Download or read book Models of EEG Data Mining and Classification in Temporal Lobe Epilepsy Wavelet chaos neural Network Methodology and Spiking Neural Networks written by Samanwoy Ghosh Dastidar and published by . This book was released on 2007 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG-based epilepsy diagnosis and seizure detection is still in its early experimental stages. In this research, a multi-paradigm approach is advocated, integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks.
Download or read book Brain Computer Interfacing for Assistive Robotics written by Vaibhav Gandhi and published by Academic Press. This book was released on 2014-09-24 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been possible due to advances in multi-disciplinary areas of research related to cognitive neuroscience, brain-imaging techniques and human-computer interfaces. However, two major challenges remain in making BCI for assistive robotics practical for day-to-day use: the inherent lower bandwidth of BCI, and how to best handle the unknown embedded noise within the raw EEG. Brain-Computer Interfacing for Assistive Robotics is a result of research focusing on these important aspects of BCI for real-time assistive robotic application. It details the fundamental issues related to non-stationary EEG signal processing (filtering) and the need of an alternative approach for the same. Additionally, the book also discusses techniques for overcoming lower bandwidth of BCIs by designing novel use-centric graphical user interfaces. A detailed investigation into both these approaches is discussed. An innovative reference on the brain-computer interface (BCI) and its utility in computational neuroscience and assistive robotics Written for mature and early stage researchers, postgraduate and doctoral students, and computational neuroscientists, this book is a novel guide to the fundamentals of quantum mechanics for BCI Full-colour text that focuses on brain-computer interfacing for real-time assistive robotic application and details the fundamental issues related with signal processing and the need for alternative approaches A detailed introduction as well as an in-depth analysis of challenges and issues in developing practical brain-computer interfaces.
Download or read book EEG Brain Signal Classification for Epileptic Seizure Disorder Detection written by Sandeep Kumar Satapathy and published by Academic Press. This book was released on 2019-02-10 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated
Download or read book Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals written by Harikumar Rajaguru and published by Anchor Academic Publishing. This book was released on 2017-03 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes’ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.
Download or read book EEG Signal Analysis and Classification written by Siuly Siuly and published by Springer. This book was released on 2017-01-03 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div
Download or read book Evolving Probabilistic Spiking Neural Networks written by Nuttapod Nuntalid and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Electroencephalography (EEG) in Brain Computer Interface (BCI) domain presents a challenging problem due to presence of spatial and temporal aspects inherent in the EEG data. Many studies either transform the data into a temporal or spatial problem for analysis. This approach results in loss of significant information since these methods fail to consider the correlation present within the spatial and temporal aspect of the EEG data. However, Spiking Neural Network (SNN) naturally takes into consideration the correlation present within the spatio-temporal data. Hence by applying the proposed SNN based novel methods on EEG, the thesis provide improved analytic on EEG data. This book introduces novel methods and architectures for spatio-temporal data modelling and classification using SNN. More specifically, SNN is used for analysis and classification of spatiotemporal EEG data.
Download or read book Decoding EEG Brain Signals using Recurrent Neural Networks written by Juri Fedjaev and published by GRIN Verlag. This book was released on 2019-01-14 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2017 in the subject Electrotechnology, grade: 1,0, Technical University of Munich (Neurowissenschaftliche Systemtheorie), language: English, abstract: Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable direct communication between humans and computers by analyzing brain activity. Specifically, modern BCIs are capable of translating imagined movements into real-life control signals, e.g., to actuate a robotic arm or prosthesis. This type of BCI is already used in rehabilitation robotics and provides an alternative communication channel for patients suffering from amyotrophic lateral sclerosis or severe spinal cord injury. Current state-of-the-art methods are based on traditional machine learning, which involves the identification of discriminative features. This is a challenging task due to the non-linear, non-stationary and time-varying characteristics of EEG signals, which led to stagnating progress in classification performance. Deep learning alleviates the efforts for manual feature engineering through end-to-end decoding, which potentially presents a promising solution for EEG signal classification. This thesis investigates how deep learning models such as long short-term memory (LSTM) and convolutional neural networks (CNN) perform on the task of decoding motor imagery movements from EEG signals. For this task, both a LSTM and a CNN model are developed using the latest advances in deep learning, such as batch normalization, dropout and cropped training strategies for data augmentation. Evaluation is performed on a novel EEG dataset consisting of 20 healthy subjects. The LSTM model reaches the state-of-the-art performance of support vector ma- chines with a cross-validated accuracy of 66.20%. The CNN model that employs a time-frequency transformation in its first layer outperforms the LSTM model and reaches a mean accuracy of 84.23%. This shows that deep learning approaches deliver competitive performance without the need for hand-crafted features, enabling end-to-end classification.
