EEG Signal Processing and Machine Learning
(Sprache: Englisch)
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...
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...
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Klappentext zu „EEG Signal Processing and Machine Learning “
EEG Signal Processing and Machine LearningExplore 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
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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.
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Inhaltsverzeichnis zu „EEG Signal Processing and Machine Learning “
Preface to the Second Edition xviiPreface to the First Edition xxi
List of Abbreviations xxiii
1 Introduction to Electroencephalography 1
1.1 Introduction 1
1.2 History 2
1.3 Neural Activities 5
1.4 Action Potentials 6
1.5 EEG Generation 8
1.6 The Brain as a Network 12
1.7 Summary 12
References 13
2 EEG Waveforms 15
2.1 Brain Rhythms 15
2.2 EEG Recording and Measurement 18
2.2.1 Conventional Electrode Positioning 21
2.2.2 Unconventional and Special Purpose EEG Recording Systems 24
2.2.3 Invasive Recording of Brain Potentials 26
2.2.4 Conditioning the Signals 27
2.3 Sleep 28
2.4 Mental Fatigue 30
2.5 Emotions 30
2.6 Neurodevelopmental Disorders 31
2.7 Abnormal EEG Patterns 32
2.8 Ageing 33
2.9 Mental Disorders 34
2.9.1 Dementia 34
2.9.2 Epileptic Seizure and Nonepileptic Attacks 35
2.9.3 Psychiatric Disorders 39
2.9.4 External Effects 40
2.10 Summary 41
References 42
3 EEG Signal Modelling 47
3.1 Introduction 47
3.2 Physiological Modelling of EEG Generation 47
3.2.1 Integrate-and-Fire Models 49
3.2.2 Phase-Coupled Models 49
3.2.3 Hodgkin-Huxley Model 51
3.2.4 Morris-Lecar Model 54
3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 57
3.4 Mathematical Models Derived Directly from the EEG Signals 61
3.4.1 Linear Models 61
3.4.1.1 Prediction Method 61
3.4.1.2 Prony's Method 62
3.4.2 Nonlinear Modelling 64
3.4.3 Gaussian Mixture Model 66
3.5 Electronic Models 67
3.5.1 Models Describing the Function of the Membrane 67
3.5.1.1 Lewis Membrane Model 68
3.5.1.2 Roy Membrane Model 68
3.5.2 Models Describing the
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Function of a Neuron 68
3.5.2.1 Lewis Neuron Model 68
3.5.2.2 The Harmon Neuron Model 71
3.5.3 A Model Describing the Propagation of the Action Pulse in an Axon 72
3.5.4 Integrated Circuit Realizations 72
3.6 Dynamic Modelling of Neuron Action Potential Threshold 72
3.7 Summary 73
References 73
4 Fundamentals of EEG Signal Processing 77
4.1 Introduction 77
4.2 Nonlinearity of the Medium 78
4.3 Nonstationarity 79
4.4 Signal Segmentation 80
4.5 Signal Transforms and Joint Time-Frequency Analysis 83
4.5.1 Wavelet Transform 87
4.5.1.1 Continuous Wavelet Transform 87
4.5.1.2 Examples of Continuous Wavelets 89
4.5.1.3 Discrete-Time Wavelet Transform 89
4.5.1.4 Multiresolution Analysis 90
4.5.1.5 Wavelet Transform Using Fourier Transform 93
4.5.1.6 Reconstruction 94
4.5.2 Synchro-Squeezed Wavelet Transform 95
4.5.3 Ambiguity Function and the Wigner-Ville Distribution 96
4.6 Empirical Mode Decomposition 100
4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function 101
4.8 Filtering and Denoising 104
4.9 Principal Component Analysis 107
4.9.1 Singular Value Decomposition 108
4.10 Summary 110
References 110
5 EEG Signal Decomposition 115
5.1 Introduction 115
5.2 Singular Spectrum Analysis 115
5.2.1 Decomposition 116
5.2.2 Reconstruction 117
5.3 Multichannel EEG Decomposition 118
5.3.1 Independent C
3.5.2.1 Lewis Neuron Model 68
3.5.2.2 The Harmon Neuron Model 71
3.5.3 A Model Describing the Propagation of the Action Pulse in an Axon 72
3.5.4 Integrated Circuit Realizations 72
3.6 Dynamic Modelling of Neuron Action Potential Threshold 72
3.7 Summary 73
References 73
4 Fundamentals of EEG Signal Processing 77
4.1 Introduction 77
4.2 Nonlinearity of the Medium 78
4.3 Nonstationarity 79
4.4 Signal Segmentation 80
4.5 Signal Transforms and Joint Time-Frequency Analysis 83
4.5.1 Wavelet Transform 87
4.5.1.1 Continuous Wavelet Transform 87
4.5.1.2 Examples of Continuous Wavelets 89
4.5.1.3 Discrete-Time Wavelet Transform 89
4.5.1.4 Multiresolution Analysis 90
4.5.1.5 Wavelet Transform Using Fourier Transform 93
4.5.1.6 Reconstruction 94
4.5.2 Synchro-Squeezed Wavelet Transform 95
4.5.3 Ambiguity Function and the Wigner-Ville Distribution 96
4.6 Empirical Mode Decomposition 100
4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function 101
4.8 Filtering and Denoising 104
4.9 Principal Component Analysis 107
4.9.1 Singular Value Decomposition 108
4.10 Summary 110
References 110
5 EEG Signal Decomposition 115
5.1 Introduction 115
5.2 Singular Spectrum Analysis 115
5.2.1 Decomposition 116
5.2.2 Reconstruction 117
5.3 Multichannel EEG Decomposition 118
5.3.1 Independent C
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Autoren-Porträt von Saeid Sanei, Jonathon A. Chambers
Saeid Sanei, PhD, DIC, FBCS, is Professor of Signal Processing and Machine Learning at Nottingham Trent University, UK, and a Visiting Professor at Imperial College London, UK. He received his doctorate in Biomedical Signal and Image Processing from Imperial College London in 1991. He is an internationally renowned expert in signal processing, biomedical signal processing, and pattern recognition.Jonathon A. Chambers, FREng, FIEEE, DSc (Imperial), is Emeritus Professor of Signal and Information Processing within the College of Science and Engineering at the University of Leicester, UK. His research interests are focused upon adaptive signal processing and machine learning and their application in biomedicine, communications, defense, and navigation systems.
Bibliographische Angaben
- Autoren: Saeid Sanei , Jonathon A. Chambers
- 2021, 2. Aufl., 752 Seiten, Maße: 17 x 24,4 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 1119386942
- ISBN-13: 9781119386940
- Erscheinungsdatum: 06.12.2021
Sprache:
Englisch
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