Adaptive processing of brain signals /

Mostrar otras versiones (1)

"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, separati...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Sanei, Saeid
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Chichester, West Sussex, United Kingdom : Wiley, 2013.
Temas:
Neural networks (Neurobiology)
Brain > Physiology.
Signal processing > Digital techniques.
Electroencephalography.
Magnetoencephalography.
Neurobiology.
Brain > physiology
Signal Processing, Computer-Assisted
Electroencephalography
Magnetoencephalography
Neurobiology
Réseaux neuronaux (Neurobiologie)
Cerveau > Physiologie.
Traitement du signal > Techniques numériques.
Électroencéphalographie.
Magnétoencéphalographie.
Neurobiologie.
SCIENCE > Waves & Wave Mechanics.
SOCIAL SCIENCE > Anthropology > Physical.
Brain > Physiology
Signal processing > Digital techniques
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

Ejemplares similares