Download or read book Automated EEG Based Diagnosis of Neurological Disorders written by Hojjat Adeli and published by CRC Press. This book was released on 2010-02-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the authors’ groundbreaking research, Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis of neurological disorders. It is based on the ingenious integration of three different computing technologies and problem-solving paradigms: neural networks, wavelets, and chaos theory. The book also includes three introductory chapters that familiarize readers with these three distinct paradigms. After extensive research and the discovery of relevant mathematical markers, the authors present a methodology for epilepsy diagnosis and seizure detection that offers an exceptional accuracy rate of 96 percent. They examine technology that has the potential to impact and transform neurology practice in a significant way. They also include some preliminary results towards EEG-based diagnosis of Alzheimer’s disease. The methodology presented in the book is especially versatile and can be adapted and applied for the diagnosis of other brain disorders. The senior author is currently extending the new technology to diagnosis of ADHD and autism. A second contribution made by the book is its presentation and advancement of Spiking Neural Networks as the seminal foundation of a more realistic and plausible third generation neural network.
Download or read book Automated Deep Neural Network Approach for Detection of Epileptic Seizures written by Nadia Moazen and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, I focus on exploiting electroencephalography (EEG) signals for early seizure diagnosis in patients. This process is based on a powerful deep learning algorithm for times series data called Long Short-Term Memory (LSTM) network. Since manual and visual inspection (detection) of epileptic seizure through the electroencephalography (EEG) signal by expert neurologists is time-consuming, work-intensive and error-prone and it might take a couple hours for experts to analyze a single patient record and to do recognition when immediate action is needed to be taken. This thesis proposes a reliable automatic seizure/non-seizure classification method that could facilitate the identification process of characteristic epileptic patterns, such as pre-ictal spikes, seizures and determination of seizure frequency, seizure type, etc. In order to recognize epileptic seizure accurately, the proposed model exploits the temporal dependencies in the EEG data. Experiments on clinical data present that this method achieves a high seizure prediction accuracy and maintains reliable performance. This thesis also finds the most efficient lengths of EEG recording for highest accuracies of different classification in the automated seizure detection realm. It could help non-experts to predict the seizure more comprehensively and bring awareness to patients and caregivers of upcoming seizures, enhancing the daily lives of patients against unpredictable occurrence of seizures.
Download or read book EEG Signal Processing and Machine Learning written by Saeid Sanei and published by John Wiley & Sons. This book was released on 2021-09-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG Signal Processing and Machine Learning Explore cutting edge techniques at the forefront of electroencephalogram research and artificial intelligence from leading voices in the field The newly revised Second Edition of EEG Signal Processing and Machine Learning delivers an inclusive and thorough exploration of new techniques and outcomes in electroencephalogram (EEG) research in the areas of analysis, processing, and decision making about a variety of brain states, abnormalities, and disorders using advanced signal processing and machine learning techniques. The book content is substantially increased upon that of the first edition and, while it retains what made the first edition so popular, is composed of more than 50% new material. The distinguished authors have included new material on tensors for EEG analysis and sensor fusion, as well as new chapters on mental fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and psychiatric abnormalities. In addition to including a comprehensive chapter on machine learning, machine learning applications have been added to almost all the chapters. Moreover, multimodal brain screening, such as EEG-fMRI, and brain connectivity have been included as two new chapters in this new edition. Readers will also benefit from the inclusion of: A thorough introduction to EEGs, including neural activities, action potentials, EEG generation, brain rhythms, and EEG recording and measurement An exploration of brain waves, including their generation, recording, and instrumentation, abnormal EEG patterns and the effects of ageing and mental disorders A treatment of mathematical models for normal and abnormal EEGs Discussions of the fundamentals of EEG signal processing, including statistical properties, linear and nonlinear systems, frequency domain approaches, tensor factorization, diffusion adaptive filtering, deep neural networks, and complex-valued signal processing Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, students and researchers in the above areas, the Second Edition of EEG Signal Processing and Machine Learning will also earn a place in the libraries of undergraduate and postgraduate students studying Biomedical Engineering, Neuroscience and Epileptology.
