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Adaptive processing of brain signals

Author: Saeid Sanei
Publisher: Chichester, West Sussex, United Kingdom : Wiley, 2013.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
"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
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Sanei, Saeid.
Adaptive processing of brain signals
Chichester, West Sussex, United Kingdom : John Wiley & Sons Inc., 2013
(DLC) 2013005190
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Saeid Sanei
ISBN: 9781118622162 1118622162 9781118622155 1118622154
OCLC Number: 828193860
Description: 1 online resource.
Contents: 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 9 Detection and Tracking of Event-Related Potentials 188 --
9.1 ERP Generation and Types 188 --
9.1.1 P300 and Its Subcomponents 191 --
9.2 Detection, Separation, and Classification of P300 Signals 192 --
9.2.1 Using ICA 193 --
9.2.2 Estimation of Single Trial Brain Responses by Modelling the ERP Waveforms 195 --
9.2.3 ERP Source Tracking in Time 197 --
9.2.4 Time-Frequency Domain Analysis 200 --
9.2.5 Application of Kalman Filter 203 --
9.2.6 Particle Filtering and Its Application to ERP Tracking 206 --
9.2.7 Variational Bayes Method 209 --
9.2.8 Prony's Approach for Detection of P300 Signals 211 --
9.2.9 Adaptive Time-Frequency Methods 214 --
9.3 Brain Activity Assessment Using ERP 216 --
9.4 Application of P300 to BCI 217 --
9.5 Conclusions 218 --
References 219 --
10 Mental Fatigue 223 --
10.1 Introduction 223 --
10.2 Measurement of Brain Synchronization and Coherency 224 --
10.2.1 Linear Measure of Synchronization 224 --
10.2.2 Nonlinear Measure of Synchronization 226 --
10.3 Evaluation of ERP for Mental Fatigue 227 --
10.4 Separation of P3a and P3b 234 --
10.5 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm 238 --
10.6 Conclusions 243 --
References 243 --
11 Emotion Encoding, Regulation and Control 245 --
11.1 Theories and Emotion Classification 246 --
11.2 The Effects of Emotions 248 --
11.3 Psychology and Psychophysiology of Emotion 251 --
11.4 Emotion Regulation 252 --
11.5 Emotion-Provoking Stimuli 257 --
11.6 Change in the ERP and Normal Brain Rhythms 259 --
11.6.1 ERP and Emotion 259 --
11.6.2 Changes in Normal Brain Waves with Emotion 261 --
11.7 Perception of Odours and Emotion: Why Are They Related? 262 --
11.8 Emotion-Related Brain Signal Processing 263 --
11.9 Other Neuroimaging Modalities Used for Emotion Study 264 --
11.10 Applications 267 --
11.11 Conclusions 268 --
References 268 --
12 Sleep and Sleep Apnoea 274 --
12.1 Introduction 274 --
12.2 Stages of Sleep 275 --
12.2.1 NREM Sleep 275 --
12.2.2 REM Sleep 277 --
12.3 The Influence of Circadian Rhythms 278 --
12.4 Sleep Deprivation 279 --
12.5 Psychological Effects 280 --
12.6 Detection and Monitoring of Brain Abnormalities During Sleep by EEG Analysis 281 --
12.6.1 Analysis of Sleep Apnoea 281 --
12.6.2 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation 282 --
12.6.3 Application of Matching Pursuit 282 --
12.6.4 Detection of Normal Rhythms and Spindles Using Higher Order Statistics 285 --
12.6.5 Application of Neural Networks 287 --
12.6.6 Model-Based Analysis 288 --
12.6.7 Hybrid Methods 290 --
12.7 EEG and Fibromyalgia Syndrome 290 --
12.8 Sleep Disorders of Neonates 291 --
12.9 Dreams and Nightmares 291 --
12.10 Conclusions 292 --
References 292 --
