Adaptive processing of brain signals:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley
2013
|
| Schlagworte: | |
| Online-Zugang: | Cover Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIV, 454 S. Ill., graph. Darst. |
| ISBN: | 0470686138 9780470686133 9781118622131 |
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Datensatz im Suchindex
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| adam_text | Titel: Adaptive processing of brain signals
Autor: Sanei, Saeid
Jahr: 2013
Contents
Preface xiii
1 Brain Signals, Their Generation, Acquisition and Properties 1
1.1 Introduction I
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
vi
Contents
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 Transform 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
Contents
vii
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 ¿-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
viii
Contents
8.8 Application of Adaptive Filters 175
8.8.1 Use of Kaiman 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 Kaiman 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
Contents
ix
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
X
Contents
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 1CA-LORETA Approach 338
14.3.9 Partially Constrained BSS Method 340
14.3.10 Constrained Least-Squares Method for Localization ofP3a 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
Contents
xi
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 421
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
Index 433
|
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