EEG Signal Processing and Machine Learning

Cover
Title Page
Copyright Page
Contents
Preface to the Second Edition
Preface to the First Edition
List of Abbreviations
Chapter 1 Introduction to Electroencephalography
	1.1 Introduction
	1.2 History
	1.3 Neural Activities
	1.4 Action Potentials
	1.5 EEG Generation
	1.6 The Brain as a Network
	1.7 Summary
	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 Ageing
	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–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 a 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 the Action Pulse in an 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 the 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-Organizing 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 Localization Using Independent Component Analysis
		10.4.2 MUSIC Algorithm
		10.4.3 LORETA Algorithm
		10.4.4 FOCUSS Algorithm
		10.4.5 Standardized 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 the Localization 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 Localization
	10.10 Summary
	References
Chapter 11 Epileptic Seizure Prediction, Detection, and Localization
	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 Factorization
		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 Regularized 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
			14.1.4.1 Agency and Intentionality
			14.1.4.2 Norm Violation
			14.1.4.3 Guilt
			14.1.4.4 Shame
			14.1.4.5 Embarrassment
			14.1.4.6 Pride
			14.1.4.7 Indignation and Anger
			14.1.4.8 Contempt
			14.1.4.9 Pity and Compassion
			14.1.4.10 Awe and Elevation
			14.1.4.11 Gratitude
		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
			16.1.1.1 Different Psychiatric and Neurodevelopmental Disorders
			16.1.1.2 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
			16.2.6.2 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
		17.1.4 Virtual Environment for BCI
		17.1.5 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 Preprocessing of the EEGs
	17.4 Multidimensional 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–fNIRS for BCI
		17.12.2 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 Preprocessing 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
EULA