EEG signal processing and machine learning. (2021)

• Record Type: Book
• Further Information: Note: Saeid Sanei, Jonathon A. Chambers.
• Authors: Sanei, Saeid; Chambers, Jonathon A
• Contents: 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
• Edition: Second edition
• Publisher Details: Hoboken : John Wiley & Sons, Inc
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 616.8047547; Electroencephalography; Signal processing; Machine learning
• Languages: English
• ISBNs: 9781119386933
• Related ISBNs: 9781119386940
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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