doi : Z. Zhang... doi : X. Wang, Boris Rozovskii. The Wick-Malliavin Approximation on Elliptic Problems with Long-Normal Random Coefficients, SIAM J Scientific...Computing, (10 2013): 2370. doi : Z. Zhang, M.V. Trrtykov, B. Rozovskii, G.E. Karniadakis. A Recursive Sparse Grid Collocation Methd for Differential

  3. Detection of burst suppression patterns in EEG using recurrence rate.

    Science.gov (United States)

    Liang, Zhenhu; Wang, Yinghua; Ren, Yongshao; Li, Duan; Voss, Logan; Sleigh, Jamie; Li, Xiaoli

    2014-01-01

    Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P = 0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

  4. Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

    Directory of Open Access Journals (Sweden)

    Zhenhu Liang

    2014-01-01

    Full Text Available Burst suppression is a unique electroencephalogram (EEG pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP analysis for the detection of the burst suppression pattern (BSP in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR, determinism (DET, and entropy (ENTR are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO. ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%,  P=0.03. Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

  5. Nonlinear acoustic analysis in the evaluation of occupational voice disorders

    Directory of Open Access Journals (Sweden)

    Ewa Niebudek-Bogusz

    2013-02-01

    Full Text Available Background: Over recent years numerous papers have stressed that production of voice is subjected to the nonlinear processes, which cause aperiodic vibrations of vocal folds. These vibrations cannot always be characterized by means of conventional acoustic parameters, such as measurements of frequency and amplitude perturbations. Thus, special attention has recently been paid to nonlinear acoustic methods. The aim of this study was to assess the applicability of nonlinear cepstral analysis, including the evaluation of mel cepstral coefficients (MFCC, in diagnosing occupational voice disorders. Material and methods: The study involved 275 voice samples of pathologic voice (sustained vowel "a" and four standardized sentences registered in female teachers with the occupation-related benign vocal fold masses (BVFM, such as vocal nodules, polyps, and 200 voice samples of normal voices from the control group of females. The mean age of patients and controls was similar (45 vs. 43 years. Voice samples from both groups were analyzed, including MFCC evaluation. Results: MFCC classification using the Sammon Mapping and Support Vector Machines yielded a considerable accuracy of the test. Voice pathologies were detected in 475 registered voice samples: for vowel "a" with 86% sensitivity and 90% specificity, and for the examined sentences the corresponding values varied between 87% and 100%, respectively. Conclusions: Nonlinear voice analysis with application of mel cepstral coefficients could be a useful and objective tool for confirming occupational-related lesions of the glottis. Further studies addressing this problem are being carried out. Med Pr 2013;64(1:29–35

  6. Nonlinear Pressure Wave Analysis by Concentrated Mass Model

    Science.gov (United States)

    Ishikawa, Satoshi; Kondou, Takahiro; Matsuzaki, Kenichiro

    A pressure wave propagating in a tube often changes to a shock wave because of the nonlinear effect of fluid. Analyzing this phenomenon by the finite difference method requires high computational cost. To lessen the computational cost, a concentrated mass model is proposed. This model consists of masses, connecting nonlinear springs, connecting dampers, and base support dampers. The characteristic of a connecting nonlinear spring is derived from the adiabatic change of fluid, and the equivalent mass and equivalent damping coefficient of the base support damper are derived from the equation of motion of fluid in a cylindrical tube. Pressure waves generated in a hydraulic oil tube, a sound tube and a plane-wave tube are analyzed numerically by the proposed model to confirm the validity of the model. All numerical computational results agree very well with the experimental results carried out by Okamura, Saenger and Kamakura. Especially, the numerical analysis reproduces the phenomena that a pressure wave with large amplitude propagating in a sound tube or in a plane tube changes to a shock wave. Therefore, it is concluded that the proposed model is valid for the numerical analysis of nonlinear pressure wave problem.

  7. Probabilistic finite elements for transient analysis in nonlinear continua

    Science.gov (United States)

    Liu, W. K.; Belytschko, T.; Mani, A.

    1985-01-01

    The probabilistic finite element method (PFEM), which is a combination of finite element methods and second-moment analysis, is formulated for linear and nonlinear continua with inhomogeneous random fields. Analogous to the discretization of the displacement field in finite element methods, the random field is also discretized. The formulation is simplified by transforming the correlated variables to a set of uncorrelated variables through an eigenvalue orthogonalization. Furthermore, it is shown that a reduced set of the uncorrelated variables is sufficient for the second-moment analysis. Based on the linear formulation of the PFEM, the method is then extended to transient analysis in nonlinear continua. The accuracy and efficiency of the method is demonstrated by application to a one-dimensional, elastic/plastic wave propagation problem. The moments calculated compare favorably with those obtained by Monte Carlo simulation. Also, the procedure is amenable to implementation in deterministic FEM based computer programs.

  8. Non-linear analysis in Light Water Reactor design

    Energy Technology Data Exchange (ETDEWEB)

    Rashid, Y.R.; Sharabi, M.N.; Nickell, R.E.; Esztergar, E.P.; Jones, J.W.

    1980-03-01

    The results obtained from a scoping study sponsored by the US Department of Energy (DOE) under the Light Water Reactor (LWR) Safety Technology Program at Sandia National Laboratories are presented. Basically, this project calls for the examination of the hypothesis that the use of nonlinear analysis methods in the design of LWR systems and components of interest include such items as: the reactor vessel, vessel internals, nozzles and penetrations, component support structures, and containment structures. Piping systems are excluded because they are being addressed by a separate study. Essentially, the findings were that nonlinear analysis methods are beneficial to LWR design from a technical point of view. However, the costs needed to implement these methods are the roadblock to readily adopting them. In this sense, a cost-benefit type of analysis must be made on the various topics identified by these studies and priorities must be established. This document is the complete report by ANATECH International Corporation.

  9. Arc-length technique for nonlinear finite element analysis

    Institute of Scientific and Technical Information of China (English)

    MEMON Bashir-Ahmed; SU Xiao-zu(苏小卒)

    2004-01-01

    Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, Received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.

  10. Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

    Directory of Open Access Journals (Sweden)

    Khald Ali I. Aboalayon

    2016-08-01

    Full Text Available Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

  11. Ketamine, propofol and the EEG: a neural field analysis of HCN1-mediated interactions

    Directory of Open Access Journals (Sweden)

    Ingo eBojak

    2013-04-01

    Full Text Available Ketamine and propofol are two well-known, powerful anesthetic agents, yet at first sight this appears to be their only commonality. Ketamine is a dissociative anesthetic agent, whose main mechanism of action is considered to be N-methyl-D-aspartate (NMDA antagonism; whereas propofol is a general anesthetic agent, which is assumed to primarily potentiate currents gated by γ-aminobutyric acid type A (GABA A receptors. However, several experimental observations suggest a closer relationship. First, the effect of ketamine on the electroencephalogram (EEG is markedly changed in the presence of propofol: on its own ketamine increases theta (4–8 Hz and decreases alpha (8–13 Hz oscillations, whereas ketamine induces a significant shift to beta band frequencies (13–30 Hz in the presence of propofol. Second, both ketamine and propofol cause inhibition of the inward pacemaker current Ih, by binding to the corresponding hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1 subunit. The resulting effect is a hyperpolarization of the neuron’s resting membrane potential. Third, the ability of both ketamine and propofol to induce hypnosis is reduced in HCN1-knockout mice. Here we show that one can theoretically understand the observed spectral changes of the EEG based on HCN1-mediated hyperpolarizations alone, without involving the supposed main mechanisms of action of these drugs through NMDA and GABA A, respectively. On the basis of our successful EEG model we conclude that ketamine and propofol should be antagonistic to each other in their interaction at HCN1 subunits. Such a prediction is in accord with the results of clinical experiment in which it is found that ketamine and propofol interact in an infra-additive manner with respect to the endpoints of hypnosis and immobility.

  12. Indications of nonlinear structures in brain electrical activity

    Science.gov (United States)

    Gautama, Temujin; Mandic, Danilo P.; van Hulle, Marc M.

