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EEG Signal Processing and Machine Learning

Sanei, Saeid / Chambers, Jonathon A.

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2. Auflage Oktober 2021
752 Seiten, Hardcover
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ISBN: 978-1-119-38694-0
John Wiley & Sons

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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.

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 Decomposition

4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function

4.8 Filtering and Denoising

4.9 Principal Component Analysis

4.9.1 Singular Value Decomposition

4.10 Summary

References

CHAPTER 5 EEG SIGNAL DECOMPOSITION

5.1 Introduction

5.2 Singular Spectrum Analysis

5.2.1 Decomposition

5.2.2 Reconstruction

5.3 Multichannel EEG Decomposition

5.3.1 Independent Component Analysis

5.3.2 Instantaneous BSS

5.3.3 Convolutive BSS

5.3.3.1 General Applications

5.3.3.2 Application of convolutive BSS to EEG

5.4 Sparse Component Analysis

5.4.1 Standard Algorithms for Sparse Source Recovery

5.4.1.1 Greedy based solution

5.4.1.2 Relaxation based solution

5.4.2 k-Sparse Mixtures

5.5 Nonlinear BSS

5.6 Constrained BSS

5.7 Application of Constrained BSS; Example

5.8 Multiway EEG decompositions

5.8.1 Tensor Factorization for BSS

5.8.2 Solving BSS of Nonstationary Sources using Tensor Factorization

5.9 Tensor Factorization for Underdetermined Source Separation

5.10 Tensor Factorization for Separation of Convolutive Mixtures in Time Domain

5.11 Separation of Correlated Sources via Tensor Factorization

5.12 Common Component Analysis

5.13 Canonical Correlation Analysis

5.14 Summary

References

CHAPTER 6 CHAOS AND DYNAMICAL ANALYSIS

6.1 Introduction to Chaos and Dynamical Systems

6.2 Entropy

6.3 Kolmogorov Entropy

6.4 Multiscale Fluctuation-Based Dispersion Entropy

6.5 Lyapunov Exponents

6.6 Plotting the Attractor Dimensions from Time Series

6.7 Estimation of Lyapunov Exponents from Time Series

6.7.1 Optimum Time Delay

6.7.2 Optimum Embedding Dimension

6.8 Approximate Entropy

6.9 Using Prediction Order

6.10 Summary

References

CHAPTER 7 MACHINE LEARNING FOR EEG ANALYSIS

7.1 Introduction

7.2 Clustering Approaches

7.2.1 k-means Clustering Algorithm

7.2.2 Iterative Self-organising Data Analysis Technique

7.2.3 Gap Statistics

7.2.4 Density Based Clustering

7.2.5 Affinity Based Clustering

7.2.6 Deep Clustering

7.2.7 Semi-supervised Clustering

7.2.7.1 Basic Semi-supervised techniques

7.2.7.2 Deep semi-supervised techniques

7.2.8 Fuzzy Clustering

7.3 Classification Algorithms

7.3.1 Decision Trees

7.3.2 Random Forest

7.3.3 Linear Discriminant Analysis

7.3.4 Support Vector Machines

7.3.5 K-Nearest Neighbour

7.3.6 Gaussian Mixture Model

7.3.7 Logistic Regression

7.3.8 Reinforcement Learning

7.3.9 Artificial Neural Networks

7.3.9.1 Deep neural networks

7.3.9.2 Convolutional neural networks

7.3.9.3 Autoencoders

7.3.9.4 Variational autoencoder

7.3.9.5 Recent DNN approaches

7.3.9.6 Spike neural networks

7.3.9.7 Applications of DNNs to EEG

7.3.10 Gaussian Processes

7.3.11 Neural Processes

7.3.12 Graph Convolutional Networks

7.3.13 Naïve Bayes Classifier

7.3.14 Hidden Markov Model

7.3.14.1 Forward algorithm

7.3.14.2 Backward algorithm

7.3.14.3 HMM design

7.4 Common Spatial Patterns

