Quantitative EEG Analysis Methods and Clinical Applications

Shanbao Tong Nitish V. Thakor

Editors

A R T E C H H O U S E BOSTON|LONDON ar techhouse.com

Contents

Foreword xiii

Preface xv

CHAPTER 1 Physiological Foundations of Quantitative EEG Analysis 1

1.1 Introduction 1 1.2 A Window on the Mind 3 1.3 Cortical Anatomy and Physiology Overview 4 1.4 Brain Sources 6 1.5 Scalp Potentials Generated by the Mesosources 9 1.6 The Average Reference 10 1.7 The Surface Laplacian 11 1.8 Dipole Layers: The Most Important Sources of EEGs 12 1.9 Alpha Rhythm Sources 14 1.10 Neural Networks, Cell Assemblies, and Field Theoretic Descriptions 17 1.11 Phase Locking 17 1.12 "Simple" Theories of Cortical Dynamics 18 1.13 Summary: Brain Volume Conduction Versus Brain Dynamics 20

References 20 Selected Bibliography 22

CHAPTER 2 Techniques of EEG Recording and Preprocessing 23

2.1 Properties of the EEG 23 2.1.1 Event-Related Potentials 23 2.1.2 Event-Related Oscillations 25 2.1.3 Event-Related Brain Dynamics 25

2.2 EEG Electrodes, Caps, and Amplifiers 26 2.2.1 EEG Electrode Types 26 2.2.2 Electrode Caps and Montages 30 2.2.3 EEG Signal and Amplifier Characteristics 31

2.3 EEG Recording and Artifact Removal Techniques 33 2.3.1 EEG Recording Techniques 33 2.3.2 EEG Artifacts 34 2.3.3 Artifact Removal Techniques 36

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VI Contents

2.4 Independent Components of Electroencephalographic Data 39 2.4.1 Independent Component Analysis 39 2.4.2 Applying ICA to EEG/ERP Signals 40 2.4.3 Artifact Removal Based on ICA 43 2.4.4 Decomposition of Event-Related EEG Dynamics Based on ICA 46 References 47

CHAPTER 3 Single-Channel EEG Analysis 51

3.1 Linear Analysis of EEGs 51 3.1.1 Classical Spectral Analysis of EEGs 52 3.1.2 Parametric Model of the EEG Time Series 59 3.1.3 Nonstationarity in EEG and Time-Frequency Analysis 63

3.2 Nonlinear Description of EEGs 73 3.2.1 Higher-Order Statistical Analysis of EEGs 75 3.2.2 Nonlinear Dynamic Measures of EEGs 81

3.3 Information Theory-Based Quantitative EEG Analysis 90 3.3.1 Information Theory in Neural Signal Processing 90 3.3.2 Estimating the Entropy of EEG Signals 92 3.3.3 Time-Dependent Entropy Analysis of EEG Signals 94 References 102

CHAPTER 4 Bivariable Analysis of EEG Signals 109

4.1 Cross-Correlation Function 111 4.2 Coherence Estimation 112 4.3 Mutual Information Analysis 114 4.4 Phase Synchronization 116 4.5 Conclusion 119

References 119

CHAPTER 5 Theory of the EEG Inverse Problem 121

5.1 Introduction 121 5.2 EEG Generation 122

5.2.1 The Electrophysiological and Neuroanatomical Basis of the EEG 122

5.2.2 The Equivalent Current Dipole 123 5.3 Localization of the Electrically Active Neurons as a Small Number

of "Hot Spots" 125 5.3.1 Single-Dipole Fitting 125 5.3.2 Multiple-Dipole Fitting 127

5.4 Discrete, Three-Dimensional Distributed Tomographic Methods 127 5.4.1 The Reference Electrode Problem 129 5.4.2 The Minimum Norm Inverse Solution 129 5.4.3 Low-Resolution Brain Electromagnetic Tomography 131 5.4.4 Dynamic Statistical Parametric Maps 132

VII

5.4.5 Standardized Low-Resolution Brain Electromagnetic Tomography 133

5.4.6 Exact Low-Resolution Brain Electromagnetic Tomography 134 5.4.7 Other Formulations and Methods 136

5.5 Selecting the Inverse Solution 136 References 137

CHAPTER 6 Epilepsy Detection and Monitoring 141

6.1 Epilepsy: Seizures, Causes, Classification, and Treatment 141 6.2 Epilepsy as a Dynamic Disease 144 6.3 Seizure Detection and Prediction 145 6.4 Univariate Time-Series Analysis 146