    2003-04-01

    The dynamical properties of electroencephalogram (EEG) segments have recently been analyzed by Andrzejak and co-workers for different recording regions and for different brain states, using the nonlinear prediction error and an estimate of the correlation dimension. In this paper, we further investigate the nonlinear properties of the EEG signals using two established nonlinear analysis methods, and introduce a “delay vector variance” (DVV) method for better characterizing a time series. The proposed DVV method is shown to enable a comprehensive characterization of the time series, allowing for a much improved classification of signal modes. This way, the analysis of Andrzejak and co-workers can be extended toward classification of different brain states. The obtained results comply with those described by Andrzejak et al., and provide complementary indications of nonlinearity in the signals.

  13. Detection of the short-term preseizure changes in EEG recordings using complexity and synchrony analysis

    Institute of Scientific and Technical Information of China (English)

    JIA Wenyan; KONG Na; MA Jun; LIU Hesheng; GAO Xiaorong; GAO Shangkai; YANG Fusheng

    2006-01-01

    An important consideration in epileptic seizure prediction is proving the existence of a pre-seizure state that can be detected using various signal processing algorithms. In the analyses of intracranial electroencephalographic (EEG)recordings of four epilepsy patients, the short-term changes in the measures of complexity and synchrony were detected before the majority of seizure events across the sample patient population. A decrease in complexity and increase in phase synchrony appeared several minutes before seizure onset and the changes were more pronounced in the focal region than in the remote region. This result was also validated statistically using a surrogate data method.

  14. Serial identification of EEG patterns using adaptive wavelet-based analysis

    Science.gov (United States)

    Nazimov, A. I.; Pavlov, A. N.; Nazimova, A. A.; Grubov, V. V.; Koronovskii, A. A.; Sitnikova, E.; Hramov, A. E.

    2013-10-01

    A problem of recognition specific oscillatory patterns in the electroencephalograms with the continuous wavelet-transform is discussed. Aiming to improve abilities of the wavelet-based tools we propose a serial adaptive method for sequential identification of EEG patterns such as sleep spindles and spike-wave discharges. This method provides an optimal selection of parameters based on objective functions and enables to extract the most informative features of the recognized structures. Different ways of increasing the quality of patterns recognition within the proposed serial adaptive technique are considered.

  15. Wavelet domain analysis of EEG data for emotion recognition: evaluation of recoursing energy efficiency

    Science.gov (United States)

    Aspiras, Theus H.; Asari, Vijayan K.

    2011-06-01

    In this paper, we evaluate the feature extraction technique of Recoursing Energy Efficiency on electroencephalograph data for human emotion recognition. A protocol has been established to elicit five distinct emotions (joy, sadness, disgust, fear, surprise, and neutral). EEG signals are collected using a 256-channel system, preprocessed using band-pass filters and Laplacian Montage, and decomposed into five frequency bands using Discrete Wavelet Transform. The Recoursing Energy Efficiency (REE) is calculated and applied to a Multi-Layer Perceptron network for classification. We compare the performance of REE features with conventional energy based features.

  16. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

    Directory of Open Access Journals (Sweden)

    Logothetis Nikos K

    2009-07-01

    Full Text Available Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast

  17. Nonlinear Seismic Analysis of Morrow Point Dam

    Energy Technology Data Exchange (ETDEWEB)

    Noble, C R; Nuss, L K

    2004-02-20

    This research and development project was sponsored by the United States Bureau of Reclamation (USBR), who are best known for the dams, power plants, and canals it constructed in the 17 western states. The mission statement of the USBR's Dam Safety Office, located in Denver, Colorado, is ''to ensure Reclamation dams do not present unacceptable risk to people, property, and the environment.'' The Dam Safety Office does this by quickly identifying the dams which pose an increased threat to the public, and quickly completing the related analyses in order to make decisions that will safeguard the public and associated resources. The research study described in this report constitutes one element of USBR's research and development work to advance their computational and analysis capabilities for studying the response of dams to strong earthquake motions. This project focused on the seismic response of Morrow Point Dam, which is located 263 km southwest of Denver, Colorado.

  18. A differentiating empirical linguistic analysis of dreamer activity in reports of EEG-controlled REM-dreams and hypnagogic hallucinations.

    Science.gov (United States)

    Speth, Jana; Frenzel, Clemens; Voss, Ursula

    2013-09-01

    We present Activity Analysis as a new method for the quantification of subjective reports of altered states of consciousness with regard to the indicated level of simulated motor activity. Empirical linguistic activity analysis was conducted with dream reports conceived immediately after EEG-controlled periods of hypnagogic hallucinations and REM-sleep in the sleep laboratory. Reports of REM-dreams exhibited a significantly higher level of simulated physical dreamer activity, while hypnagogic hallucinations appear to be experienced mostly from the point of passive observer. This study lays the groundwork for clinical research on the level of simulated activity in pathologically altered states of subjective experience, for example in the REM-dreams of clinically depressed patients, or in intrusions and dreams of patients diagnosed with PTSD.

  19. A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation

    Science.gov (United States)

    Götz, Th; Stadler, L.; Fraunhofer, G.; Tomé, A. M.; Hausner, H.; Lang, E. W.

    2017-02-01

    Objective. We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. Approach. EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. Main results. The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands. Significance. The proposed method provides various means to discriminate both disease pictures in a clinical environment.

  20. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

    Science.gov (United States)

    Akrami, Amin; Nazeri, Sina

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose. PMID:27699169

  1. Sensorimotor cortical response during motion reflecting audiovisual stimulation: evidence from fractal EEG analysis.

    Science.gov (United States)

    Hadjidimitriou, S; Zacharakis, A; Doulgeris, P; Panoulas, K; Hadjileontiadis, L; Panas, S

    2010-06-01

    Sensorimotor activity in response to motion reflecting audiovisual titillation is studied in this article. EEG recordings, and especially the Mu-rhythm over the sensorimotor cortex (C3, CZ, and C4 electrodes), were acquired and explored. An experiment was designed to provide auditory (Modest Mussorgsky's "Promenade" theme) and visual (synchronized human figure walking) stimuli to advanced music students (AMS) and non-musicians (NM) as a control subject group. EEG signals were analyzed using fractal dimension (FD) estimation (Higuchi's, Katz's and Petrosian's algorithms) and statistical methods. Experimental results from the midline electrode (CZ) based on the Higuchi method showed significant differences between the AMS and the NM groups, with the former displaying substantial sensorimotor response during auditory stimulation and stronger correlation with the acoustic stimulus than the latter. This observation was linked to mirror neuron system activity, a neurological mechanism that allows trained musicians to detect action-related meanings underlying the structural patterns in musical excerpts. Contrarily, the response of AMS and NM converged during audiovisual stimulation due to the dominant presence of human-like motion in the visual stimulus. These findings shed light upon music perception aspects, exhibiting the potential of FD to respond to different states of cortical activity.

  2. Simple and difficult mathematics in children: a minimum spanning tree EEG network analysis.

    Science.gov (United States)

    Vourkas, Michael; Karakonstantaki, Eleni; Simos, Panagiotis G; Tsirka, Vasso; Antonakakis, Marios; Vamvoukas, Michael; Stam, Cornelis; Dimitriadis, Stavros; Micheloyannis, Sifis

    2014-07-25

    Sensor-level network characteristics associated with arithmetic tasks varying in complexity were estimated using tools from modern network theory. EEG signals from children with math difficulties (MD) and typically achieving controls (NI) were analyzed using minimum spanning tree (MST) indices derived from Phase Lag Index values - a graph method that corrects for comparison bias. Results demonstrated progressive modulation of certain MST parameters with increased task difficulty. These findings were consistent with more distributed network activation in the theta band, and greater network integration (i.e., tighter communication between involved regions) in the alpha band as task demands increased. There was also evidence of stronger intraregional signal inter-dependencies in the higher frequency bands during the complex math task. Although these findings did not differ between groups, several MST parameters were positively correlated with individual performance on psychometric math tasks involving similar operations, especially in the NI group. The findings support the potential utility of MST analyses to evaluate function-related electrocortical reactivity over a wide range of EEG frequencies in children.