7.5 Summary

References

CHAPTER 8 BRAIN CONNECTIVITY AND ITS APPLICATIONS

8.1 Introduction

8.2 Connectivity through Coherency

8.3 Phase-Slope Index

8.4 Multivariate Directionality Estimation

8.4.1 Directed Transfer Function

8.4.2 Direct DTF

8.4.3 Partial Directed Coherence

8.5 Modelling the Connectivity by Structural Equation Modelling

8.6 Stockwell Time-Frequency Transform for Connectivity Estimation

8.7 Inter-Subject EEG Connectivity

8.7.1 Objectives

8.7.2 Technological Relevance

8.8 State-Space Model for Estimation of Cortical Interactions

8.9 Application of Cooperative Adaptive Filters

8.9.1 Use of Cooperative Kalman Filter

8.9.2 Task-related Adaptive Connectivity

8.9.3 Diffusion Adaptation

8.9.4 Brain Connectivity for Cooperative Adaptation

8.9.5 Other Applications of Cooperative Learning and Brain Connectivity Estimation

8.10 Graph Representation of Brain Connectivity

8.11 Tensor Factorization Approach

8.12 Summary

References

CHAPTER 9 EVENT RELATED BRAIN RESPONSES

9.1 Introduction

9.2 ERP Generation and Types

9.2.1 P300 and its Subcomponents

9.3 Detection, Separation, and Classification of P300 Signals

9.3.1 Using ICA

9.3.2 Estimation of Single Trial Brain Responses by Modelling the ERP Waveforms

9.3.3 ERP Source Tracking in Time

9.3.4 Time-Frequency Domain Analysis

9.3.5 Application of Kalman Filter

9.3.6 Particle Filtering and its Application to ERP Tracking

9.3.7 Variational Bayes Method

9.3.8 Prony's Approach for Detection of P300 Signals

9.3.9 Adaptive Time-Frequency Methods

9.4 Brain Activity Assessment Using ERP

9.5 Application of P300 to BCI

9.6 Summary

References

CHAPTER 10 LOCALIZATION OF BRAIN SOURCES

10.1 Introduction

10.2 General Approaches to Source Localization

10.2.1 Dipole Assumption

10.3 Head Model

10.4 Most Popular Brain Source Localization Approaches

10.4.1 EEG Source Localisation Using Independent Component Analysis

10.4.2 MUSIC Algorithm

10.4.3 LORETA Algorithm

10.4.4 FOCUSS Algorithm

10.4.5 Standardised LORETA

10.4.6 Other Weighted Minimum Norm Solutions

10.4.7 Evaluation Indices

10.4.8 Joint ICA-LORETA Approach

10.5 Forward Solutions to Localisation problem

10.5.1 Partially Constrained BSS Method

10.5.2 Constrained Least Squares Method for localization of P3a and P3b

10.5.3 Spatial Notch Filtering Approach

10.6 The Methods based on Source Tracking

10.6.1 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization

10.6.2 Hybrid Beamforming - Particle Filtering

10.7 Determination of the Number of Sources from the EEG/MEG Signals

10.8 Other Hybrid Methods

10.9 Application of Machine Learning for EEG/MEG Source Localisation

10.10 Summary

References

CHAPTER 11 EPILEPTIC SEIZURE PREDICTION, DETECTION, AND LOCALISATION

11.1 Introduction

11.2 Seizure Detection

11.2.1 Adult Seizure Detection from EEGs

11.2.2 Detection of Neonatal Seizure

11.3 Chaotic Behaviour of Seizure EEG

11.4 Seizure detection from Brain Connectivity

11.5 Prediction of Seizure Onset from EEG

11.6 Intracranial and Joint Scalp-Intracranial Recordings for IED Detection

11.6.1 Introduction to IED

11.6.2 iEED-times IED Detection from Scalp EEG

11.6.3 A Multiview Approach to IED Detection

11.6.4 Coupled Dictionary Learning for IED Detection

11.6.5 A Deep Learning Approach to IED Detection

11.7 Fusion of EEG - fMRI data for Seizure Prediction

11.8 Summary

References

