EEG Signal Processing and Machine Learning

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.

Auteur

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.

Texte du rabat

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.

Contenu

Preface to the Second Edition

Preface to the First Edition

List of Abbreviations

CHAPTER 1 INTRODUCTION TO ELECTROENCEPHALOGRAPHY

1.1Introduction

1.2History

1.3 Neural Activities

1.4 Action Potentials

1.5 EEG Generation

1.6 Brain as a Network

1.7 Conclusion

References

CHAPTER 2 EEG WAVEFORMS

2.1 Brain Rhythms

2.2 EEG Recording and Measurement

2.2.1 Conventional Electrode Positioning

2.2.2 Unconventional and Special Purpose EEG Recording Systems

2.2.3 Invasive Recording of Brain Potentials

2.2.4 Conditioning the Signals

2.3 Sleep

2.4 Mental fatigue

2.5 Emotions

2.6 Neurodevelopmental Disorders

2.7 Abnormal EEG Patterns

2.8 Aging

2.9 Mental Disorders

2.9.1 Dementia

2.9.2 Epileptic Seizure and Nonepileptic Attacks

2.9.3 Psychiatric Disorders

2.9.4 External Effects

2.10 Summary

References

CHAPTER 3 EEG SIGNAL MODELLING

3.1 Introduction

3.2 Physiological Modelling of EEG Generation

3.2.1 Integrate and Fire Models

3.2.2 Phase-Coupled Models

3.2.3 Hodgkin and Huxley Model

3.2.4 Morris-Lecar Model

3.3 Generating EEG Signals Based on Modelling the Neuronal Activities

3.4 Mathematical Models Derived Directly from the EEG Signals

3.4.1 Linear Models

3.4.1.1 Prediction method

3.4.1.2 Prony's method

3.4.2 Nonlinear Modelling

3.4.3 Gaussian Mixture Model

3.5 Electronic Models

3.5.1 Models Describing the Function of the Membrane

3.5.1.1 Lewis membrane model

3.5.1.2 Roy membrane model

3.5.2 Models Describing the Function of Neuron

3.5.2.1 Lewis neuron model

3.5.2.2 The Harmon neuron model

3.5.3 A Model Describing the Propagation of Action Pulse in Axon

3.5.4 Integrated Circuit Realizations

3.6 Dynamic Modelling of Neuron Action Potential Threshold

3.7 Summary

References

CHAPTER 4 FUNDAMENTALS OF EEG SIGNAL PROCESSING

4.1 Introduction

4.2 Nonlinearity of the Medium

4.3 Nonstationarity

4.4 Signal Segmentation

4.5 Signal Transforms and Joint Time-Frequency Analysis

4.5.1 Wavelet Transform

4.5.1.1 Continuous wavelet transform

4.5.1.2 Examples of continuous wavelets

4.5.1.3 Discrete time wavelet transform

4.5.1.4 Multiresolution analysis

4.5.1.5 Wavelet transform using Fourier transform

4.5.1.6 Reconstruction

4.5.2 Synchro-squeezed Wavelet Transform

4.5.3 Ambiguity Function and the Wigner-Ville Distribution

4.6 Empirical Mode Decomposi…