Nonlinear analysis of EEG signals at different mental states

The Electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Chaotic measures like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H) and entropy are used to characterize the signal. Results indicate that these nonlinear measures are good discriminators of normal and epileptic EEG signals. These measures distinguish epileptic EEG and alcoholic from normal EEG with an accuracy of more than 90%. The dynamical behavior is less random for alcoholic and epileptic compared to normal. This indicates less of information processing in the brain due to the hyper-synchronization of the EEG. Hence, the application of nonlinear time series analysis to EEG signals offers insight into the dynamical nature and variability of the brain signals.

Nonlinear Biomedical Physics, 2009

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak et al. [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusion: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.

Complexity, 2002

In this article, nonlinear dynamical tools such as largest Lyapunov exponents (LE), fractal dimension, correlation dimension, pointwise correlation dimension will be used to analyze electroencephalogram (EEG) data obtained from healthy young subjects with eyes open and eyes closed condition with the view to compare brain complexity under this two condition. Results of similar calculations from some earlier works will be produced for comparison with present results. Also, a brief report on difference of opinion among coworkers regarding such tools will be reported; particularly applicability of LE will be reviewed. The issue of nonlinearity will be addressed by using surrogate data technique. We have extracted another data set that represented chaotic state of the system considered in our earlier work of mathematical modeling of artificial neural network. We further attempt to compare results to find nature of chaos arising from such theoretical models.

Music has been proven to be a valuable a 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.

Physica A: Statistical Mechanics and its Applications

We apply a nonlinear prediction algorithm to investigate the presence of nonlinear structure in electroencephalogram (EEG) recordings. The EEG signal could be modeled as a realization of a nonlinear model plus a residual noise (uncorrelated Gaussian noise). Using linear and nonlinear models we analyze the statistical nature of these residual noises in the case of epileptic patients and normal subjects. We found that the residual noise presents Gaussian distribution for epileptic patients if a nonlinear model is used whereas in the case of normal subjects the residual noise will exhibit a Gaussian distribution only if a linear model (autoregressive) is used. These results provide another evidence of the nonlinear character of the epileptic seizure recordings, while the normal EEG seems to be better described as linearly correlated noise.

2015

Electroencephalogram (EEG) signals carry information about the dynamics of the brain. A nonlinear method development is of great significance objective or goal because of the brain signals are nonlinear. Epilepsy is a neurological disorder which can be seen all over the world. It can be diagnosed by brain’s electrical activity. The determination of epileptic attacks or seizures by EEG signals is quite common in both clinical and research fields. During epileptic seizures, brain dynamics that make up the graph consists of abnormalities in EEG signals. Therefore, both time and frequency bands of the signals as well as need to go to review and determination of the pattern. Preictal and ictal EEG epochs were evaluated by wavelet-entropy and artificial neural networks (ANN) methods in this study. One hour EEG signals from different patients were used for wavelet-entropy method. One-hour epileptic EEG signals divided into two states (preictal and ictal) for this study. Then preictal end i...

Procedia Computer Science, 2015

Epilepsy is a critical brain disorder which can be detected through the signals captured from the brain. Electroencephalography is an efficient method used to capture signals from the brain. K nearest neighbor is one of the simplest methods used for classi fying epilepsy patterns. Classification of the epilepsy signal from normal pattern will be primarily based on features extracted from brain signals. This paper discusses statistical based linear feature extraction methods such as Root Mean Square, Variance and Linear Prediction Coefficient. This paper also focuses influence of decision rules such as consensus and majority rule in the classification of epilepsy data set. Results show better classification with respect to increased k value.

Journal of Medical Systems, 2010

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

2012

In this paper, we examined methods for analysing EEG (Electroencephalogram) during music listening and the relationship between EEG and music cognition. We especially focused on non-linear signal analysis for EEGs. We calculated correlation dimension (CD) and Lyapunov exponents (LE) signals of interest for two healthy subjects. Thus, we try to obtain the experimental results from the beginnings works about the possibility of medical treatment by music.

Acta Neurologica Scandinavica, 2009

Spectral analysis methods are useful for the evaluation of EEG signals. Nevertheless, they refer only to the frequency domain and ignore any potentially interesting phase information. Analytical methods based upon the theory of nonlinear dynamics provides this and additional information. We used both methods to evaluate the EEG signals of volunteers performing two distinct mental arithmetic tasks. We extracted the power spectrum, the coherence and nonlinear parameters (dimension, the first Lyapunov exponent, the Kolmogorov entropy, the mutual dimension and the dimensions based upon spatial embedding of the original data as well as their surrogates). We found that 1) the spatial embedding dimension differed from that of the surrogates, indicating nonlinearity, 2) there were differences between the two arithmetic tasks, and 3) the spectral and nonlinear methods differ in terms of the information they provide. Our results indicate that nonlinear analysis methods can be useful despite the fact that they are still at an early stage of development.