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More About This Title Adaptive Processing of Brain Signals
English
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
English
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.
English
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
