Initial analysis of brain EEG signal for mental state detection of human being

With the pace of modern lifestyle, about 40–50 million people in the world suffer from epilepsy—a disease with neurological disorder. Electroencephalography (EEG) is the process of recording brain signals that generate due to a small amount of electric discharge in brain. This may occur due to the information flow among several neurons. Therefore, in every minute, analysis of EEG signal can solve much neurological disorders like epilepsy. In this paper, a systematic procedure for analysis and classification of EEG signal is discussed for identification of epilepsy in a human brain. The analysis of EEG signal is made through a series of steps from feature extraction to classification. Feature extraction from EEG signal is done through discrete wavelet transform (DWT), and the classification task is carried out by MLPNN based on supervised training algorithms such as backpropagation, resilient propagation (RPROP), and Manhattan update rule. Experimental study in a Java platform confirms that RPROP trained MLPNN to classify EEG signal is promising as compared to back-propagation or Manhattan update rule trained MLPNN.

Depression is a common phenomenon in the present scenario. Due to the fast pace at which our lives move and immense pressure that we face adolescents, office goers and even the elders face depression. Diagnosing depression in the early curable stages is very important and may even save the life of a patient. EEG signal analysis has been used for medical research like epilepsy, sleep disorder, insomnia etc. Similarly, video signal analysis has been used for facial features detection, eye movement, emotion recognition etc. Collaborating both the methods accuracy of depression detection can be improved upon. This paper describes a novel method for combining both EEG signal analysis and facial emotion recognition through video analysis to successfully categorize depression into various levels. For this aim, power spectrum of three frequency bands (alpha, beta, and theta) and the whole bands of EEG are used as features along with standard deviation, mean and entropy.

In order to investigate the nonlinear relations of the electroencephalogram (EEG) signals under different brain functional states, higher-order statistics is used to study the nonlinear interrelation of the EEG components for the purpose of further understanding of the EEG generation and its construction. A parametric bispectral estimation for the analysis of EEG signals has been presented as an useful tool for detecting the nonlinearity of EEG signals. The bicoherence pattern is proposed in the paper to extract more. information beyond first and second-order statistics or spectral structure. Several EEG signals with normal subjects in different brain functional states are investigated by employing the non-Gaussian parametric model. The experimental results demonstrate that practical EEG signals provide obvious quadratic nonlinear coupling phenomena. The bicoherence structures of EEG signals is also different from that corresponding to the brain functional states. It is suggest that the bispectral analysis can be used as an effective way for nonlinear analysis and automatic classification of EEG signals and other biomedical measurements.

This paper covers the initial research and analysis of the EEG signal for the purpose of designing a neural interface for identification of the mental state. Such neural interface can be beneficial in various fields of automation and industry and can also potentially serve as a safety feature for safety critical processes. In the first section of this paper we discuss the performed experiment and also the technical means for the EEG data acquisition. In following chapter, we are describing the data itself and we are also performing the basic data analysis as well as the correlation identification. Final part of this paper we are evaluating our hypothesis to finding correlations in the dataset.

Background: Drivers drowsiness is one of the prime reasons for road accidents. Electroencephalogram (EEG) signals provide crucial information regarding drowsy state due to neurological changes in the brain. But the complex nature of EEG signals makes it difficult to study these changes. A detailed analysis of the EEG signal can be done if it is decomposed into multi-modes. Method: In this paper, adaptive variational mode decomposition (AVMD) is used for accurate analysis and synthesis of EEG signals. The number of modes (J) and quadratic penalty factor (α) is selected adaptively to find out representative information from EEG signals. Selection of J and α is done by minimizing the reconstruction error using the Jaya optimization algorithm. Features are extracted from the adaptively decomposed modes. Entropy-based features selected by statistical analysis are classified with different classification algorithms. Eight performance parameters are evaluated to test the system's effectiveness. Results: The reconstruction error of 4.035 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-09</sup> and 1.564 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-09</sup> for the alert and drowsy state shows that the proposed method gives a better synthesis of signals. An accuracy, sensitivity, specificity, F-1 score, Kappa, false-positive rate, error, and precision of 97.19%, 97.01%, 97.46%, 0.976, 94.23%, 2.54%, 2.81%, and 98.18% shows that the proposed method provides representative modes for analysis. Conclusion: The comparison shows that AVMD is superior over conventional and existing methods by about 7% and 1%, respectively. The solution provided in this paper takes a step ahead for efficient synthesis and analysis of EEG signals to detect the drowsy state of drivers.