  3. EEG resting state functional connectivity analysis in children with benign epilepsy with centrotemporal spikes

    Directory of Open Access Journals (Sweden)

    Azeez eAdebimpe

    2016-03-01

    Full Text Available In this study, we investigated changes in functional connectivity of the brain networks in patients with benign epilepsy with centrotemporal spikes compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnetic Tomography (eLORETA. We investigated functional connectivity (FC between 84 Brodmann areas using lagged phase synchronization (LPS in four frequency bands (δ, θ, α, and β. We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β lagged phase synchronization values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the beta band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.

  4. Analysis of EEG activity in response to binaural beats with different frequencies.

    Science.gov (United States)

    Gao, Xiang; Cao, Hongbao; Ming, Dong; Qi, Hongzhi; Wang, Xuemin; Wang, Xiaolu; Chen, Runge; Zhou, Peng

    2014-12-01

    When two coherent sounds with nearly similar frequencies are presented to each ear respectively with stereo headphones, the brain integrates the two signals and produces a sensation of a third sound called binaural beat (BB). Although earlier studies showed that BB could influence behavior and cognition, common agreement on the mechanism of BB has not been reached yet. In this work, we employed Relative Power (RP), Phase Locking Value (PLV) and Cross-Mutual Information (CMI) to track EEG changes during BB stimulations. EEG signals were acquired from 13 healthy subjects. Five-minute BBs with four different frequencies were tested: delta band (1 Hz), theta band (5 Hz), alpha band (10 Hz) and beta band (20 Hz). We observed RP increase in theta and alpha bands and decrease in beta band during delta and alpha BB stimulations. RP decreased in beta band during theta BB, while RP decreased in theta band during beta BB. However, no clear brainwave entrainment effect was identified. Connectivity changes were detected following the variation of RP during BB stimulations. Our observation supports the hypothesis that BBs could affect functional brain connectivity, suggesting that the mechanism of BB-brain interaction is worth further study. Copyright © 2014. Published by Elsevier B.V.

  5. EEG Spectral Analysis in Serious Gaming: An Ad Hoc Experimental Application

    Directory of Open Access Journals (Sweden)

    Minchev Z.

    2009-12-01

    Full Text Available The application of serious gaming technology in different areas of human knowledge for learning is raising the question of quantitative measurement of the training process quality. In the present paper a pilot study of 10 healthy volunteers' EEG spectra is performed for ad hoc selected game events ('win' and 'lose' via continuous wavelet transform (real and complex on the basis of the Morlet mother wavelet function and S-transformation. The results have shown a general decrease of the alpha rhythms power spectra frequencies for the 'lose' events and increase for the 'win' events. This fact corresponds to an opposite behaviour of the theta rhythm of the players for the same 'win' and 'lose' events. Additionally, the frequency changes in the alpha1 (8-10.5 Hz, alpha2 (10.5-13 Hz and theta2 rhythms (6-8 Hz were supposed to be a phenomena related to positive and negative emotions appearance in the EEG activity of the players regarding the selected 'win' and 'lose' states.

  6. Slope stability analysis using limit equilibrium method in nonlinear criterion.

    Science.gov (United States)

    Lin, Hang; Zhong, Wenwen; Xiong, Wei; Tang, Wenyu

    2014-01-01

    In slope stability analysis, the limit equilibrium method is usually used to calculate the safety factor of slope based on Mohr-Coulomb criterion. However, Mohr-Coulomb criterion is restricted to the description of rock mass. To overcome its shortcomings, this paper combined Hoek-Brown criterion and limit equilibrium method and proposed an equation for calculating the safety factor of slope with limit equilibrium method in Hoek-Brown criterion through equivalent cohesive strength and the friction angle. Moreover, this paper investigates the impact of Hoek-Brown parameters on the safety factor of slope, which reveals that there is linear relation between equivalent cohesive strength and weakening factor D. However, there are nonlinear relations between equivalent cohesive strength and Geological Strength Index (GSI), the uniaxial compressive strength of intact rock σ ci , and the parameter of intact rock m i . There is nonlinear relation between the friction angle and all Hoek-Brown parameters. With the increase of D, the safety factor of slope F decreases linearly; with the increase of GSI, F increases nonlinearly; when σ ci is relatively small, the relation between F and σ ci is nonlinear, but when σ ci is relatively large, the relation is linear; with the increase of m i , F decreases first and then increases.

  7. Nonlinear Finite Element Analysis of Reinforced Concrete Shells

    Directory of Open Access Journals (Sweden)

    Mustafa K. Ahmed

    2013-05-01

    Full Text Available This investigation is to develop a numerical model suitable for nonlinear analysis of reinforced concrete shells. A nine-node Lagrangian element Figure (1 with enhanced shear interpolation will be used in this study. Table (1 describes shape functions and their derivatives of this element.An assumed transverse shear strain is used in the formulation of this element to overcome shear locking. Degenerated quadratic thick plate elements employing a layered discrelization through the thickness will be adopted. Different numbers of layers for different thickness can be used per element. A number of layers between (6 and 10 have proved to be appropriate to represent the nonlinear material behavior in structures. In this research 8 layers will be adequate. Material nonlinearities due to cracking of concrete, plastic flow or crushing of concrete in compression and yield condition of reinforcing steel are considered. The maximum tensile strength is used as a criterion for crack initiation. Attention is given to the tension stiffening phenomenon and the degrading effect of cracking on the compressive and shear strength of concrete. Perfect bond between concrete and steel is assumed. Attention is given also to geometric nonlinearities. An example have been chosen in order to demonstrate the suitability of the models by comparing the predicted behaviour with the experimental results for shell exhibiting various modes of failure.

  8. Investigation of Nonlinear Pupil Dynamics by Recurrence Quantification Analysis

    Directory of Open Access Journals (Sweden)

    L. Mesin

    2013-01-01

    Full Text Available Pupil is controlled by the autonomous nervous system (ANS. It shows complex movements and changes of size even in conditions of constant stimulation. The possibility of extracting information on ANS by processing data recorded during a short experiment using a low cost system for pupil investigation is studied. Moreover, the significance of nonlinear information contained in the pupillogram is investigated. We examined 13 healthy subjects in different stationary conditions, considering habitual dental occlusion (HDO as a weak stimulation of the ANS with respect to the maintenance of the rest position (RP of the jaw. Images of pupil captured by infrared cameras were processed to estimate position and size on each frame. From such time series, we extracted linear indexes (e.g., average size, average displacement, and spectral parameters and nonlinear information using recurrence quantification analysis (RQA. Data were classified using multilayer perceptrons and support vector machines trained using different sets of input indexes: the best performance in classification was obtained including nonlinear indexes in the input features. These results indicate that RQA nonlinear indexes provide additional information on pupil dynamics with respect to linear descriptors, allowing the discrimination of even a slight stimulation of the ANS. Their use in the investigation of pathology is suggested.

  9. Geometrically Nonlinear Finite Element Analysis of a Composite Space Reflector

    Science.gov (United States)

    Lee, Kee-Joo; Leet, Sung W.; Clark, Greg; Broduer, Steve (Technical Monitor)

    2001-01-01

    Lightweight aerospace structures, such as low areal density composite space reflectors, are highly flexible and may undergo large deflection under applied loading, especially during the launch phase. Accordingly, geometrically nonlinear analysis that takes into account the effect of finite rotation may be needed to determine the deformed shape for a clearance check and the stress and strain state to ensure structural integrity. In this study, deformation of the space reflector is determined under static conditions using a geometrically nonlinear solid shell finite element model. For the solid shell element formulation, the kinematics of deformation is described by six variables that are purely vector components. Because rotational angles are not used, this approach is free of the limitations of small angle increments. This also allows easy connections between substructures and large load increments with respect to the conventional shell formulation using rotational parameters. Geometrically nonlinear analyses were carried out for three cases of static point loads applied at selected points. A chart shows results for a case when the load is applied at the center point of the reflector dish. The computed results capture the nonlinear behavior of the composite reflector as the applied load increases. Also, they are in good agreement with the data obtained by experiments.

