Eeg Signal Analysis And Classification
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Eeg Signal Analysis And Classification
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Author : Siuly Siuly
language : en
Publisher: Springer
Release Date : 2017-01-03
Eeg Signal Analysis And Classification written by Siuly Siuly and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-03 with Technology & Engineering categories.
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
Eeg Signal Analysis And Classification
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Author : Jamil Raza
language : en
Publisher:
Release Date : 2000
Eeg Signal Analysis And Classification written by Jamil Raza and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
Analysis And Classification Of Eeg Signals For Brain Computer Interfaces Data Acquisition Methods For Human Brain Activity
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Author : Szczepan Paszkiel
language : en
Publisher:
Release Date : 2020
Analysis And Classification Of Eeg Signals For Brain Computer Interfaces Data Acquisition Methods For Human Brain Activity written by Szczepan Paszkiel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Brain-computer interfaces categories.
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.
Analysis And Classification Of Eeg Signals For Brain Computer Interfaces
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Author : Szczepan Paszkiel
language : en
Publisher: Springer Nature
Release Date : 2019-08-31
Analysis And Classification Of Eeg Signals For Brain Computer Interfaces written by Szczepan Paszkiel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-31 with Technology & Engineering categories.
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.
Analysis And Classification Of Electroencephalography Signals
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Author :
language : en
Publisher:
Release Date :
Analysis And Classification Of Electroencephalography Signals written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into healthy and pathological cases is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from a publicly available source. Three types of cases were classified viz. signals recorded from healthy volunteers having their eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures. The feature extraction was done by computing the discrete wavelet transform and spectral analysis using AR model. The wavelet transform coefficients compress the number of data points into few features. Various statistics were used to further reduce the dimensionality. The AR coefficients obtained from burg auto-regressive method provide important features of the EEG signals. Classification of the EEG data using committee neural network provides robust and improved performance over individual members of the committee. F-ratio based dimension reduction technique was used to reduce the number of features without affecting the accuracy much.
Brain Computer Interface
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Author : Narayan Panigrahi
language : en
Publisher: CRC Press
Release Date : 2022-07-29
Brain Computer Interface written by Narayan Panigrahi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with Medical categories.
Brain Computer Interface: EEG Signal Processing discusses electroencephalogram (EEG) signal processing using effective methodology and algorithms. This book provides a basic introduction to EEG and a classification of different components present in EEG. It also helps the reader to understand the scope of processing EEG signals and their associated applications. Further, it covers specific aspects such as epilepsy detection; exploitation of P300 for various applications; design of an EEG acquisition system; and detection of saccade, fix, and blink from EEG and EOG data. Key Features: Explains the basis of brain computer interface and how it can be established using different EEG signal characteristics Covers the detailed classification of different types of EEG signals with respect to their physical characteristics Explains detection and diagnosis of epileptic seizures from the EEG data of a subject Reviews the design and development of a low-cost and robust EEG acquisition system Provides mathematical analysis of EEGs, including MATLAB® codes for students to experiment with EEG data This book is aimed at graduate students and researchers in biomedical, electrical, electronics, communication engineering, healthcare, and cyber physical systems.
Eeg Signal Processing
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Author : Saeid Sanei
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28
Eeg Signal Processing written by Saeid Sanei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Science categories.
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.
A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis
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Author : Kaushik Bhimraj
language : en
Publisher:
Release Date : 2017
A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis written by Kaushik Bhimraj and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic dissertations categories.
Author's abstract: Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user's neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement as recorded brain signals can be unreliable and vary in pattern throughout time. In the initial work, a novel classifier structure is proposed to classify different types of imaginary motions (left hand, right hand, and imagination of words starting with the same letter) across multiple sessions using an optimized set of electrodes for each user. The proposed technique uses raw brain signals obtained utilizing 32 electrodes and classifies the imaginary motions using Artificial Neural Networks (ANN). To enhance the classification rate and optimize the set of electrodes of each subject, a majority voting system combining a set of simple ANNs is used. This electrode optimization technique achieved classification accuracies of 69.83%, 94.04% and 84.56% respectively for the three subjects considered in this work. In the second work, the signal variations are studied in detail for a large EEG dataset. Using the Independent Component Analysis (ICA) with a dynamic threshold model, noise features were filtered. The data was classified to a high precision of more than 94% using artificial neural networks. A decreased variance in classification validated both, the effectiveness of the proposed dynamic threshold systems and the presence of higher concentrations of noise in data for specific subjects. Using this variance and classification accuracy, subjects were separated into two groups. The lower accuracy group was found to have an increased variance in classification. To confirm these results, a Kaiser windowing technique was used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR was obtained for all EEG signals pertaining to the group with the poor data classification. This work not only establishes a direct relationship between high signal variance, low SNR, and poor signal classification but also presents classification results that are significantly higher than the accuracies reported by prior studies for the same EEG user dataset.
Eeg Brain Signal Classification For Epileptic Seizure Disorder Detection
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Author : Sandeep Kumar Satapathy
language : en
Publisher: Academic Press
Release Date : 2019-02-10
Eeg Brain Signal Classification For Epileptic Seizure Disorder Detection written by Sandeep Kumar Satapathy and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-10 with Medical categories.
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
Brain Source Localization Using Eeg Signal Analysis
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Author : Munsif Ali Jatoi
language : en
Publisher: CRC Press
Release Date : 2017-12-14
Brain Source Localization Using Eeg Signal Analysis written by Munsif Ali Jatoi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-14 with Science categories.
Of the research areas devoted to biomedical sciences, the study of the brain remains a field that continually attracts interest due to the vast range of people afflicted with debilitating brain disorders and those interested in ameliorating its effects. To discover the roots of maladies and grasp the dynamics of brain functions, researchers and practitioners often turn to a process known as brain source localization, which assists in determining the source of electromagnetic signals from the brain. Aiming to promote both treatments and understanding of brain ailments, ranging from epilepsy and depression to schizophrenia and Parkinson’s disease, the authors of this book provide a comprehensive account of current developments in the use of neuroimaging techniques for brain analysis. Their book addresses a wide array of topics, including EEG forward and inverse problems, the application of classical MNE, LORETA, Bayesian based MSP, and its modified version, M-MSP. Within the ten chapters that comprise this book, clinicians, researchers, and field experts concerned with the state of brain source localization will find a store of information that can assist them in the quest to enhance the quality of life for people living with brain disorders.