ADAPTIVE PROCESSING OF BRAIN SIGNALS

Saeid Sanei Reader in Neurocomputing University of Surrey, UK

WILEY

Contents

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

1.7.1 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 Psychiatrie Disorders 28 1.10.4 Externat 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 1ntegrate-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 ofthe 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 1ntegrated 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 lntroduction 72 4.2 Parametric Spectrum Estimation and Z-Transform 73 4.3 Time-Frequency Domain Transforms 74

4.3.1 Short-Time Fourier Transfonn 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

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 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 lnteractions 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 ofEvent-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 Wavefonns 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 Approachfor 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. l 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

11 Emotion Encoding, Regulation and Control 11.1 Theories and Emotion Classification 11.2 The Effects of Emotions 11.3 Psychology and Psychophysiology of Emotion 11.4 Emotion Regulation

243

245 246 248 251 252

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 Emc;tion Study 264 11.10 Applications 267 11.11 Conclusions 268

References 268

12 Sleep and Sleep Apnoea 274 12.1 lntroduction 274 12.2 Stages of Sleep 275

12.2.1 NREM Sleep 275 12.2.2 REM Sleep 277

12.3 The lnftuence 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 Drain-Computer Interfacing 295 13.1 lntroduction 295 13.2 State of the Art in BCI 296 13.3 BCI-Related EEG Features 300

13.3.1 Readiness Potential and lts Detection 300 13.3.2 ERDandERS 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 Co1\tent&

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 ICAMethod 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 Methodfor Localization of P3a and P3b 341 14.3.11 Spatial Notch Filtering Approach 342 14.3.12 Deflation Beamforming Approachfor EEG!MEG Multiple Source

Localization 347 14.3.13 Hybrid Beamforming - Particle Filtering 351

14.4 Detennination of the Number of Sources from the EEG/MEG Signals 353 14.5 Conclusions 355

References 356

15 Seizure and Epilepsy 360 15.1 lntroduction 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

coments xi

15.7 Conclusions 391 References 392

16 Joint Analysis of EEG and tMRI 397 16.l Fundamental Concepts 397

16.1.1 Blood Oxygenation Level Dependent 399 16.1.2 Popular fMRl Data Formats 400 16.1.3 Preprocessing offMRI 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.l Extraction ofjMRI Time-Coursefrom EEG 419 16.5.2 Fusion of EEG and jMRI, Blind Approach 421 16.5.3 Fusion of EEG and jMRI, Model-Based Approach 425

16.6 Application to Seizure Detection 425 16.7 Conclusions 427

References 427

Index 433