This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods for classification. We begin with comparing performance between 'black-box' generative and discriminative approaches. In order to take potential advantage of the temporal nature of the EEG, we use two temporal models: the standard generative hidden Markov model, and the discriminative input-output hidden Markov model. For this latter model, we introduce a novel 'apposite' training algorithm which is of particular benefit for the type of training sequences that we use. We also asses the advantage of using these temporal probabilistic models compared with their static alternatives. We then investigate the incorporation of more specific prior information about the physical nature of EEG signals into the model structure. In particular, a common successful assumption in EEG research is that signals are generated by a linear mixing of independent sources in the brain and other external components. Such domain knowledge is conveniently introduced by using a generative model, and leads to a generative form of Independent Components Analysis (gICA). We analyze whether or not this approach is advantageous in terms of performance compared to a more standard discriminative approach, which uses domain knowledge by extracting relevant features which are subsequently fed into classifiers. The user of a BCI system may have more than one way to perform a particular mental task. Furthermore, the physiological and psychological conditions may change from one recording session and/or day to another. As a consequence, the corresponding EEG signals may change significantly. As a first attempt to deal with this effect, we use a mixture of gICA in which the EEG signal is split into different regimes, each regime corresponding to a potentially different realization of the same mental task. An arguable limitation of the gICA model is the fact that the temporal nature of the EEG signal is not taken into account. Therefore, we analyze an extension in which each hidden component is modeled with an autoregressive process. The second part of the thesis focuses on analyzing the EEG signal and, in particular, on extracting independent dynamical processes from multiple channels. In BCI research, such a decomposition technique can be applied, for example, to denoise EEG signals from artifacts and to analyze the source generators in the brain, thereby aiding the visualization and interpretation of the mental state. In order to do this, we introduce a specially constrained form of the linear Gaussian state-space model which satisfies several properties, such as flexibility in the specification of the number of recovered independent processes and the possibility to obtain processes in particular frequency ranges. We then discuss an extension of this model to the case in which we don't know a priori the correct number of hidden processes which have generated the observed time-series and the prior knowledge about their frequency content is not precise. This is achieved using an approximate variational Bayesian analysis. The resulting model can automatically determine the number and appropriate complexity of the underlying dynamics, with a preference for the simplest solution, and estimates processes with preferential spectral properties. An important contribution from our work is a novel 'sequential' algorithm for performing smoothed inference, which is numerically stable and simpler than others previously published.

The recent science and technology studies in neuroscience and machine learning have focused attention on investigating the functioning of the brain through nonlinear analysis. The brain is a nonlinear dynamic system, imparting randomness and nonlinearity in the EEG signals. The stochastic nature of the brain seeks the paramount importance of understanding the underlying neurophysiology. The nonlinear analysis of the dynamic structure may help to reveal the complex behavior of the brain signals. EEG signal analysis is helpful in various clinical applications to characterize the normal and diseased brain states. The EEG is used in predicting epileptic seizures, classifying the sleep stages, measuring the depth of anesthesia, and detecting the abnormal brain states. With the onset of EEG-based brain-computer interfaces, the characteristics of brain signals are used to control the devices through different mental states. Hence, the need to understand the brain state is important and crucial. In this chapter, the author introduces the theory and methods of chaos theory measurements and its applications in EEG signal analysis. A broad perspective of the techniques and implementation of the Correlation Dimension, Lyapunov Exponents, Fractal Dimension, Approximate Entropy, Sample Entropy, Hurst Exponent, Lempel-Ziv complexity, Hopf Bifurcation Theorem and Higher-order spectra is explained and their usage in EEG signal analysis is mentioned. We suggest that chaos theory provides not only potentially valuable diagnostic information but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

This paper mainly studies the EEG signals, eye movement signals, ECG signal analysis under natural human-computer interaction and the fusion of multiple information sources to generate decision signals to judge whether the driver is fatigued or not. It is mainly used to automatically monitor the driver's mental state in real time while the vehicle is running. Alerts when the driver just shows signs of fatigue. Forced deceleration or even forced parking in the event of a dangerous situation. The analysis of EEG signals mainly includes EEG signal acquisition, feature extraction and classification. The analysis of eye movement signals mainly includes face detection, human eye positioning, pupil and eye angle detection, etc. Integration and implementation mainly include the integration of multiple information sources, decision signal transmission, and fatigue driving monitoring system design.

