EEG signal processing and machine learning / / Saeid Sanei, Jonathon A. Chambers

• Autore: Sanei Saeid
• Pubblicazione: Hoboken, New Jersey : , : Wiley, , [2022]; ©2022
• Edizione: Second edition.
• Descrizione fisica: 1 online resource (751 pages)
• Disciplina: 616.8047547
• Soggetto topico: Electroencephalography
• Persona (resp. second.): ChambersJonathon A.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: "Electroencephalogram (EEG) signal processing is concerned with the development and application of advanced digital signal processing algorithms for analysis, quantification, separation, and classification of the impact of various brain abnormalities on the EEGs. Any medical or neurological condition that affects brain function will alter the EEG. Brain abnormalities introduce various rhythmic or arrhythmic effects on the signals. Moreover, most of the abnormalities in the human body directly or indirectly affect the brain and consequently change the EEG signals. Processing of biosignals using newly developed techniques have become a strong field of research and digital signal processing concepts have become part of the core training in biomedical engineering"--
• Altri titoli varianti: Electroencephalogram signal processing and machine learning
• Titolo autorizzato: EEG signal processing and machine learning
• ISBN: 1-119-38693-4; 1-119-38695-0; 1-119-38692-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830667303321
• Lo trovi qui: Univ. Federico II