  10. Painlevé analysis for nonlinear partial differential equations

    CERN Document Server

    Musette, M

    1998-01-01

    The Painlevé analysis introduced by Weiss, Tabor and Carnevale (WTC) in 1983 for nonlinear partial differential equations (PDE's) is an extension of the method initiated by Painlevé and Gambier at the beginning of this century for the classification of algebraic nonlinear differential equations (ODE's) without movable critical points. In these lectures we explain the WTC method in its invariant version introduced by Conte in 1989 and its application to solitonic equations in order to find algorithmically their associated so-called ``integrable'' equations but they are generically no more valid for equations modelising physical phenomema. Belonging to this second class, some equations called ``partially integrable'' sometimes keep remnants of integrability. In that case, the singularity analysis may also be useful for building closed form analytic solutions, which necessarily % Conte agree with the singularity structure of the equations. We display the privileged role played by the Riccati equation and syste...

  11. Nonlinear optical polarization analysis in chemistry and biology

    CERN Document Server

    Simpson, Garth J

    2017-01-01

    This rigorous yet accessible guide presents a molecular-based description of nonlinear optical polarization analysis of chemical and biological assemblies. It includes discussion of the most common nonlinear optical microscopy and interfacial measurements used for quantitative analysis, specifically second harmonic generation (SHG), two-photon excited fluorescence (2PEF), vibrational sum frequency generation (SFG), and coherent anti-Stokes Raman spectroscopy/stimulated Raman spectroscopy (CARS/SRS). A linear algebra mathematical framework is developed, allowing step-wise systematic connections to be made between the observable measurements and the molecular response. Effects considered include local field corrections, the molecular orientation distribution, rotations between the molecular frame, the local frame and the laboratory frame, and simplifications from molecular and macromolecular symmetry. Specific examples are provided throughout the book, working from the common and relatively simple case studies ...

  12. Nonlinear analysis of the forced response of structural elements

    Science.gov (United States)

    Nayfeh, A. H.; Mook, D. T.; Sridhar, S.

    1974-01-01

    A general procedure is presented for the nonlinear analysis of the forced response of structural elements to harmonic excitations. Internal resonances (i.e., modal interactions) are taken into account. All excitations are considered, with special consideration given to resonant excitations. The general procedure is applied to clamped-hinged beams. The results reveal that exciting a higher mode may lead to a larger response in a lower interacting mode, contrary to the results of linear analyses.

  13. Intense DC beam nonlinear transport-analysis & simulation

    Institute of Scientific and Technical Information of China (English)

    L(U) Jian-Qin; ZHAO Xiao-Song

    2009-01-01

    The intense dc beam nonlinear transport was analyzed with the Lie algebraic method,and the particle trajectories of the second order approximation were obtained.Based on the theoretical analysis a computer code was designed.To get self-consistent solutions,iteration procedures were used in the code.As an example,we calculated a beam line(drift-electrostatic quadrupole doublet-drift).The results agree to the results calculated by using the PIC method.

  14. Linear Algebraic Method for Non-Linear Map Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Yu,L.; Nash, B.

    2009-05-04

    We present a newly developed method to analyze some non-linear dynamics problems such as the Henon map using a matrix analysis method from linear algebra. Choosing the Henon map as an example, we analyze the spectral structure, the tune-amplitude dependence, the variation of tune and amplitude during the particle motion, etc., using the method of Jordan decomposition which is widely used in conventional linear algebra.

  15. Singularity analysis of a new discrete nonlinear Schrodinger equation

    OpenAIRE

    Sakovich, Sergei

    2001-01-01

    We apply the Painleve test for integrability to a new discrete (differential-difference) nonlinear Schrodinger equation introduced by Leon and Manna. Since the singular expansions of solutions of this equation turn out to contain nondominant logarithmic terms, we conclude that the studied equation is nonintegrable. This result supports the observation of Levi and Yamilov that the Leon-Manna equation does not admit high-order generalized symmetries. As a byproduct of the singularity analysis c...

  16. Application of homotopy analysis method for solving nonlinear Cauchy problem

    Directory of Open Access Journals (Sweden)

    V.G. Gupta

    2012-11-01

    Full Text Available In this paper, by means of the homotopy analysis method (HAM, the solutions of some nonlinear Cauchy problem of parabolic-hyperbolic type are exactly obtained in the form of convergent Taylor series. The HAM contains the auxiliary parameter \\hbar that provides a convenient way of controlling the convergent region of series solutions. This analytical method is employed to solve linear examples to obtain the exact solutions. The results reveal that the proposed method is very effective and simple.

  17. Weakly nonlinear analysis and localised structures in nonlinear cavities with metamaterials

    Science.gov (United States)

    Slimani, N.; Makhoute, A.; Tlidi, M.

    2016-04-01

    We consider an optical ring cavity filled with a metamaterial and with a Kerr medium. The cavity is driven by a coherent radiation beam. The modelling of this device leads to the well known Lugiato-Lefever equation with high order diffraction term. We assume that both left-handed and right-handed materials possess a Kerr focusing type of nonlinearity. We show that close to the zero-diffraction regime, high-order diffraction effect allows us to stabilise dark localised structures in this device. These structures consist of dips or holes in the transverse profile of the intracavity field and do not exist without high-order diffraction effects. We show that high order diffraction effects alter in depth the space-time dynamics of this device. A weakly nonlinear analysis in the vicinity of the first threshold associated with the Turing instability is performed. This analysis allows us to determine the parameter regime where the transition from super- to sub-critical bifurcation occurs. When the modulational instability appears subcritically, we show that bright localised structures of light may be generated in two-dimensional setting. Close to the second threshold associated with the Turing instability, dark localised structures are generated.

  18. A machine learning approach to nonlinear modal analysis

    Science.gov (United States)

    Worden, K.; Green, P. L.

    2017-02-01

    Although linear modal analysis has proved itself to be the method of choice for the analysis of linear dynamic structures, its extension to nonlinear structures has proved to be a problem. A number of competing viewpoints on nonlinear modal analysis have emerged, each of which preserves a subset of the properties of the original linear theory. From the geometrical point of view, one can argue that the invariant manifold approach of Shaw and Pierre is the most natural generalisation. However, the Shaw-Pierre approach is rather demanding technically, depending as it does on the analytical construction of a mapping between spaces, which maps physical coordinates into invariant manifolds spanned by independent subsets of variables. The objective of the current paper is to demonstrate a data-based approach motivated by Shaw-Pierre method which exploits the idea of statistical independence to optimise a parametric form of the mapping. The approach can also be regarded as a generalisation of the Principal Orthogonal Decomposition (POD). A machine learning approach to inversion of the modal transformation is presented, based on the use of Gaussian processes, and this is equivalent to a nonlinear form of modal superposition. However, it is shown that issues can arise if the forward transformation is a polynomial and can thus have a multi-valued inverse. The overall approach is demonstrated using a number of case studies based on both simulated and experimental data.

  19. Nonlinear coupled dynamics analysis of a truss spar platform

    Science.gov (United States)

    Li, Cheng-xi; Zhang, Jun

    2016-12-01

    Accurate prediction of the offshore structure motion response and associate mooring line tension is important in both technical applications and scientific research. In our study, a truss spar platform, operated in Gulf of Mexico, is numerically simulated and analyzed by an in-house numerical code `COUPLE'. Both the platform motion responses and associated mooring line tension are calculated and investigated through a time domain nonlinear coupled dynamic analysis. Satisfactory agreement between the simulation and corresponding field measurements is in general reached, indicating that the numerical code can be used to conduct the time-domain analysis of a truss spar interacting with its mooring and riser system. Based on the comparison between linear and nonlinear results, the relative importance of nonlinearity in predicting the platform motion response and mooring line tensions is assessed and presented. Through the coupled and quasi-static analysis, the importance of the dynamic coupling effect between the platform hull and the mooring/riser system in predicting the mooring line tension and platform motions is quantified. These results may provide essential information pertaining to facilitate the numerical simulation and design of the large scale offshore structures.