Chapter 12 SLEEP RECOGNITION, SCORING AND ABNORMALITIES

12.1 Introduction

12.1.1 Definition of Sleep

12.1.2 Sleep disorder

12.2 Stages of Sleep

12.2.1 NREM Sleep

12.2.2 REM Sleep

12.3 The Influence of Circadian Rhythms

12.4 Sleep Deprivation

12.5 Psychological Effects

12.6 EEG Sleep Analysis and Scoring

12.6.1 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation

12.6.2 Time-Frequency Analysis of Sleep EEG using Matching Pursuit

12.6.3 Detection of Normal Rhythms and Spindles Using Higher Order Statistics

12.6.4 Sleep Scoring using Tensor Factorisation

12.6.5 Application of Neural Networks

12.6.6 Model-Based Analysis

12.7 Detection and Monitoring of Brain Abnormalities during Sleep by EEG and multimodal PSG Analysis

12.7.1 Analysis of Sleep Apnoea

12.7.2 EEG and Fibromyalgia Syndrome

12.7.3 Sleep Disorders of Neonates

12.8 Dreams and Nightmares

12.9 EEG and Consciousness

12.10 Functional Brain Connectivity for Sleep Analysis

12.11 Summary

References

CHAPTER 13 EEG-BASED MENTAL FATIGUE MONITORING

13.1 Introduction

13.2 Feature-based Machine Learning Approaches

13.2.1 Hidden Markov Model Application

13.2.2 Kernel Principal Component Analysis and Hidden Markov Model

13.2.3 Regression-based Fatigue Estimation

13.2.4 Regularised Regression

13.2.5 Other feature Based Approaches

13.3 Measurement of Brain Synchronization and Coherency

13.3.1 Linear Measure of Synchronization

13.3.2 Nonlinear Measure of Synchronization

13.4 Evaluation of ERP for Mental Fatigue

13.5 Separation of P3a and P3b

13.6 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm

13.7 Assessing Mental Fatigue by Measuring Functional Connectivity

13.8 Deep Learning Approaches for Fatigue Evaluation

13.9 Summary

References

CHAPTER 14 EEG-BASED EMOTION RECOGNITION AND CLASSIFICATION

14.1 Introduction

14.1.1 Theories and Emotion Classification

14.1.2 The Physiological Effects of Emotions

14.1.3 Psychology and Psychophysiology of Emotion

14.1.4 Emotion Regulation

14.1.5 Emotion-Provoking Stimuli

14.2 Effect of Emotion on the Brain

14.2.1 ERP Change due to Emotion

14.2.2 Changes of Normal Brain Rhythms with Emotion

14.2.3 Emotion and Lateral Brain Engagement

14.2.4 Perception of Odours and Emotion: Why Are They Related?

14.3 Emotion-Related Brain Signal Processing and Machine Learning

14.3.1 Evaluation of Emotion Based on the Changes in Brain Rhythms

14.3.2 Brain Asymmetricity and Connectivity for Emotion Evaluation

14.3.3 Changes in ERPs for Emotion Recognition

14.3.4 Combined Features for Emotion Analysis

14.4 Other Physiological Measurement Modalities Used for Emotion Study

14.5 Applications

14.6 Pain Assessment using EEG

14.7 Emotion Elicitation and Induction through Virtual Reality

14.8 Summary

References

CHAPTER 15 EEG ANALYSIS OF NEURODEGENERATIVE DISEASES

15.1 Introduction

15.2 Alzheimer's Disease

15.2.1 Application of Brain Connectivity Estimation to AD and MCI

15.2.2 ERP based AD Monitoring

15.2.3 Other Approaches to EEG-based AD Monitoring

15.3 Motor Neuron Disease

15.4 Parkinson's Disease

15.5 Huntington's Disease

15.6 Prion Disease

15.7 Behaviour Variant Frontotemporal Dementia

15.8 Lewy Body Dementia

15.9 Summary

References

CHAPTER 16 EEG AS A BIOMARKER FOR PSYCHIATRIC AND NEURODEVELOPMENTAL DISORDERS

16.1 Introduction

16.1.1 History

16.1.2 Different Psychiatric and Neurodevelopmental Disorders

