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 Wavelet Neural Networks for EEG Modeling and Classification written by Javier Ramon 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 Wavelets in Neuroscience written by Alexander E. Hramov and published by Springer. This book was released on 2014-08-05 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade. Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics. Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural networks (chapter 4). The features of time-frequency organization of EEG signals are then extensively discussed, from theory to practical applications (chapters 5 and 6). Lastly, the technical details of automatic diagnostics and processing of EEG signals using wavelets are examined (chapter 7). The book will be a useful resource for neurophysiologists and physicists familiar with nonlinear dynamical systems and data processing, as well as for graduat e students specializing in the corresponding areas.
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 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 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 EEG Signal Processing and Machine Learning written by Saeid Sanei and published by John Wiley & Sons. This book was released on 2021-09-27 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 Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing written by Rajesh Kumar Tripathy and published by CRC Press. This book was released on 2024-06-06 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book: Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.
Download or read book Analysis and Classification of EEG Signals for Brain computer Interfaces Data acquisition methods for human brain activity written by Szczepan Paszkiel and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain-computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore-Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology. In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain-computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain-computer technology and virtual reality technology.
Download or read book Wavelet Neural Network Algorithms and Architectures written by E. Ribes Gomez and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Multidisciplinary Approaches to Neural Computing written by Anna Esposito and published by Springer. This book was released on 2017-08-28 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of contributions in the field of Artificial Neural Networks (ANNs). The themes addressed are multidisciplinary in nature, and closely connected in their ultimate aim to identify features from dynamic realistic signal exchanges and invariant machine representations that can be exploited to improve the quality of life of their end users. Mathematical tools like ANNs are currently exploited in many scientific domains because of their solid theoretical background and effectiveness in providing solutions to many demanding tasks such as appropriately processing (both for extracting features and recognizing) mono- and bi-dimensional dynamic signals, solving strong nonlinearities in the data and providing general solutions for deep and fully connected architectures. Given the multidisciplinary nature of their use and the interdisciplinary characterization of the problems they are applied to – which range from medicine to psychology, industrial and social robotics, computer vision, and signal processing (among many others) – ANNs may provide a basis for redefining the concept of information processing. These reflections are supported by theoretical models and applications presented in the chapters of this book. This book is of primary importance for: (a) the academic research community, (b) the ICT market, (c) PhD students and early-stage researchers, (d) schools, hospitals, rehabilitation and assisted-living centers, and (e) representatives of multimedia industries and standardization bodies.
Download or read book Design and Analysis for Wavelet Neural Network Identification Model written by Loi Leung Yeung and published by . This book was released on 2005 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Application of Neural Networks for the Classification of EEG Data written by Bert Klöppel and published by . This book was released on 1992 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Wavelet Neural Networks in Nonlinear System Modelling and Motor Drives written by Yujie Song (Ph. D.) and published by . This book was released on 2005 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