  20. Pulsatile instability in rapid directional solidification - Strongly-nonlinear analysis

    Science.gov (United States)

    Merchant, G. J.; Braun, R. J.; Brattkus, K.; Davis, S. H.

    1992-01-01

    In the rapid directional solidification of a dilute binary alloy, analysis reveals that, in addition to the cellular mode of Mullins and Sekerka (1964), there is an oscillatory instability. For the model analyzed by Merchant and Davis (1990), the preferred wavenumber is zero; the mode is one of pulsation. Two strongly nonlinear analyses are performed that describe this pulsatile mode. In the first case, nonequilibrium effects that alter solute rejection at the interface are taken asymptotically small. A nonlinear oscillator equation governs the position of the solid-liquid interface at leading order, and amplitude and phase evolution equations are derived for the uniformly pulsating interface. The analysis provides a uniform description of both subcritical and supercritical bifurcation and the transition between the two. In the second case, nonequilibrium effects that alter solute rejection are taken asymptotically large, and a different nonlinear oscillator equation governs the location of the interface to leading order. A similar analysis allows for the derivation of an amplitude evolution equation for the uniformly pulsating interface. In this case, the bifurcation is always supercritical. The results are used to make predictions about the characteristics of solute bands that would be frozen into the solid.

  1. Elements of nonlinear time series analysis and forecasting

    CERN Document Server

    De Gooijer, Jan G

    2017-01-01

    This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

  2. QUANTITATIVE METHODOLOGY FOR STABILITY ANALYSIS OF NONLINEAR ROTOR SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    ZHENG Hui-ping; XUE Yu-sheng; CHEN Yu-shu

    2005-01-01

    Rotor-bearings systems applied widely in industry are nonlinear dynamic systems of multi-degree-of-freedom. Modem concepts on design and maintenance call for quantitative stability analysis. Using trajectory based stability-preserving and dimensional-reduction, a quanttative stability analysis method for rotor systems is presented. At first, an n-dimensional nonlinear non-autonomous rotor system is decoupled into n subsystems after numerical integration. Each of them has only onedegree-of-freedom and contains time-varying parameters to represent all other state variables. In this way, n-dimensional trajectory is mapped into a set of one-dimensional trajectories. Dynamic central point (DCP) of a subsystem is then defined on the extended phase plane, namely, force-position plane. Characteristics of curves on the extended phase plane and the DCP's kinetic energy difference sequence for general motion in rotor systems are studied. The corresponding stability margins of trajectory are evaluated quantitatively. By means of the margin and its sensitivity analysis, the critical parameters of the period doubling bifurcation and the Hopf bifurcation in a flexible rotor supported by two short journal beatings with nonlinear suspensionare are determined.

  3. Electroencephalogram (EEG) (For Parents)

    Science.gov (United States)

    ... Old Feeding Your 1- to 2-Year-Old EEG (Electroencephalogram) KidsHealth > For Parents > EEG (Electroencephalogram) A A A What's in this article? ... Child If You Have Questions en español Electroencefalograma (EEG) What It Is An electroencephalogram (EEG) is a ...

  4. Electroencephalogram (EEG) (For Parents)

    Science.gov (United States)

    ... Old Feeding Your 1- to 2-Year-Old EEG (Electroencephalogram) KidsHealth > For Parents > EEG (Electroencephalogram) Print A A A What's in this ... Child If You Have Questions en español Electroencefalograma (EEG) What It Is An electroencephalogram (EEG) is a ...

  5. 不同意识脑电的双谱切片特征分析与分类%The analysis and classification of EEG bispectrum slice in different mental states

    Institute of Scientific and Technical Information of China (English)

    刘海红; 杜英举; 姚伟华

    2015-01-01

    脑电是一种典型的非高斯、非线性随机信号,传统时域或频域分析已不能准确表征信号特征,而高阶谱方法对脑电信号的处理却有较好效果。本文通过几组不同意识状态下的脑电测试实验,提取脑电信号,并通过双谱分析提取脑电双谱切片的特征值,借助支持向量机、概率神经网络、最近邻分类算法等3种方法对双谱切片的特征值进行处理,比较其分类效果。研究结果表明,电极C3处脑电与其他电极处的脑电具有不同的双谱特征,不同脑电极信号双谱切片具有明显差异。%Electroencephalogram (EEG) is a very complex non-Gaussian, nonlinear stochastic signal, using the time domain or frequency domain analysis can not accurately characterize the characteristics, but the high-order spectrum analysis methods for the use of EEG signal processing research does have a good effect. In this paper, by doing several different groups of mental EEG experiment, we obtained the EEG bispectrum slice of every experimental data, extracted the characteristic values of the bispectrum slice, and used SVM, KNN, PNN classifications methods to process the spectral slice. Then the classification accuracy was obtained in different experimental conditions. The result shows that SVM classifier has the highest classification accuracy; compared C3 electrode with other 11 electrodes, the signals of different slice of brain electrode have different bispectral characteristics.

  6. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg

    2010-01-01

    Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...

  7. Electroencephalograph (EEG) signal processing method of motor imaginary potential for attention level classification.

    Science.gov (United States)

    Ming, Dong; Xi, Youyuan; Zhang, Mingming; Qi, Hongzhi; Cheng, Longlong; Wan, Baikun; Li, Liyong

    2009-01-01

    Research of visual attention is one of the important domains of psychology and neurophysiology. In this study, an attention related electroencephalograph (EEG) signal processing method was proposed to distinguish the different levels of people's attention during the imaginary limbs motor. There were two EEG feedback experiments (playing tennis and walking) to measure the different levels of visual attention. Three imaginary motor tasks (attention, inattention, and rest task) were performed with the flash stimulus displayed on the screen in the experiments. A nonlinear dynamics parameter of multi-scale entropy (MSE) was extracted from those EEG data recorded. According to the statistics analysis of 14 subjects, there was an obvious declining tendency of MSE with the level of attention declining, which validated the effectiveness of the proposed method to classify the visual attention level.

  8. Extracting the Neural Representation of Tone Onsets for Separate Voices of Ensemble Music Using Multivariate EEG Analysis

    DEFF Research Database (Denmark)

    Sturm, Irene; Treder, Matthias S.; Miklody, Daniel

    2015-01-01

    When listening to ensemble music even non-musicians can follow single instruments effortlessly. Electrophysiological indices for neural sensory encoding of separate streams have been described using oddball paradigms which utilize brain reactions to sound events that deviate from a repeating...... standard pattern. Obviously, these paradigms put constraints on the compositional complexity of the musical stimulus. Here, we apply a regression-based method of multivariate EEG analysis in order to reveal the neural encoding of separate voices of naturalistic ensemble music that is based on cortical...... responses to tone onsets, such as N1/P2 ERP components. Music clips (resembling minimalistic electro-pop) were presented to 11 subjects, either in an ensemble version (drums, bass, keyboard) or in the corresponding three solo versions. For each instrument we train a spatio-temporal regression filter...

  9. Detection and Separation of Event-related Potentials from Multi-Artifacts Contaminated EEG by Means of Independent Component Analysis

    Institute of Scientific and Technical Information of China (English)

    WANGRong-chang; DUSi-dan; GAODun-tang

    2004-01-01

    Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto--adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.

  10. Evaluating Acupuncture Point and Nonacupuncture Point Stimulation with EEG: A High-Frequency Power Spectrum Analysis

    Science.gov (United States)

    Choi, Kwang-Ho; Cho, Seong Jin; Kang, Suk-Yun; Ahn, Seong Hun

    2016-01-01

    To identify physical and sensory responses to acupuncture point stimulation (APS), nonacupuncture point stimulation (NAPS) and no stimulation (NS), changes in the high-frequency power spectrum before and after stimulation were evaluated with electroencephalography (EEG). A total of 37 healthy subjects received APS at the LI4 point, NAPS, or NS with their eyes closed. Background brain waves were measured before, during, and after stimulation using 8 channels. Changes in the power spectra of gamma waves and high beta waves before, during, and after stimulation were comparatively analyzed. After NAPS, absolute high beta power (AHBP), relative high beta power (RHBP), absolute gamma power (AGP), and relative gamma power (RGP) tended to increase in all channels. But no consistent notable changes were found for APS and NS. NAPS is believed to cause temporary reactions to stress, tension, and sensory responses of the human body, while APS responds stably compared to stimulation of other parts of the body.