Download or read book EEG Signal Processing written by Saeid Sanei and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.
Download or read book DEEP ARCHITECTURES FOR SPATIO TEMPORAL SEQUENCE RECOGNITION WITH APPLICATIONS IN AUTOMATIC SEIZURE DETECTION written by Meysam Golmohammadi and published by . This book was released on 2021 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scalp electroencephalograms (EEGs) are used in a broad range of health care institutions to monitor and record electrical activity in the brain. EEGs are essential in diagnosis of clinical conditions such as epilepsy, seizure, coma, encephalopathy, and brain death. Manual scanning and interpretation of EEGs is time-consuming since these recordings may last hours or days. It is also an expensive process as it requires highly trained experts. Therefore, high performance automated analysis of EEGs can reduce time to diagnosis and enhance real-time applications by identifying sections of the signal that need further review.Automatic analysis of clinical EEGs is a very difficult machine learning problem due to the low fidelity of a scalp EEG signal. Commercially available automated seizure detection systems suffer from unacceptably high false alarm rates. Many signal processing methods have been developed over the years including time-frequency processing, wavelet analysis and autoregressive spectral analysis. Though there has been significant progress in machine learning technology in recent years, use of automated technology in clinical settings is limited, mainly due to unacceptably high false alarm rates. Further, state of the art machine learning algorithms that employ high dimensional models have not previously been utilized in EEG analysis because there has been a lack of large databases that accurately characterize clinical operating conditions. Deep learning approaches can be viewed as a broad family of neural network algorithms that use many layers of nonlinear processing units to learn a mapping between inputs and outputs. Deep learning-based systems have generated significant improvements in performance for sequence recognitions tasks for temporal signals such as speech and for image analysis applications that can exploit spatial correlations, and for which large amounts of training data exists. The primary goal of our proposed research is to develop deep learning-based architectures that capture spatial and temporal correlations in an EEG signal. We apply these architectures to the problem of automated seizure detection for adult EEGs. The main contribution of this work is the development of a high-performance automated EEG analysis system based on principles of machine learning and big data that approaches levels of performance required for clinical acceptance of the technology. In this work, we explore a combination of deep learning-based architectures. First, we present a hybrid architecture that integrates hidden Markov models (HMMs) for sequential decoding of EEG events with a deep learning-based postprocessing that incorporates temporal and spatial context. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: spike and/or sharp waves, generalized periodic epileptiform discharges and periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: eye movement, artifacts, and background. Our approach delivers a sensitivity above 90% while maintaining a specificity above 95%. Next, we replace the HMM component of the system with a deep learning architecture that exploits spatial and temporal context. We study how effectively these architectures can model context. We introduce several architectures including a novel hybrid system that integrates convolutional neural networks with recurrent neural networks to model both spatial relationships (e.g., cross-channel dependencies) and temporal dynamics (e.g., spikes). We also propose a topology-preserving architecture for spatio-temporal sequence recognition that uses raw data directly rather than low-level features. We show this model learns representations directly from raw EEGs data and does not need to use predefined features. In this study, we use the Temple University EEG (TUEG) Corpus, supplemented with data from Duke University and Emory University, to evaluate the performance of these hybrid deep structures. We demonstrate that performance of a system trained only on Temple University Seizure Corpus (TUSZ) data transfers to a blind evaluation set consisting of the Duke University Seizure Corpus (DUSZ) and the Emory University Seizure Corpus (EUSZ). This type of generalization is very important since complex high-dimensional deep learning systems tend to overtrain. We also investigate the robustness of this system to mismatched conditions (e.g., train on TUSZ, evaluate on EUSZ). We train a model on one of three available datasets and evaluate the trained model on the other two datasets. These datasets are recorded from different hospitals, using a variety of devices and electrodes, under different circumstances and annotated by different neurologists and experts. Therefore, these experiments help us to evaluate the impact of the dataset on our training process and validate our manual annotation process. Further, we introduce methods to improve generalization and robustness. We analyze performance to gain additional insight into what aspects of the signal are being modeled adequately and where the models fail. The best results for automatic seizure detection achieved in this study are 45.59% with 12.24 FA per 24 hours on TUSZ, 45.91% with 11.86 FAs on DUSZ, and 62.56% with 11.26 FAs on EUSZ. We demonstrate that the performance of the deep recurrent convolutional structure presented in this study is statistically comparable to the human performance on the same dataset.
