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
• Soggetto genere / forma: Electronic books.
• 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.
• 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.: 9910555251803321
• Lo trovi qui: Univ. Federico II