Adaptive Processing of Brain Signals

ISBN-13: 9780470686133 / Angielski / Twarda / 2013 / 472 str.

Saeid Sanei

Opis

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 comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Recent developments in detection, estimation and separation of diagnostic cues from different modality neuroimaging systems are discussed. These include constrained nonlinear signal processing techniques which incorporate sparsity, nonstationarity, multimodal data, and multiway techniques. Key features:

• Covers advanced and adaptive signal processing techniques for the processing of electroencephalography (EEG) and magneto-encephalography (MEG) signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI)
• Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain
• Puts a major emphasis on brain dynamics and how this can be evaluated for the assessment of brain activity in various states such as for brain-computer interfacing emotions and mental fatigue analysis
• Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research

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.

Spis treści

Preface xiii

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

1.8 Abnormal EEG Patterns 19

1.9 Aging 22

1.10 Mental Disorders 23

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

3.2 Mathematical Models 54

3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 61

3.4 Electronic Models 64

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

4.4 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

5.6 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

7.3 Independent Component Analysis 121

7.4 Instantaneous BSS 125

7.5 Convolutive BSS 130

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

7.11 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

8.5 Modelling the Connectivity by Structural Equation Modelling 166

8.6 EEG Hyper–Scanning and Inter–Subject Connectivity 168

8.7 State–Space Model for Estimation of Cortical Interactions 173

8.8 Application of Adaptive Filters 175

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

9.2 Detection, Separation, and Classification of P300 Signals 192

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

10.3 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

11.7 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

12.3 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

12.7 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

13.4 Major Problems in BCI 303

13.5 Multidimensional EEG Decomposition 306

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

14.3 Most Popular Brain Source Localization Approaches 329

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

15.4 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

16.2 Model–Based Method for BOLD Detection 403

16.3 Simultaneous EEG–fMRI Recording: Artefact Removal from EEG 405

16.4 BOLD Detection in fMRI 413

16.5 Fusion of EEG and fMRI 419

16.6 Application to Seizure Detection 425

16.7 Conclusions 427

References 427

Index 433

O autorze

Dr Saeid Sanei, Reader in Biomedical Signal Processing and Deputy Head of Computing Department, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom.

Dr Sanei received his PhD from Imperial College of Science, Technology and Medicine, London, in Biomedical Signal and Image Processing in 1991. He has made a major contribution to Electroencephalogram (EEG) analysis; blind source separation, sparse component analysis and compressive sensing; parallel factor analysis and tensor factorization; particle filtering; chaos and time series analysis; support vector machines; hidden Markov models; and brain computer interfacing (BCI).He has published over 180 papers in refereed journals and conference proceedings, and a book on EEG Signal Processing. He has served as an editor, member of the technical committee, and reviewer for a number of journals and conferences, and has recently been selected as the Biomedical Signal Processing Track Chair for the IEEE Engineering in Medicine and Biology Conference 2009. His international collaborations involve both educational and industrial organizations, including the RIKEN Brain Science Research Institute in Japan. He also teaches extensively at both undergraduate and postgraduate level.

Blurb

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 comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Recent developments in detection, estimation and separation of diagnostic cues from different modality neuroimaging systems are discussed. These include constrained nonlinear signal processing techniques which incorporate sparsity, nonstationarity, multimodal data, and multiway techniques.

Key features:

• Covers advanced and adaptive signal processing techniques for the processing of electroencephalography (EEG) and magneto–encephalography (MEG) signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI)
• Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain
• Puts a major emphasis on brain dynamics and how this can be evaluated for the assessment of brain activity in various states such as for brain–computer interfacing emotions and mental fatigue analysis
• Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research