EEG Signal classification by using Empirical Mode Decomposition and LVQ (original) (raw)
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IEEE Sensors Journal, 2015
In spite of recent advances, the interest in extracting knowledge hidden in the electroencephalogram(EEG) signals is rapidly growing, as well as their application in the computational neuro engineering field such as mobile robot control, wheelchair control and person identification using brainwaves. The large number of methods for EEG feature extraction demands a good feature for every task. Digging up the most unique feature would be worth for identification of individual using EEG signal. This research presents a novel approach for feature extraction of electroencephalogram (EEG) signal using the Empirical mode decomposition (EMD) and information-theoretic-method (ITM). The EMD technique is applied to decompose an EEG signal into set of intrinsic mode function (IMF). These decomposed signals are of the same length and in the same time domain as the Original Signal. Hence, EMD method preserves varying frequencies in time. To measure the performance of the features, we have used Hybrid learning for classification where we have selected Learning Vector Quantization Neural Network (LVQ-NN) with fuzzy algorithm. Furthermore, to investigate the performance and accuracy of each subject over the different cognitive tasks based on Cohen's kappa coefficient. The results are compared with past methods in literature for feature extraction and classification methods. Results confirm that proposed features present a satisfactory performance.
Detection of epileptic seizures by the analysis of EEG signals using empirical mode decomposition
The detection of epileptic seizure has a primary role in patient diagnosis with epilepsy. Epilepsy causes sudden and uncontrolled electrical discharges in brain cells. Recordings of the abnormal brain activities are time consuming and outcomes are very subjective, so automated detection systems are highly recommended. In this study, it is aimed to classify EEG signals for the detection of epileptic seizures using intrinsic mode functions (IMF) and feature extraction based on Empirical Mode Decomposition (EMD). These records have been acquired from the database of the Epileptology Department of Bonn University and consisting of 5 marker groups A, B, C, D, E in this study. These records taken from healthy individuals and patients are decomposed into IMFs by EMD method. Feature vectors have been extracted based on Tsallis Entropy, Renyi Entropy, Relative Entropy and Coherence methods. These features are then classified by K-Nearest Neighbors Classification (KNN), Linear Discriminant Analysis (LDA) and Naive Bayes Classification (NBC). Significant differences were determined between healthy and patient EEG data at the end of the study.
International Journal of Neural Systems, 2012
Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.
Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures
Advances in Neural Signal Processing, 2020
Empirical mode decomposition (EMD) is a remarkable method for the analysis of nonlinear and non-stationary data. EMD will breakdown the given signal into intrinsic mode functions (IMFs), which can represent natural signals effectively. In this work, the competence of EMD with traditional features to classify the seizure and non-seizure EEG signals is studied. Due to the complex nature of human brain, the EEG signals which are recorded from different regions of brain are nonstationary in nature. Different features such as entropy features (approximate entropy (ApEn), sample entropy (SmEn), Shannon entropy (ShEn), Rényi entropy (RnEn)), fractal dimension features (Petrosian fractal dimension, Higuchi fractal dimension, Katz fractal dimension), statistical features (mean, standard deviation and energy) and exponential energy features are extracted from IMFs and fed to a SVM classifier. The performances of extracted features are studied independently. The result shows that, the EMD method is well suited for complex seizure EEG signal classification.
Quantization-Based Novel Extraction Method Of EEG Signal For Classification
2020
In the pattern recognition field, features or object’s characteristics are one of the key points to recognizing them. The feature extraction process will see that objects have different features, where the features are obtained through the analysis process from the extractor, such as for data statistics, energy, power spectral, and so on. This study aims to enrich the point of view of EEG signal features by quantifying the signal. It will be analyzed whether the features obtained by quantization represent the EEG signal object from different viewpoints. This research uses the DEAP dataset, with the result being a feature vector that will be included in the artificial neural network classifier using the Keras library. The experiment carried out is to try to enter quantized and Non-quantized feature vectors into the classifier. As a result, the accuracy of the classification process with the quantization vector was 75%, and the accuracy in the Nonquantized vector classification proces...
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015
This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of non-stationary signals. The intrinsic mode functions (IMF) obtained as a result of EMD give the decomposition of a signal according to its frequency components. We present the usage of upto third order temporal moments, and spectral features including spectral centroid, coefficient of variation and the spectral skew of the IMFs for feature extraction from EEG signals. These features are physiologically relevant given that the normal EEG signals have different temporal and spectral centroids, dispersions and symmetries when compared with the pathological EEG signals. The calculated features are fed into the standard support vector machine (SVM) for classification purposes. The performance of the proposed method is studied on a publicly available dataset which is designed to handle various classification problems including the identification of epilepsy patients and detection of seizures. Experiments show that good classification results are obtained using the proposed methodology for the classification of EEG signals. Our proposed method also compares favorably to other state-of-the-art feature extraction methods.
Features Extraction Method for Brain-Machine Communication Based on the Empirical Mode Decomposition
Biomedical Engineering: Applications, Basis and Communications, 2013
A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classi¯cation on di®erent mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing¯ve mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, LempelÀZiv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classi¯cation of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classi¯cation over all subjects in database is 91 AE 5% and 87 AE 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classi¯cation of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.
An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.
Journal of University of Human Development
The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy...
Alzheimer's Disease Detection using Empirical Mode Decomposition and Hjorth parameters of EEG signal
Alzheimer's disease (AD) is a progressive neurodegenerative disorder observed in the elderly. AD diagnosis is performed through interviews or questionnaires by an experienced psychiatrist. This process is time-consuming, biased, and subject-specific. Hence, its urgent need to develop an. The paper presents an automatic AD detection system using Electroencephalogram (EEG) signal to alleviate these problems and support neurologists. Nine IMFs (Intrinsic mode functions) are generated for each EEG signal using empirical mode analysis. Ten different features are extracted from these IMFs. Three Hjorth parameters (activity, mobility, complexity) are selected using the Kruskal-Wallis test. The selected features from EEG recordings of 23 subjects (AD-12 and NC-11) are evaluated using the least-square support vector machine (LS-SVM) model with 10-fold cross-validation for three kernels. A maximum of 92.90% classification accuracy is obtained using the features of IMF-4. The results showed that the proposed method detected AD patients efficiently. Further, the proposed method can be used to detect other neurological disorders.