  11. Evaluating Acupuncture Point and Nonacupuncture Point Stimulation with EEG: A High-Frequency Power Spectrum Analysis

    Directory of Open Access Journals (Sweden)

    Kwang-Ho Choi

    2016-01-01

    Full Text Available To identify physical and sensory responses to acupuncture point stimulation (APS, nonacupuncture point stimulation (NAPS and no stimulation (NS, changes in the high-frequency power spectrum before and after stimulation were evaluated with electroencephalography (EEG. A total of 37 healthy subjects received APS at the LI4 point, NAPS, or NS with their eyes closed. Background brain waves were measured before, during, and after stimulation using 8 channels. Changes in the power spectra of gamma waves and high beta waves before, during, and after stimulation were comparatively analyzed. After NAPS, absolute high beta power (AHBP, relative high beta power (RHBP, absolute gamma power (AGP, and relative gamma power (RGP tended to increase in all channels. But no consistent notable changes were found for APS and NS. NAPS is believed to cause temporary reactions to stress, tension, and sensory responses of the human body, while APS responds stably compared to stimulation of other parts of the body.

  12. Evaluating Acupuncture Point and Nonacupuncture Point Stimulation with EEG: A High-Frequency Power Spectrum Analysis.

    Science.gov (United States)

    Choi, Kwang-Ho; Kwon, O Sang; Cho, Seong Jin; Lee, Sanghun; Kang, Suk-Yun; Ahn, Seong Hun; Ryu, Yeonhee

    2016-01-01

    To identify physical and sensory responses to acupuncture point stimulation (APS), nonacupuncture point stimulation (NAPS) and no stimulation (NS), changes in the high-frequency power spectrum before and after stimulation were evaluated with electroencephalography (EEG). A total of 37 healthy subjects received APS at the LI4 point, NAPS, or NS with their eyes closed. Background brain waves were measured before, during, and after stimulation using 8 channels. Changes in the power spectra of gamma waves and high beta waves before, during, and after stimulation were comparatively analyzed. After NAPS, absolute high beta power (AHBP), relative high beta power (RHBP), absolute gamma power (AGP), and relative gamma power (RGP) tended to increase in all channels. But no consistent notable changes were found for APS and NS. NAPS is believed to cause temporary reactions to stress, tension, and sensory responses of the human body, while APS responds stably compared to stimulation of other parts of the body.

  13. High-Resolution Movement EEG Classification

    Directory of Open Access Journals (Sweden)

    Jakub Štastný

    2007-01-01

    Full Text Available The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%, but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

  14. EEG analysis of the brain activity during the observation of commercial, political, or public service announcements.

    Science.gov (United States)

    Vecchiato, Giovanni; Astolfi, Laura; Tabarrini, Alessandro; Salinari, Serenella; Mattia, Donatella; Cincotti, Febo; Bianchi, Luigi; Sorrentino, Domenica; Aloise, Fabio; Soranzo, Ramon; Babiloni, Fabio

    2010-01-01

    The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and "supporter" voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

  15. EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements

    Directory of Open Access Journals (Sweden)

    Giovanni Vecchiato

    2010-01-01

    Full Text Available The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs. In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and “supporter” voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

  16. Nonlinear flutter wind tunnel test and numerical analysis of folding fins with freeplay nonlinearities

    Directory of Open Access Journals (Sweden)

    Yang Ning

    2016-02-01

    Full Text Available The flutter characteristics of folding control fins with freeplay are investigated by numerical simulation and flutter wind tunnel tests. Based on the characteristics of the structures, fins with different freeplay angles are designed. For a 0° angle of attack, wind tunnel tests of these fins are conducted, and vibration is observed by accelerometers and a high-speed camera. By the expansion of the connected relationships, the governing equations of fit for the nonlinear aeroelastic analysis are established by the free-interface component mode synthesis method. Based on the results of the wind tunnel tests, the flutter characteristics of fins with different freeplay angles are analyzed. The results show that the vibration divergent speed is increased, and the divergent speed is higher than the flutter speed of the nominal linear system. The vibration divergent speed is increased along with an increase in the freeplay angle. The developed free-interface component mode synthesis method could be used to establish governing equations and to analyze the characteristics of nonlinear aeroelastic systems. The results of the numerical simulations and the wind tunnel tests indicate the same trends and critical velocities.

  17. Nonlinear flutter wind tunnel test and numerical analysis of folding fins with freeplay nonlinearities

    Institute of Scientific and Technical Information of China (English)

    Yang Ning; Wang Nan; Zhang Xin; Liu Wei

    2016-01-01

    The flutter characteristics of folding control fins with freeplay are investigated by numer-ical simulation and flutter wind tunnel tests. Based on the characteristics of the structures, fins with different freeplay angles are designed. For a 0? angle of attack, wind tunnel tests of these fins are conducted, and vibration is observed by accelerometers and a high-speed camera. By the expansion of the connected relationships, the governing equations of fit for the nonlinear aeroelastic analysis are established by the free-interface component mode synthesis method. Based on the results of the wind tunnel tests, the flutter characteristics of fins with different freeplay angles are analyzed. The results show that the vibration divergent speed is increased, and the divergent speed is higher than the flutter speed of the nominal linear system. The vibration divergent speed is increased along with an increase in the freeplay angle. The developed free-interface component mode synthesis method could be used to establish governing equations and to analyze the characteristics of nonlinear aeroe-lastic systems. The results of the numerical simulations and the wind tunnel tests indicate the same trends and critical velocities.

  18. Denoising and robust non-linear wavelet analysis

    Science.gov (United States)

    Bruce, Andrew G.; Donoho, David L.; Gao, Hong-Ye; Martin, R. D.

    1994-04-01

    In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistance wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transforms, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the S+Wavelets object-oriented toolkit for wavelet analysis.

  19. Nonlinear dynamic analysis of quasi-symmetric anisotropic structures

    Science.gov (United States)

    Noor, Ahmed K.; Peters, Jeanne M.

    1987-01-01

    An efficient computational method for the nonlinear dynamic analysis of quasi-symmetric anisotropic structures is proposed. The application of mixed models simplifies the analytical development and improves the accuracy of the response predictions, and operator splitting allows the reduction of the analysis model of the quasi-symmetric structure to that of the corresponding symmetric structure. The preconditoned conjugate gradient provides a stable and effective technique for generating the unsymmetric response of the structure as the sum of a symmetrized response plus correction modes. The effectiveness of the strategy is demonstrated with the example of a laminated anisotropic shallow shell of quadrilateral planform subjected to uniform normal loading.

  20. State-variable analysis of non-linear circuits with a desk computer

    Science.gov (United States)

    Cohen, E.

    1981-01-01

    State variable analysis was used to analyze the transient performance of non-linear circuits on a desk top computer. The non-linearities considered were not restricted to any circuit element. All that is required for analysis is the relationship defining each non-linearity be known in terms of points on a curve.

  1. A perturbation approach for geometrically nonlinear structural analysis using a general purpose finite element code

    NARCIS (Netherlands)

    Rahman, T.

    2009-01-01

    In this thesis, a finite element based perturbation approach is presented for geometrically nonlinear analysis of thin-walled structures. Geometrically nonlinear static and dynamic analyses are essential for this class of structures. Nowadays nonlinear analysis of thin-walled shell structures is oft

  2. Dynamics of EEG Entropy: beyond signal plus noise

    CERN Document Server

    Ignaccolo, M; Jernajczyk, W; Grigolini, P; West, B J

    2009-01-01

    EEG time series are analyzed using the diffusion entropy method. The resulting EEG entropy manifests short-time scaling, asymptotic saturation and an attenuated alpha-rhythm modulation. These properties are faithfully modeled by a phenomenological Langevin equation interpreted within a neural network context. Detrended fluctuation analysis of the EEG data is compared with diffusion entropy analysis and is found to suppress certain important properties of the EEG time series.

