Optimal Metrics Investigation for Human Functional States Differences Assessment Based on EEG Signal Analysis

Mental state detection is the need of today's age due to increase in cases of mental disorders. Emotion describes the current mental state of the human being. The development of Emotion based Non Invasive Electroencephalogram brain-computer interface will be useful to analyze brain activity and to read hidden brains of people in need that most of us take for granted. The behavior of Electroencephalogram EEG signal is categorized in linear, nonlinear, stationary and non stationary. Behavioral analysis of the EEG signal is necessary to understand complex brain activity. The focus of this paper is the Initial analysis of brain EEG signal for mental state detection of human being. This paper presents initial analysis of EEG signal, databases and emotion classification system for the development of Intelligent Emotion Recognition System.

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.

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 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.

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.

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.

Because of problems with therecording and analysis of the EEG signal, automatic sleep staging using cardiorespiratory signals has been employed as an alternative. This study reports on certain critical points which hold considerable promise for the improvement of the results of the automatic sleep staging using cardiorespiratory signals. A systematic review. The review and analysis of the literature in this area revealed four outstanding points: (1) the feature extraction epoch length, denoting that the standard 30-s segments of cardiorespiratory signals do not carry enough information for automatic sleep staging and that a 4.5-min length segment centering on each 30-s segment is proper for staging, (2) the time delay between the EEG signal extracted from the central nervous system activity and the cardiorespiratory signals extracted from theautonomic nervous system activity should be considered in the automatic sleep staging using cardiorespiratory signals, (3) the information in the morphology of ECG signals can contribute to the improvement of sleep staging, and (4) applying convolutional neural network (CNN) and long short-term memory network (LSTM) deep structures simultaneously to a large PSG recording database can lead to more reliable automatic sleep staging results. Considering the above-mentioned points simultaneously can improve automatic sleep staging by cardiorespiratory signals. It is hoped that by considering the points, staging sleep automatically using cardiorespiratory signals, which does not have problems with the recording and analysis of EEG signals, yields results acceptably close to the results of automatic sleep staging by EEG signals.

This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.

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.

Automated change-point detection of EEG signals is becoming essential for the monitoring of health behaviors and health status in a wide range of clinical applications. This paper presents a structural time-series analysis to capture and characterize the dynamic behavior of EEG signals, and develops a method to detect the EEG change points. For a given EEG signal, the proposed method is operated as follows: 1) a sub-band pass filter is fist designed to capture those frequency components that can characterize the dynamic behavior of the data, and the so-called power spectrum is extracted as the EEG features; 2) together with a sliding-window technique, an automatic `segment-to-segment' analysis of EEG signal, is developed with a null hypothesis testing for decision making. In particular, the main challenge of the proposed method is to design an appropriate distance metric that is compatible with our considered data/problem. To achieve this end, we first collect a variety of metrics from other areas that would be potentially available for our problem, and then compare them for the considered EEG change point detection. Experiments are conducted on two different data sets. Results show the Bhattacharyya distance achieves the best detection result among all investigated metrics. Meanwhile, comparison with state-of-the-arts demonstrates the effectiveness of the method in real applications.

A technique for detecting brain injury at the bedside has great clinical value, but conventional imaging techniques (such as computed tomography [CT] and magnetic resonance imaging) are impractical. In this study, a novel method–the symmetrical channel electroencephalogram (EEG) signal analysis–was developed for this purpose. The study population consisted of 45 traumatic brain injury patients and 10 healthy controls. EEG signals in resting and stimulus states were acquired, and approximate entropy (ApEn) and slow-wave coefficient were extracted to calculate the ratio values of ApEn and SWC for injured and uninjured areas. Statistical analyses showed that the ratio values for both ApEn and SWC between injured and uninjured brain areas differed significantly (P < 0.05) for both resting and name call stimulus states. A set of criteria (range of ratio values) to determine whether a brain area is injured or uninjured was proposed and its reliability was verified by statistical analyses and CT images.

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..