13 Brain-Computer Interfacing 295 --
13.1 Introduction 295 --
13.2 State of the Art in BCI 296 --
13.3 BCI-Related EEG Features 300 --
13.3.1 Readiness Potential and Its Detection 300 --
13.3.2 ERD and ERS 300 --
13.3.3 Transient Beta Activity after the Movement 302 --
13.3.4 Gamma Band Oscillations 302 --
13.3.5 Long Delta Activity 303 --
13.4 Major Problems in BCI 303 --
13.4.1 Pre-Processing of the EEGs 304 --
13.5 Multidimensional EEG Decomposition 306 --
13.5.1 Space-Time-Frequency Method 308 --
13.5.2 Parallel Factor Analysis 309 --
13.6 Detection and Separation of ERP Signals 310 --
13.7 Estimation of Cortical Connectivity 311 --
13.8 Application of Common Spatial Patterns 314 --
13.9 Multiclass Brain-Computer Interfacing 316 --
13.10 Cell-Cultured BCI 318 --
13.11 Conclusions 319 --
References 320 --
14 EEG and MEG Source Localization 325 --
14.1 Introduction 325 --
14.2 General Approaches to Source Localization 326 --
14.2.1 Dipole Assumption 327 --
14.3 Most Popular Brain Source Localization Approaches 329 --
14.3.1 ICA Method 329 --
14.3.2 MUSIC Algorithm 329 --
14.3.3 LORETA Algorithm 333 --
14.3.4 FOCUSS Algorithm 335 --
14.3.5 Standardised LORETA 335 --
14.3.6 Other Weighted Minimum Norm Solutions 336 --
14.3.7 Evaluation Indices 338 --
14.3.8 Joint ICA-LORETA Approach 338 --
14.3.9 Partially Constrained BSS Method 340 --
14.3.10 Constrained Least-Squares Method for Localization of P3a and P3b 341 --
14.3.11 Spatial Notch Filtering Approach 342 --
14.3.12 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization 347 --
14.3.13 Hybrid Beamforming --
Particle Filtering 351 --
14.4 Determination of the Number of Sources from the EEG/MEG Signals 353 --
14.5 Conclusions 355 --
References 356 --
15 Seizure and Epilepsy 360 --
15.1 Introduction 360 --
15.2 Types of Epilepsy 362 --
15.3 Seizure Detection 365 --
15.3.1 Adult Seizure Detection 365 --
15.3.2 Detection of Neonate Seizure 371 --
15.4 Chaotic Behaviour of EEG Sources 376 --
15.5 Predictability of Seizure from the EEGs 378 --
15.6 Fusion of EEG --
fMRI Data for Seizure Detection and Prediction 391 --
15.7 Conclusions 391 --
References 392 --
16 Joint Analysis of EEG and fMRI 397 --
16.1 Fundamental Concepts 397 --
16.1.1 Blood Oxygenation Level Dependent 399 --
16.1.2 Popular fMRI Data Formats 400 --
16.1.3 Preprocessing of fMRI Data 401 --
16.1.4 Relation between EEG and fMRI 401 --
16.2 Model-Based Method for BOLD Detection 403 --
16.3 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG 405 --
16.3.1 Gradient Artefact Removal 405 --
16.3.2 Ballistocardiogram Artefact Removal 406 --
16.4 BOLD Detection in fMRI 413 --
16.4.1 Implementation of Different NMF Algorithms for BOLD Detection 414 --
16.4.2 BOLD Detection Experiments 416 --
16.5 Fusion of EEG and fMRI 419 --
16.5.1 Extraction of fMRI Time-Course from EEG 419 --
16.5.2 Fusion of EEG and fMRI, Blind Approach 241 --
16.5.3 Fusion of EEG and fMRI, Model-Based Approach 425 --
16.6 Application to Seizure Detection 425 --
16.7 Conclusions 427 --
References 427.
Responsibility: Saeid Sanei.
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In this book, the field of adaptive learning and processing is extended to arguably one of its most important contexts which is the understanding and analysis of brain signals. No attempt is made to  Read more...

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