  3. Analysis and modeling of time-variant amplitude-frequency couplings of and between oscillations of EEG bursts.

    Science.gov (United States)

    Witte, Herbert; Putsche, Peter; Hemmelmann, Claudia; Schelenz, Christoph; Leistritz, Lutz

    2008-08-01

    Low-frequency (0.5-2.5 Hz) and individually defined high-frequency (7-11 or 8-12 Hz; 11-15 or 14-18 Hz) oscillatory components of the electroencephalogram (EEG) burst activity derived from thiopental-induced burst-suppression patterns (BSP) were investigated in seven sedated patients (17-26 years old) with severe head injury. The predominant high-frequency burst oscillations (>7 Hz) were detected for each patient by means of time-variant amplitude spectrum analysis. Thereafter, the instantaneous envelope (IE) and the instantaneous frequency (IF) were computed for these low- and high-frequency bands to quantify amplitude-frequency dependencies (envelope-envelope, envelope-frequency, and frequency-frequency correlations). Time-variant phase-locking, phase synchronization, and quadratic phase couplings are associated with the observed amplitude-frequency characteristics. Additionally, these time-variant analyses were carried out for modeled burst patterns. Coupled Duffing oscillators were adapted to each EEG burst and by means of these models data-based burst simulations were generated. Results are: (1) strong envelope-envelope correlations (IE courses) can be demonstrated; (2) it can be shown that a rise of the IE is associated with an increase of the IF (only for the frequency bands 0.5-2.5 and 7-11 or 8-12 Hz); (3) the rise characteristics of all individually averaged envelope-frequency courses (IE-IF) are strongly correlated; (4) for the 7-11 or 8-12 Hz oscillation these associations are weaker and the variation between the time courses of the patients is higher; (5) for both frequency ranges a quantitative amplitude-frequency dependency can be shown because higher IE peak maxima are accompanied by stronger IF changes; (6) the time range of significant phase-locking within the 7-11 or 8-12 Hz frequency bands and of the strongest quadratic phase couplings (between 0.5-2.5 and 7-11 or 8-12 Hz) is between 0 and 1,000 ms; (7) all phase coupling characteristics of the

  4. Nonlinear Aerodynamics-Structure Time Simulation for HALE Aircraft Design/Analysis Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Time simulation of a nonlinear aerodynamics model (NA) developed at Virginia Tech coupled with a nonlinear structure model (NS) is proposed as a design/analysis...

  5. Analysis of Pleasant and Unpleasant Emotion Based on EEG Signal%基于脑电(EEG)信号的“愉快-不愉快”情绪分析

    Institute of Scientific and Technical Information of China (English)

    王亚卿; 曹艳珺; 樊浩宇; 郭明磊; 宋秋霞

    2012-01-01

    脑电是目前研究脑科学的重要方法之一.情绪变化可以导致脑电信号的差异,本文运用小波分析以及EEGLAB工具箱对“愉快-不愉快”这两种情绪状态下的脑电(EEG)信号进行差异化分析,然后定量地找出不同情绪状态下的信号差别,最终实现人体情绪的可视化.该研究的方法和分析结果为研究老师、学生、海员等工作或学习压力比较大的职业群体的情绪变化提供了较好的参考价值.%EEC is currently one of the most important methods to study brain signal. The mood changes also will cause brain electrical signal differences, this paper uses wavelet analysis and EECLAB toolbox (mostly Independent Component Analysis) to analyse pleasant and unpleasant these two kinds of mood state of EEG signal, then finds qualitatively the differences in these two kinds of signals, thereby realizing visualization of human emotions. The analysis has greater value for teachers, students and seamen whose emotions are more likely to be influenced because of heavy stress.

  6. Quantitative analysis for nonlinear fluorescent spectra based on edges matching

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A novel spectra-edge-matching approach is proposed for the quantitative analysis of the nonlinear fluorescence spectra of the air impurities excited by a femtosecond laser.The fluorescence spectra are first denoised and compressed,both by wavelet transform,and several peak groups are then picked from each spectrum according to a threshold of intensity and are used to extract the spectral features through principal component analysis.It is indicated that the first two principle components actually cover up to 98% of the total information and are sufficient for the final concentration analysis.The analysis reveals a monotone relationship between the spectra intensity and the concentration of the air impurities,suggesting that the femtosecond laser induced fluorescence spectroscopy along with the proposed spectra analysis method can become a powerful tool for monitoring environmental pollutants.

  7. Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy

    Science.gov (United States)

    Gao, Lin; Wang, Jue; Chen, Longwei

    2013-06-01

    Objective. Various approaches have been applied for the quantification of event-related desynchronization/synchronization (ERD/ERS) in EEG/MEG data analysis, but most of them are based on band power analysis. In this paper, we sought a novel method using a nonlinear measurement to quantify the ERD/ERS time course of motor-related EEG. Approach. We applied Kolmogorov entropy to quantify the ERD/ERS time course of motor-related EEG in relation to hand movement imagination and execution for the first time. To further test the validity of the Kolmogorov entropy measure, we tested it on five human subjects for feature extraction to classify the left and right hand motor tasks. Main results. The results show that the relative increase and decrease of Kolmogorov entropy indicates the ERD and ERS respectively. An average classification accuracy of 87.3% was obtained for five subjects. Significance. The results prove that Kolmogorov entropy can effectively quantify the dynamic process of event-related EEG, and it also provides a novel method of classifying motor imagery tasks from scalp EEG by Kolmogorov entropy measurement with promising classification accuracy.

  8. Nonlinear mathematical modeling and sensitivity analysis of hydraulic drive unit

    Science.gov (United States)

    Kong, Xiangdong; Yu, Bin; Quan, Lingxiao; Ba, Kaixian; Wu, Liujie

    2015-09-01

    The previous sensitivity analysis researches are not accurate enough and also have the limited reference value, because those mathematical models are relatively simple and the change of the load and the initial displacement changes of the piston are ignored, even experiment verification is not conducted. Therefore, in view of deficiencies above, a nonlinear mathematical model is established in this paper, including dynamic characteristics of servo valve, nonlinear characteristics of pressure-flow, initial displacement of servo cylinder piston and friction nonlinearity. The transfer function block diagram is built for the hydraulic drive unit closed loop position control, as well as the state equations. Through deriving the time-varying coefficient items matrix and time-varying free items matrix of sensitivity equations respectively, the expression of sensitivity equations based on the nonlinear mathematical model are obtained. According to structure parameters of hydraulic drive unit, working parameters, fluid transmission characteristics and measured friction-velocity curves, the simulation analysis of hydraulic drive unit is completed on the MATLAB/Simulink simulation platform with the displacement step 2 mm, 5 mm and 10 mm, respectively. The simulation results indicate that the developed nonlinear mathematical model is sufficient by comparing the characteristic curves of experimental step response and simulation step response under different constant load. Then, the sensitivity function time-history curves of seventeen parameters are obtained, basing on each state vector time-history curve of step response characteristic. The maximum value of displacement variation percentage and the sum of displacement variation absolute values in the sampling time are both taken as sensitivity indexes. The sensitivity indexes values above are calculated and shown visually in histograms under different working conditions, and change rules are analyzed. Then the sensitivity

  9. Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology

    OpenAIRE

    Fingelkurts, Al. A; Fingelkurts, An. A

    2010-01-01

    Spectral decomposition, to this day, still remains the main analytical paradigm for the analysis of EEG oscillations. However, conventional spectral analysis assesses the mean characteristics of the EEG power spectra averaged out over extended periods of time and/or broad frequency bands, thus resulting in a “static” picture which cannot reflect adequately the underlying neurodynamic. A relatively new promising area in the study of EEG is based on reducing the signal to elementary short-term ...

  10. Nonlinear rotordynamics analysis. [Space Shuttle Main Engine turbopumps

    Science.gov (United States)

    Noah, Sherif T.