Notice of Violation of IEEE Publication Principles<br><br>"Online Epilepsy Diagnosis Based on Analysis of EEG Signals by Hybrid Adaptive Filtering and Higher-order Crossings"<br>by Saadat Nasehi, Hossein Pourghassem, and Afshine Etesami<br>in the 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br>This paper contains portions of text from the paper(s) cited below. A reference is included, but due to the absence of quotation marks or offset text, copied material is not clearly credited or specifically identified.<br><br>"Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis"<br>by Panagiotis C. Petrantonakis and Leontios J. Hadjileontiadis<br> in IEEE Transactions on Affective Computing, Vol 1, No 2, July-December 2010, pp. 81-97<br><br> <br/> This paper presents a novel epilepsy diagnosis algorithm based on analysis of EEG signals by hybrid adaptive filtering (HAF) and higher-order crossings (HOC). In this algorithm, HAF is developed to isolate the seizure and non-seizure EEG characteristics and facilitating the task of the feature vector extraction. Furthermore, HOC analysis is employed to select the effective feature from the HAF-filtered signals. The extracted features by HAF-HOC scheme can create maximum distinction between two classes. Finally, Quadratic Discriminant Analysis (QDA) and Mahalanobis Distance (MD) is used for classification and recognition of seizures through EEG signals. The proposed algorithm is implemented on CHB dataset and its performance has been evaluated for three measures. The results indicate that the algorithm can recognize the seizure with smaller delay and higher good detection rate that are important factors from a clinical viewpoint.

In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy.

Electroencephalogram (EEG) signals are widely used in neuroscience and clinical medicine, which are non-invasive, real-time and low-cost. However, their complexity and noise interference limit their application in brain stripe recognition, emotion recognition, motor imagination, and epilepsy detection. This paper aims to explore the application of EEG signals in different fields, analyzing the role of deep learning in EEG signal classification and recognition tasks, so as to propose the challenges faced by current technologies and future development. Through the literature review, the definition and analysis of EEG signals are summarized, and its applications in brain striate recognition, emotion recognition, motor imagination and epilepsy detection are discussed in detail. Meanwhile, this paper analyzes the contributions to improving classification accuracy, feature extraction and model generalization ability. Finally, the research status of four sub-fields is discussed, and their research prospects are prospected. With the technology advancement and in-depth interdisciplinary cooperation, EEG signal processing technology will make greater contributions to human health and scientific and technological development.

&lt;p&gt;In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.&lt;/p&gt;

In this paper, a method based on the nolinear Granger causality is used to analyze epilptic EEG and ECG signal.Polynomial kernel function, Gaussian kernel function and sigmoid kernel function are used to map the linear data in low dimensional input space into high dimensional feature space .In this space linear Granger method can be used to analyse the biomedical signals.The results show that the effect of ECG signals to EEG signals is more significant than that of EEG signals to ECG signals and the result by normal subjects is more significant than that of epileptic subjects.This study is helpful for the analysis of epileptic patient's EEG and ECG signal..

Electroencephalography (EEG) is one of the field in diagnosing g epilepsy. Analysis of the EEG records can provide valuable insight and improve understanding of the mechanisms causing epileptic disorders. In this paper, the fast Fourier transform (FFT) and wavelet transform are used as spectral analysis tools of the EEG signals. These methods are chosen because they provide time–frequency shifted on the EEG signals. Since the frequency characteristics are important information that can be observed from the signals, FFT and wavelet transform are among a the best methods in analysis of EEG signals. The comparisons between these two methods are also carried out. Result showed that the wavelet transform is better than FFT in analysis of EEG signals. A software for analysing EEG signal is also developed using C++ programming. The software is able to compute and show the results of the analysis signal data by both of the two methods in graphical form.

Electroencephalography is non-invasive tool used in monitoring brain activities and diagnosis of many neurological disorders. EEG is a measurement tool to measure electrical activity generated by translating chemical variation in brain into voltage. EEG signals are measured with multi-electrode placed at properly localized part of the brain with either intracranial or Scalp EEG method. EEG (Electroencephalograph) analysis has become very important to detect various human diseases. The most important and simple aspect in processing EEG signals is to use time frequency analysis for possible diagnosis. Advanced methods of spectral analysis can extract new information encompassed in EEG signals by means of specific parameters. The EEG analysis plays very important role in feature extraction of EEG signal for detecting and predicting various brain diseases. The main objective of this paper is to study time-frequency analysis of EEG signal and compare their performances.