Adaptive processing of brain signals /

• Κύριος συγγραφέας: Sanei, Saeid
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Chichester, West Sussex, United Kingdom : Wiley, 2013.
• Θέματα: Neural networks (Neurobiology); Brain > Physiology.; Signal processing > Digital techniques.; SCIENCE / Waves & Wave Mechanics.; Brain > physiology.; Signal Processing, Computer-Assisted.; Electroencephalography.; Magnetoencephalography.; Neurobiology.; SOCIAL SCIENCE / Anthropology / Physical; Electronic books.

 "Brain signal processing spans a broad range of knowledge across engineering, science and medicine, and this book brings together the disparate theory and application to create a comprehensive resource on this growing topic. It will provide advanced tools for the detection, monitoring, separation, localizing and understanding of brain functional, anatomical, and physiological abnormalities. The focus will be on advanced and adaptive signal processing techniques for the processing of electroencephalography and magneto-encephalography signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI). Multimodal processing of brain signals, the new focus for brain signal research, will also be explored. The book covers the broad remit of neuro-imaging, ensuring comprehensive coverage of all issues related to brain signal processing. Topics such as mental fatigue, brain connectivity and new recording techniques will also be covered.This book will be a progression/follow on from Dr Sanei's first book with Wiley, EEG Signal Processing"--   Provided by publisher. 

 1 Brain Signals, Their Generation, Acquisition and Properties 1 -- 1.1 Introduction 1 -- 1.2 Historical Review of the Brain 1 -- 1.3 Neural Activities 5 -- 1.4 Action Potentials 5 -- 1.5 EEG Generation 8 -- 1.6 Brain Rhythms 10 -- 1.7 EEG Recording and Measurement 14 -- 1.7.1 Conventional EEG Electrode Positioning 16 -- 1.7.2 Conditioning the Signals 18 -- 1.8 Abnormal EEG Patterns 19 -- 1.9 Aging 22 -- 1.10 Mental Disorders 23 -- 1.10.1 Dementia 23 -- 1.10.2 Epileptic Seizure and Nonepileptic Attacks 24 -- 1.10.3 Psychiatric Disorders 28 -- 1.10.4 External Effects 29 -- 1.11 Memory and Content Retrieval 30 -- 1.12 MEG Signals and Their Generation 32 -- 1.13 Conclusions 32 -- References 33 -- 2 Fundamentals of EEG Signal Processing 37 -- 2.1 Introduction 37 -- 2.2 Nonlinearity of the Medium 38 -- 2.3 Nonstationarity 39 -- 2.4 Signal Segmentation 40 -- 2.5 Other Properties of Brain Signals 43 -- 2.6 Conclusions 44 -- References 44 -- 3 EEG Signal Modelling 45 -- 3.1 Physiological Modelling of EEG Generation 45 -- 3.1.1 Integrate-and-Fire Models 45 -- 3.1.2 Phase-Coupled Models 46 -- 3.1.3 Hodgkin and Huxley Model 48 -- 3.1.4 Morris-Lecar Model 52 -- 3.2 Mathematical Models 54 -- 3.2.1 Linear Models 54 -- 3.2.2 Nonlinear Modelling 57 -- 3.2.3 Gaussian Mixture Model 59 -- 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 61 -- 3.4 Electronic Models 64 -- 3.4.1 Models Describing the Function of the Membrane 64 -- 3.4.2 Models Describing the Function of Neurons 65 -- 3.4.3 A Model Describing the Propagation of an Action Pulse in an Axon 67 -- 3.4.4 Integrated Circuit Realizations 68 -- 3.5 Dynamic Modelling of the Neuron Action Potential Threshold 68 -- 3.6 Conclusions 68 -- References 68 -- 4 Signal Transforms and Joint Time-Frequency Analysis 72 -- 4.1 Introduction 72 -- 4.2 Parametric Spectrum Estimation and Z-Transform 73 -- 4.3 Time-Frequency Domain Transforms 74 -- 4.3.1 Short-Time Fourier Transform 74 -- 4.3.2 Wavelet Transfonn 75 -- 4.3.3 Multiresolution Analysis 78 -- 4.4 Ambiguity Function and the Wigner-Ville Distribution 82 -- 4.5 Hermite Transform 85 -- 4.6 Conclusions 88 -- References 88 -- 5 Chaos and Dynamical Analysis 90 -- 5.1 Entropy 91 -- 5.2 Kolmogorov Entropy 91 -- 5.3 Lyapunov Exponents 92 -- 5.4 Plotting the Attractor Dimensions from Time Series 93 -- 5.5 Estimation of Lyapunov Exponents from Time Series 94 -- 5.5.1 Optimum Time Delay 96 -- 5.5.2 Optimum Embedding Dimension 97 -- 5.6 Approximate Entropy 98 -- 5.7 Using Prediction Order 98 -- 5.8 Conclusions 99 -- References 100 -- 6 Classification and Clustering of Brain Signals 101 -- 6.1 Introduction 101 -- 6.2 Linear Discriminant Analysis 102 -- 6.3 Support Vector Machines 103 -- 6.4 k-Means Algorithm 109 -- 6.5 Common Spatial Patterns 112 -- 6.6 Conclusions 115 -- References 116 -- 7 Blind and Semi-Blind Source Separation 118 -- 7.1 Introduction 118 -- 7.2 Singular Spectrum Analysis 119 -- 7.2.1 Decomposition 119 -- 7.2.2 Reconstruction 120 -- 7.3 Independent Component Analysis 121 -- 7.4 Instantaneous BSS 125 -- 7.5 Convolutive BSS 130 -- 7.5.1 General Applications 130 -- 7.5.2 Application of Convolutive BSS to EEG 132 -- 7.6 Sparse Component Analysis 133 -- 7.7 Nonlinear BSS 134 -- 7.8 Constrained BSS 135 -- 7.9 Application of Constrained BSS; Example 136 -- 7.10 Nonstationary BSS 137 -- 7.10.1 Tensor Factorization for BSS 140 -- 7.10.2 Solving BSS of Nonstationary Sources Using Tensor Factorization 144 -- 7.11 Tensor Factorization for Underdetermined Source Separation 151 -- 7.12 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 153 -- 7.13 Separation of Correlated Sources via Tensor Factorization 153 -- 7.14 Conclusions 154 -- References 154 -- 8 Connectivity of Brain Regions 159 -- 8.1 Introduction 159 -- 8.2 Connectivity Through Coherency 161 -- 8.3 Phase-Slope Index 163 -- 8.4 Multivariate Directionality Estimation 163 -- 8.4.1 Directed Transfer Function 164 -- 8.5 Modelling the Connectivity by Structural Equation Modelling 166 -- 8.6 EEG Hyper-Scanning and Inter-Subject Connectivity 168 -- 8.6.1 Objectives 168 -- 8.6.2 Technological Relevance 169 -- 8.7 State-Space Model for Estimation of Cortical Interactions 173 -- 8.8 Application of Adaptive Filters 175 -- 8.8.1 Use of Kalman Filter 176 -- 8.8.2 Task-Related Adaptive Connectivity 178 -- 8.8.3 Diffusion Adaptation 179 -- 8.8.4 Application of Diffusion Adaptation to Brain Connectivity 179 -- 8.9 Tensor Factorization Approach 182 -- 8.10 Conclusions 184 -- References 185