    1991-01-01

    Effective analysis tools were developed for predicting the nonlinear rotordynamic behavior of the Space Shuttle Main Engine (SSME) turbopumps under steady and transient operating conditions. Using these methods, preliminary parametric studies were conducted on both generic and actual HPOTP (high pressure oxygen turbopump) models. In particular, a novel modified harmonic balance/alternating Fourier transform (HB/AFT) method was developed and used to conduct a preliminary study of the effects of fluid, bearing and seal forces on the unbalanced response of a multi-disk rotor in the presence of bearing clearances. The method makes it possible to determine periodic, sub-, super-synchronous and chaotic responses of a rotor system. The method also yields information about the stability of the obtained response, thus allowing bifurcation analyses. This provides a more effective capability for predicting the response under transient conditions by searching in proximity of resonance peaks. Preliminary results were also obtained for the nonlinear transient response of an actual HPOTP model using an efficient, newly developed numerical method based on convolution integration. Currently, the HB/AFT is being extended for determining the aperiodic response of nonlinear systems. Initial results show the method to be promising.

  11. 癫(癎)性痉挛发作的头皮及颅内脑电图特点%Epileptic Spasms: scalp EEG and intracranial EEG analysis

    Institute of Scientific and Technical Information of China (English)

    周文静; 石岩芳; 王东明; 刘晓燕; 张光明; 韩宏彦; 田宏; 林久銮; 孙朝晖

    2011-01-01

    Objective:To analyze the changes of scalp and intracranial EEG at ictal in patients with medically refractory epileptic spasms and to assess the changes of ictal discharges associated with spasms and their relation to interictal epileptiform activity and neuroimaging. Methods: Eighty-two seizures from 11 patients, aged 1~19 years were analyzed with intraoperative electrocorticography (EcoG). Ictal events were also analyzed with intracranial EEG from 4 patients, who were being performed for cortical resection. Results: Patients with EcoG monitoring, apart from diffuse sporadic spikes, displayed with continuous or frequent rhythmic epileptogenic discharges (CED) recorded from cortex with MRI lesion.For patients with chronic intracranial EEG monitoring, the spasms were associated with either a "leading" spike followed by fast-wave bursts or fast-wave bursts without a "leading" spike. Resection of the associated cortex achieved good surgical outcome. Fast-wave bursts were associated with spasms. Conclusion: Epileptic spasms may be triggered by a focal neocortical impulse in a subset of patients, and a leading spike might be used as a marker of the trigger zone for epileptic spasms. Rapidly emerging widespread fast-wave bursts might explain the clinical semiology of epileptic spasms. Completeness of excision of cortical tissue displaying CED is possitively corrlated with surgical outcome.%目的:研究难治性癫(癎)性痉挛发作患者的头皮及颅内脑电图(EEG)特点,探讨与痉挛发作相关的EEG变化及其与发作间期放电、神经影像学之间的关系.方法:回顾性分析经外科手术治疗的11例患者的临床资料,分析头皮同步视频脑电图(V-EEG).此11例患者均行术中皮层EEG监测30-60min,其中4例术前行颅内电极长程EEG监测.结果:8例患者表现为双侧基本对称的痉挛发作,发作期头皮EEG为全导高波幅慢波、尖波伴低波幅快波活动或广泛低波幅快波活动发放;另3例患者表

  12. Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market

    Directory of Open Access Journals (Sweden)

    Junhai Ma

    2008-01-01

    Full Text Available This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.

  13. Analysis of Grey Matter in Thalamus and Basal Ganglia Based on EEG α3/α2 Frequency Ratio Reveals Specific Changes in Subjects with Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Davide V Moretti

    2012-11-01

    Full Text Available GM (grey matter changes of thalamus and basal ganglia have been demonstrated to be involved in AD (Alzheimer's disease. Moreover, the increase of a specific EEG (electroencephalogram marker, α3/α2, have been associated with AD-converters subjects with MCI (mild cognitive impairment. To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to detect biomarkers (morpho-physiological early predictive of AD and non-AD dementia. Seventy-four adult subjects with MCI underwent EEG recording and high-resolution 3D MRI (three-dimensional magnetic resonance imaging. The α3/α2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of α3/α2 ratio. GM density differences between groups were investigated using a VBM (voxel-based morphometry technique. Subjects with higher α3/α2 ratios when compared with subjects with lower and middle α3/α2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally and of the pulvinar nuclei in the thalamus; The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.

  14. Stability Analysis of Some Nonlinear Anaerobic Digestion Models

    Directory of Open Access Journals (Sweden)

    Ivan Simeonov

    2010-04-01

    Full Text Available Abstract: The paper deals with local asymptotic stability analysis of some mass balance dynamic models (based on one and on two-stage reaction schemes of the anaerobic digestion (AD in CSTR. The equilibrium states for models based on one (with Monod, Contois and Haldane shapes for the specific growth rate and on two-stage (only with Monod shapes for both the specific growth rate of acidogenic and methanogenic bacterial populations reaction schemes have been determined solving sets of nonlinear algebraic equations using Maples. Their stability has been analyzed systematically, which provides insight and guidance for AD bioreactors design, operation and control.

  15. Method of guiding functions in problems of nonlinear analysis

    CERN Document Server

    Obukhovskii, Valeri; Van Loi, Nguyen; Kornev, Sergei

    2013-01-01

    This book offers a self-contained introduction to the theory of guiding functions methods, which can be used to study the existence of periodic solutions and their bifurcations in ordinary differential equations, differential inclusions and in control theory. It starts with the basic concepts of nonlinear and multivalued analysis, describes the classical aspects of the method of guiding functions, and then presents recent findings only available in the research literature. It describes essential applications in control theory, the theory of bifurcations, and physics, making it a valuable resource not only for “pure” mathematicians, but also for students and researchers working in applied mathematics, the engineering sciences and physics.

  16. CAD—Oriented Noise Analysis Method of Nonlinear Microwave Chircuits

    Institute of Scientific and Technical Information of China (English)

    WANGJun; TANGGaodi; CHENHuilian

    2003-01-01

    A general method is introduced which is capable of making accurate,quantitative predictions about the noise of different type of nonlinear microwave circuits.This new approach also elucidates several design criteria for making it suitable to CAD-oriented analysis via identifying the mechanisms by which intrinsic device noise and external noise sources contribute to the total equivalent noise.In particular,it explains the details of how noise spectrum at the interesting port is obtained.And the theory also naturally leads to additional important design insights.In the illustrative experiments,excellent agreement among theory,simulations,and measurements is observed.

  17. Nonlinear analysis of imperfect squarely-reticulated shallow spherical shells

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Nonlinear behavior of single-layer squarely-reticulated shallow spherical shells with geometrical imperfections subjected to a central concentrated (joint) load has been studied in this paper. Using the asymptotic iteration method, an analytical characteristic relationship between the non-dimensional load and central deflection is obtained. The resulting asymptotic solution can be used readily to perform the analysis of parameters and predict the buckling critical load. Meanwhile, numerical examples are presented and effects of imperfection factor and boundary conditions on buckling of the structures are discussed. Comparisons with data based on the finite element method show good exactness of the resulting solution.

  18. Nonlinear Principal Component Analysis Using Strong Tracking Filter

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

  19. A General Nonlinear Optimization Algorithm for Lower Bound Limit Analysis

    DEFF Research Database (Denmark)

    Krabbenhøft, Kristian; Damkilde, Lars

    2003-01-01

    The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...

  20. Nonlinear analysis of imperfect squarely- reticulated shallow spherical shells

    Institute of Scientific and Technical Information of China (English)

    NIE GuoHua; LI ZhiWei

    2007-01-01

    Nonlinear behavior of single-layer squarely-reticulated shallow spherical shells with geometrical imperfections subjected to a central concentrated (joint) load has been studied in this paper.Using the asymptotic iteration method,an analytical characteristic relationship between the non-dimensional load and central deflection is obtained.The resulting asymptotic solution can be used readily to perform the analysis of parameters and predict the buckling critical load.Meanwhile,numerical examples are presented and effects of imperfection factor and boundary conditions on buckling of the structures are discussed.Comparisons with data based on the finite element method show good exactness of the resulting solution.