16.1.3 NDD Diagnosis

16.2 EEG Analysis for Different NDDs

16.2.1 ADHD

16.2.1.1 ADHD symptoms and possible treatment

16.2.1.2 EEG-based diagnosis of ADHD

16.2.2 ASD

16.2.2.1 ASD Symptoms and possible treatment

16.2.2.2 EEG-based diagnosis of ASD

16.2.3 Mood Disorder

16.2.3.1 EEG for monitoring depression

16.2.3.2 EEG for monitoring bipolar disorder

16.2.4 Schizophrenia

16.2.4.1 Schizophrenia symptoms and management

16.2.4.2 EEG as the biomarker for schizophrenia

16.2.5 Anxiety (and Panic) Disorder

16.2.5.1 Definition and symptoms

16.2.5.2 EEG for assessing anxiety

16.2.6 Insomnia

16.2.6.1 Symptoms of insomnia

16.2.5.2 EEG for insomnia analysis

16.2.7 Schizotypal personality disorder

16.2.7.1 What is schizotypal disorder?

16.2.7.2 EEG manifestation of schizotypal

16.3 Summary

References

CHAPTER 17 BRAIN-COMPUTER INTERFACING USING EEG

17.1 Introduction

17.1.1 State of the Art in BCI

17.1.2 BCI Terms and Definitions

17.1.3 Popular BCI Directions

17.1.3 Virtual Environment for BCI

17.1.4 Evolution of BCI Design

17.2 BCI Related EEG Components

17.2.1 Readiness Potential and its Detection

17.2.2 ERD and ERS

17.2.3 Transient Beta Activity after the Movement

17.2.4 Gamma Band Oscillations

17.2.5 Long Delta Activity

17.2.6 ERPs

17.3 Major Problems in BCI

17.3.1 Pre-processing of the EEGs

17.4 Multi-Dimensional EEG Decomposition

17.4.1 Space-Time-Frequency Method

17.4.2 Parallel Factor Analysis

17.5 Detection and Separation of ERP Signals

17.6 Estimation of Cortical Connectivity

17.7 Application of Common Spatial Patterns

17.8 Multiclass Brain Computer Interfacing

17.9 Cell-Cultured BCI

17.10 Recent BCI Applications

17.11 Neurotechnology for BCI

17.12 Joint EEG and other Brain Scanning modalities for BCI

17.12.1 Joint EEG-fMRI for BCI

10.12.2 Joint EEG-fNIRS for BCI

10.12.3 Joint EEG-MEG for BCI

17.13 Performance Measures for BCI Systems

17.14 Summary

References

CHAPTER 18 JOINT ANALYSIS OF EEG AND OTHER SIMULTANEOUSLY RECORDED BRAIN FUNCTIONAL NEUROIMAGING MODALITIES

18.1 Introduction

18.2 Fundamental Concepts

18.2.1 Functional Magnetic Resonance Imaging

18.2.1.1 Blood oxygenation level dependence

18.2.1.2 Popular fMRI data formats

18.2.1.3 Pre-processing of fMRI data

18.2.2 Functional Near Infrared Spectroscopy

18.2.3 Magnetoencephalography

18.3 Joint EEG-fMRI

18.3.1 Relation Between EEG and fMRI

18.3.2 Model-based Method for BOLD Detection

18.3.3 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG

18.3.3.1 Gradient artefact removal from EEG

18.3.3.2 Ballistocardiogram artefact removal from EEG

18.3.4 BOLD Detection in fMRI

18.3.4.1 Implementation of different NMF algorithms for BOLD detection

18.3.4.2 BOLD detection experiments

18.3.5 Fusion of EEG and fMRI

18.3.5.1 Extraction of fMRI time-course from EEG

18.3.5.2 Fusion of EEG and fMRI; blind approach

18.3.5.3 Fusion of EEG and fMRI; model-based approach

18.3.6 Application to Seizure Detection

18.3.7 Investigation of Decision Making in the Brain

18.3.8 Application to Schizophrenia

18.3.9 Other Applications

18.4 EEG-NIRS Joint Recording and Fusion

18.5 MEG-EEG Fusion

18.6 Summary

References

INDEX
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.

S. Sanei, Cardiff University; J. A. Chambers, Department of Electronic and Electrical Engineering, University of Bath, UK