Computational Eeg Analysis
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Computational Eeg Analysis
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Author : Chang-Hwan Im
language : en
Publisher:
Release Date : 2018
Computational Eeg Analysis written by Chang-Hwan Im and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electroencephalography categories.
This book introduces and reviews all of the currently available methods being used for computational electroencephalogram (EEG) analysis, from the fundamentals through to the state-of-the-art. The aim of the book is to help biomedical engineers and medical doctors who use EEG to better understand the methods and applications of computational EEG analysis from a single, well-organized resource. Following a brief introduction to the principles of EEG and acquisition techniques, the book is divided into two main sections. The first of these covers analysis methods, beginning with preprocessing, and then describing EEG spectral analysis, event-related potential analysis, source imaging and multimodal neuroimaging, and functional connectivity analysis. The following section covers application of EEG analysis to specific fields, including the diagnosis of psychiatric diseases and neurological disorders, brain-computer interfacing, and social neuroscience. Aimed at practicing medical specialists, engineers, researchers and advanced students, the book features contributions from world-renowned biomedical engineers working across a broad spectrum of computational EEG analysis techniques and EEG applications.
Computational Eeg Analysis
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Author : Chang-Hwan Im
language : en
Publisher: Springer
Release Date : 2018-08-16
Computational Eeg Analysis written by Chang-Hwan Im and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-16 with Science categories.
This book introduces and reviews all of the currently available methods being used for computational electroencephalogram (EEG) analysis, from the fundamentals through to the state-of-the-art. The aim of the book is to help biomedical engineers and medical doctors who use EEG to better understand the methods and applications of computational EEG analysis from a single, well-organized resource. Following a brief introduction to the principles of EEG and acquisition techniques, the book is divided into two main sections. The first of these covers analysis methods, beginning with preprocessing, and then describing EEG spectral analysis, event-related potential analysis, source imaging and multimodal neuroimaging, and functional connectivity analysis. The following section covers application of EEG analysis to specific fields, including the diagnosis of psychiatric diseases and neurological disorders, brain-computer interfacing, and social neuroscience. Aimed at practicing medical specialists, engineers, researchers and advanced students, the book features contributions from world-renowned biomedical engineers working across a broad spectrum of computational EEG analysis techniques and EEG applications.
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.
Advancing Computational Analysis And Modelling Of Eeg Meg Data
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Author : Dheeraj Rathee
language : en
Publisher:
Release Date : 2019
Advancing Computational Analysis And Modelling Of Eeg Meg Data written by Dheeraj Rathee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Computational Intelligence Techniques In Diagnosis Of Brain Diseases
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Author : Sasikumar Gurumoorthy
language : en
Publisher: Springer
Release Date : 2017-09-05
Computational Intelligence Techniques In Diagnosis Of Brain Diseases written by Sasikumar Gurumoorthy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-05 with Technology & Engineering categories.
This book highlights a new biomedical signal processing method of extracting a specific underlying signal from possibly noisy multi-channel recordings, and shows that the method is suitable for extracting independent components from the measured electroencephalogram (EEG) signal. The system efficiently extracts memory spindles and is also effective in Alzheimer seizures. Current developments in computer hardware and signal processing have made it possible for EEG signals or “brain waves” to communicate between humans and computers – an area that can be extended for use in this domain.
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.
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.
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.
Computational Analysis Of Eeg Microstate Syntax
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Author : Anthony Oshifeso
language : en
Publisher:
Release Date : 2012
Computational Analysis Of Eeg Microstate Syntax written by Anthony Oshifeso and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Computational Methods For Translational Brain Behavior Analysis
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Author : Rong Chen
language : en
Publisher: Frontiers Media SA
Release Date : 2021-06-24
Computational Methods For Translational Brain Behavior Analysis written by Rong Chen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-24 with Science categories.