Download or read book Wavelet Neural Networks for EEG Modeling and Clasification written by Javier R. Echauz and published by . This book was released on 1995 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Atlas of EEG Seizure Semiology and Management written by Karl Edward Misulis and published by Oxford University Press. This book was released on 2022 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Electroencephalography (EEG) is an invaluable tool for evaluating patients with suspected seizures or encephalopathy, yet EEG is only one source of data, so information from this technology must be integrated with knowledge of basic science and clinical neurology. This work has a principal focus on EEG, but interleaves that discussion with information on seizures, epilepsy, encephalopathy, and other neurologic conditions for which EEG can be a useful diagnostic tool"--
Download or read book Removing Artefacts and Periodically Retraining Improve Performance of Neural Network based Seizure Prediction Models written by Fabio Lopes and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models
Download or read book Analysis and Pattern Identification in EEG Signals Using Artificial Neural Networks written by and published by . This book was released on 2017 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the years of research, EEG signal study has grown to give promising outcomes. A lot of research has been done on implementing brain-computer interfaces, detecting seizure through EEG signal abnormalities and so on. A stimulus is given to trigger the EEG signals, the resulting responses are recorded, analyzed and inferences are drawn. Brain signal analysis is an important aspect in understanding signal properties by extracting useful information that describes the signal. Using these extracted features, the EEG signals can be categorized based on the patterns, abnormalities or uniqueness that they may reflect. EEG signal classification finds great use in clinical applications and prosthetics. This study aims at delivering an efficient algorithm to classify EEG signals using signal analysis methods and machine learning techniques. An EEG signal is first simulated and tested with multiple scenarios to generate different types of patterns in them. Signal preprocessing and feature extraction techniques are applied to the signal. Specifically, the strength of the signal is computed using root mean square (RMS) methods and classified using neural network models. Accordingly, a new index called R-index is introduced to increase classification accuracy. This series of processes is then tested upon human data to validate the robustness of the proposed algorithm. EEG signals are collected using state-of-the-art signal acquisition system, g.Nautilus. In this study, voiced sounds have been used as auditory stimuli. Various pitches and patterns have been incorporated in the stimuli to generate different EEG patterns for analysis. Temporal and prefrontal regions of the brain have been targeted, thereby using FP1, FP2, T7 and T8 channels of the EEG signal acquisition system. These are analyzed in a fashion, similar to the simulated signals. The acquired data was analyzed using RMS analysis methods and hierarchical clustering analysis (HCA) techniques. Statistical significance of the data was strengthened using Analysis of Variance (ANOVA) approach. In a nutshell, the objective of this study was to formulate an efficient algorithm using different techniques to identify different patterns in EEG signals using artificial neural networks.
Download or read book A New Approach for Diagnosing Epilepsy by Using Wavelet Transform and Neural Networks written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, epilepsy keeps its importance as a major brain disorder. However, although some devices such as magnetic resonance (MR), brain tomography (BT) are used to diagnose the structural disorders of brain, for observing some special illnesses especially such as epilepsy, EEG is routinely used for observing the epileptic seizures, in neurology clinics. In our study, we aimed to classify the EEG signals and diagnose the epileptic seizures directly by using wavelet transform and an artificial neural network model. EEG signals are separated into delta, theta, alpha, and beta spectral components by using wavelet transform. These spectral components are applied to the inputs of the neural network. Then, neural network is trained to give three outputs to signify the health situation of the patients.
