Titre :	EEG Signal Processing
Type de document :	texte imprimé
Auteurs :	Saeid Sanei, Auteur ; J.A. Chambers, Auteur
Editeur :	The Atrium, Southern Gate, Chichester : John Wiley & Sons
Année de publication :	2007
Importance :	289 p.
Présentation :	couv. ill. en coul., ill.
Format :	25,2 cm.
ISBN/ISSN/EAN :	978-0-470-02581-9
Langues :	Anglais (eng)
Catégories :	GÉNIE BIOMÉDICAL
Index. décimale :	35-11 Traitement des biosignaux
Résumé :	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.
Note de contenu :	Contents 1 Introduction to EEG 1.1 History 1.2 Neural Activities 1.3 Action Potentials 1.4 EEG Generation 1.5 Brain Rhythms 1.6 EEG Recording and Measurement 1.7 Abnormal EEG Patterns 1.8 Ageing 1.9 Mental Disorders 1.10 Summary and Conclusions References 2 Fundamentals of EEG Signal Processing 2.1 EEG Signal Modelling 2.2 Nonlinearity of the Medium 2.3 Nonstationarity 2.4 Signal Segmentation 2.5 Signal Transforms and Joint Time–Frequency Analysis 2.6 Coherency, Multivariate Autoregressive (MVAR) Modelling, and Directed Transfer Function (DTF) 2.7 Chaos and Dynamical Analysis 2.8 Filtering and Denoising 2.9 Principal Component Analysis 2.10 Independent Component Analysis 2.11 Application of Constrained BSS: Example 2.12 Signal Parameter Estimation 2.13 Classification Algorithms 2.14 Matching Pursuits 2.15 Summary and Conclusions References 3 Event-Related Potentials 3.1 Detection, Separation, Localization, and Classification of P300 Signals 3.2 Brain Activity Assessment Using ERP 3.3 Application of P300 to BCI 3.4 Summary and Conclusions References 4 Seizure Signal Analysis 4.1 Seizure Detection 4.2 Chaotic Behaviour of EEG Sources 4.3 Predictability of Seizure from the EEGs\ 4.4 Fusion of EEG–fMRI Data for Seizure Prediction 4.5 Summary and Conclusions References 5 EEG Source Localization 5.1 Introduction 5.2 Overview of the Traditional Approaches 5.3 Determination of the Number of Sources 5.4 Summary and Conclusions References 6 Sleep EEG 6.1 Stages of Sleep 6.2 The Influence of Circadian Rhythms 6.3 Sleep Deprivation 6.4 Psychological Effects 6.5 Detection and Monitoring of Brain Abnormalities During Sleep by EEG Analysis 6.6 Concluding Remarks References 7 Brain–Computer Interfacing 7.1 State of the Art in BCI 7.2 Major Problems in BCI 7.3 Multidimensional EEG Decomposition 7.4 Detection and Separation of ERP Signals 7.5 Source Localization and Tracking of the Moving Sources within the Brain 7.6 Multivariant Autoregressive (MVAR) Modelling and Coherency Maps 7.7 Estimation of Cortical Connectivity 7.8 Summary and Conclusions References Index

EEG Signal Processing [texte imprimé] / Saeid Sanei, Auteur ; J.A. Chambers, Auteur . - The Atrium, Southern Gate, Chichester : John Wiley & Sons, 2007 . - 289 p. : couv. ill. en coul., ill. ; 25,2 cm.
ISBN : 978-0-470-02581-9
Langues : Anglais (eng)

Catégories :	GÉNIE BIOMÉDICAL
Index. décimale :	35-11 Traitement des biosignaux
Résumé :	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.
Note de contenu :	Contents 1 Introduction to EEG 1.1 History 1.2 Neural Activities 1.3 Action Potentials 1.4 EEG Generation 1.5 Brain Rhythms 1.6 EEG Recording and Measurement 1.7 Abnormal EEG Patterns 1.8 Ageing 1.9 Mental Disorders 1.10 Summary and Conclusions References 2 Fundamentals of EEG Signal Processing 2.1 EEG Signal Modelling 2.2 Nonlinearity of the Medium 2.3 Nonstationarity 2.4 Signal Segmentation 2.5 Signal Transforms and Joint Time–Frequency Analysis 2.6 Coherency, Multivariate Autoregressive (MVAR) Modelling, and Directed Transfer Function (DTF) 2.7 Chaos and Dynamical Analysis 2.8 Filtering and Denoising 2.9 Principal Component Analysis 2.10 Independent Component Analysis 2.11 Application of Constrained BSS: Example 2.12 Signal Parameter Estimation 2.13 Classification Algorithms 2.14 Matching Pursuits 2.15 Summary and Conclusions References 3 Event-Related Potentials 3.1 Detection, Separation, Localization, and Classification of P300 Signals 3.2 Brain Activity Assessment Using ERP 3.3 Application of P300 to BCI 3.4 Summary and Conclusions References 4 Seizure Signal Analysis 4.1 Seizure Detection 4.2 Chaotic Behaviour of EEG Sources 4.3 Predictability of Seizure from the EEGs\ 4.4 Fusion of EEG–fMRI Data for Seizure Prediction 4.5 Summary and Conclusions References 5 EEG Source Localization 5.1 Introduction 5.2 Overview of the Traditional Approaches 5.3 Determination of the Number of Sources 5.4 Summary and Conclusions References 6 Sleep EEG 6.1 Stages of Sleep 6.2 The Influence of Circadian Rhythms 6.3 Sleep Deprivation 6.4 Psychological Effects 6.5 Detection and Monitoring of Brain Abnormalities During Sleep by EEG Analysis 6.6 Concluding Remarks References 7 Brain–Computer Interfacing 7.1 State of the Art in BCI 7.2 Major Problems in BCI 7.3 Multidimensional EEG Decomposition 7.4 Detection and Separation of ERP Signals 7.5 Source Localization and Tracking of the Moving Sources within the Brain 7.6 Multivariant Autoregressive (MVAR) Modelling and Coherency Maps 7.7 Estimation of Cortical Connectivity 7.8 Summary and Conclusions References Index