6.4.1 Short-Term Fourier Transform 146 6.4.2 Discrete Wavelet Transforms 148 6.4.3 Statistical Moments 150 6.4.4 Recurrence Time Statistics 151 6.4.5 Lyapunov Exponent 152

6.5 Multivariate Measures 154 6.5.1 Simple Synchronization Measure 154 6.5.2 Lag Synchronization 155

6.6 Principal Component Analysis 156 6.7 Correlation Structure 157 6.8 Multidimensional Probability Evolution 158 6.9 Self-Organizing Map 158 6.10 Support Vector Machine 158 6.11 Phase Correlation 159 6.12 Seizure Detection and Prediction 159 6.13 Performance of Seizure Detection/Prediction Schemes 160

6.13.1 Optimality Index 161 6.13.2 Specificity Rate 162

6.14 Closed-Loop Seizure Prevention Systems 162 6.15 Conclusion 163

References 165

CHAPTER 7 Monitoring Neurological Injury by qEEG 169

7.1 Introduction: Global Ischemic Brain Injury After Cardiac Arrest 169 7.1.1 Hypothermia Therapy and the Effects on Outcome After

Cardiac Arrest 170 7.2 Brain Injury Monitoring Using EEG 171 7.3 Entropy and Information Measures of EEG 173

7.3.1 Information Quantity 175 7.3.2 Subband Information Quantity 176

7.4 Experimental Methods 177 7.4.1 Experimental Model of CA, Resuscitation, and Neurological

Evaluation 178

VIII Contents

7.4.2 Therapeutic Hypothermia 179 7.5 Experimental Results 180

7.5.1 qEEG-IQ Analysis of Brain Recovery After Temperature Manipulation 181

7.5.2 qEEG-IQ Analysis of Brain Recovery After Immediate Versus Conventional Hypothermia 182

7.5.3 qEEG Markers Predict Survival and Functional Outcome 184 7.6 Discussion of the Results 187

References 188

CHAPTER 8 Quantitative EEG-Based Brain-Computer Interface 193

8.1 Introduction to the qEEG-Based Brain-Computer Interface 193 8.1.1 Quantitative EEG as a Noninvasive Link Between Brain and

Computer 193 8.1.2 Components of a qEEG-Based BCI System 194 8.1.3 Oscillatory EEG as a Robust BCI Signal 196

8.2 SSVEP-Based BCI 197 8.2.1 Physiological Background and BCI Paradigm 197 8.2.2 A Practical BCI System Based on SSVEP 199 8.2.3 Alternative Approaches and Related Issues 202

8.3 Sensorimotor Rhythm-Based BCI 205 8.3.1 Physiological Background and BCI Paradigm 205 8.3.2 Spatial Filter for SMR Feature Enhancing 207 8.3.3 Online Three-Class SMR-Based BCI 210 8.3.4 Alternative Approaches and Related Issues 215

8.4 Concluding Remarks 218 8.4.1 BCI as a Modulation and Demodulation System 218 8.4.2 System Design for Practical Applications 219 Acknowledgments 220 References 220

CHAPTER 9 EEG Signal Analysis in Anesthesia 225

9.1 Rationale for Monitoring EEG in the Operating Room 225 9.2 Nature of the OR Environment 229 9.3 Data Acquisition and Preprocessing for the OR 230

9.3.1 Amplifiers 230 9.3.2 Signal Processing 231

9.4 Time-Domain EEG Algorithms 233 9.4.1 Clinical Applications of Time-Domain Methods 235 9.4.2 Entropy 237

9.5 Frequency-Domain EEG Algorithms 239 9.5.1 Fast Fourier Transform 239 9.5.2 Mixed Algorithms: Bispectrum 245 9.5.3 Bispectral Index: Implementation 247 9.5.4 Bispectral Index: Clinical Results 250

Contents IX

9.6 Conclusions 251 References 251

CHAPTER 10 Quantitative Sleep Monitoring 257

10.1 Overview of Sleep Stages and Cycles 257 10.2 Sleep Architecture Definitions 259 10.3 Differential Amplifiers, Digital Polysomnography, Sensitivity,

and Filters 259 10.4 Introduction to EEG Terminology and Monitoring 261 10.5 EEG Monitoring Techniques 262 10.6 Eye Movement Recording 262 10.7 Electromyographic Recording 262 10.8 Sleep Stage Characteristics 264

10.8.1 Atypical Sleep Patterns 264 10.8.2 Sleep Staging in Infants and Children 265

10.9 Respiratory Monitoring 267 10.10 Adult Respiratory Definitions 268 10.11 Pediatric Respiratory Definitions 270 10.12 Leg Movement Monitoring 271 10.13 Polysomnography, Biocalibrations, and Technical Issues 272 10.14 Quantitative Polysomnography 273

10.14.1 EEG 273 10.14.2 EOG 276 10.14.3 EMG 278

10.15 Advanced EEG Monitoring 280 10.15.1 Wavelet Analysis 281 10.15.2 Matching Pursuit 282

10.16 Statistics of Sleep State Detection Schemes 282 10.16.1 M Binary Classification Problems 283 10.16.2 Contingency Table 284

10.17 Positive Airway Pressure Treatment for Obstructive Sleep Apnea 285 10.17.1 APAP with Forced Oscillations 285 10.17.2 Measurements for FOT 285 References 286

CHAPTER 11 EEG Signals in Psychiatry: Biomarkers for Depression Management 289

11.1 EEG in Psychiatry 289 11.1.1 Application of EEGs in Psychiatry: From Hans Berger

to qEEG 289 11.1.2 Challenges to Acceptance: What Do the Signals Mean? 290 11.1.3 Interpretive Frameworks to Relate qEEG to Other

Neurobiological Measures 291 11.2 qEEG Measures as Clinical Biomarkers in Psychiatry 293

11.2.1 Biomarkers in Clinical Medicine 293

Contents

11.2.2 Potential for the Use of Biomarkers in the Clinical Care of Psychiatric Patients 294

11.2.3 Pitfalls 302 11.2.4 Pragmatic Evaluation of Candidate Biomarkers 304

11.3 Research Applications of EEG to Examine Pathophysiology in Depression 305 11.3.1 Resting State or Task-Related Differences Between Depressed

and Healthy Subjects 305 11.3.2 Toward Physiological Endophenotypes 307

11.4 Conclusions 307 Acknowledgments 308 References 308

CHAPTER 12 Combining EEG and MRI Techniques 31 7

12.1 EEG and MRI 317 12.1.1 Coregistration 319 12.1.2 Volume Conductor Models 321 12.1.3 Source Space 323 12.1.4 Source Localization Techniques 327 12.1.5 Communication and Visualization of Results 329

12.2 Simultaneous EEG and fMRI 335 12.2.1 Introduction 335 12.2.2 Technical Challenges 336 12.2.3 Using fMRI to Study EEG Phenomena 341 12.2.4 EEG in Generation of Better Functional MR Images 348 12.2.5 The Inverse EEG Problem: fMRI Constrained EEG Source

Localization 349 12.2.6 Ongoing and Future Directions 349 Acknowledgments 350 References 350

v l l n l I L f\ I J

Cortical Functional Mapping by High-Resolution EEG 355

13.1 HREEG: An Overview 355 13.2 The Solution of the Linear Inverse Problem: The Head Models

and the Cortical Source Estimation 357 13.3 Frequency-Domain Analysis: Cortical Power Spectra Computation 360 13.4 Statistical Analysis: A Method to Assess Differences Between Brain

Activities During Different Experimental Tasks 361 13.5 Group Analysis: The Extraction of Common Features Within the

Population 365 13.6 Conclusions 366

References 366

CHAPTER 14

Cortical Function Mapping with Intracranial EEG 369

14.1 Strengths and Limitations of iEEG 369 14.2 Intracranial EEG Recording Methods 370 14.3 Localizing Cortical Function 372

14.3.1 Analysis of Phase-Locked iEEG Responses 372 14.3.2 Application of Phase-Locked iEEG Responses to Cortical

Function Mapping 373 14.3.3 Analysis of Nonphase-Locked Responses in iEEG 375 14.3.4 Application of Nonphase-Locked Responses to Cortical

Function Mapping 379 14.4 Cortical Network Dynamics 384

14.4.1 Analysis of Causality in Cortical Networks 385 14.4.2 Application of ERC to Cortical Function Mapping 389

14.5 Future Applications of iEEG 391 Acknowledgments 391 References 392

About the Editors 401

List of Contributors 403

Index 409