Quantitative EEG Analysis Methods and Applications

Name: Quantitative EEG Analysis Methods and Applications
Brand: Artech House
Price: 148.99 EUR
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Shanbao Tong(Editor)

Artech House (Publisher)

1st Edition

Published on 1. January 2009

300 pages

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978-1-59693-205-0 (ISBN)

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Quantitative EEG Analysis Methods and Clinical Applications
Contents
Foreword
Preface
Chapter 1: Physiological Foundations of Quantitative EEG Analysis
1.1 Introduction
1.2 A Window on the Mind
1.3 Cortical Anatomy and Physiology Overview
1.4 Brain Sources
1.5 Scalp Potentials Generated by the Mesosources
1.6 The Average Reference
1.7 The Surface Laplacian
1.8 Dipole Layers: The Most Important Sources of EEGs
1.9 Alpha Rhythm Sources
1.10 Neural Networks, Cell Assemblies, and Field Theoretic Descriptions
1.11 Phase Locking
1.12 "Simple" Theories of Cortical Dynamics
1.13 Summary: Brain Volume Conduction Versus Brain Dynamics
References
Selected Bibliography
Chapter 2: Techniques of EEG Recording and Preprocessing
2.1 Properties of the EEG
2.1.1 Event-Related Potentials
2.1.2 Event-Related Oscillations
2.1.3 Event-Related Brain Dynamics
2.2 EEG Electrodes, Caps, and Amplifiers
2.2.1 EEG Electrode Types
2.2.2 Electrode Caps and Montages
2.2.3 EEG Signal and Amplifier Characteristics
2.3 EEG Recording and Artifact Removal Techniques
2.3.1 EEG Recording Techniques
2.3.2 EEG Artifacts
2.3.3 Artifact Removal Techniques
2.4 Independent Components of Electroencephalographic Data
2.4.1 Independent Component Analysis
2.4.2 Applying ICA to EEG/ERP Signals
2.4.3 Artifact Removal Based on ICA
2.4.4 Decomposition of Event-Related EEG Dynamics Based on ICA
References
Chapter 3: Single-Channel EEG Analysis
3.1 Linear Analysis of EEGs
3.1.1 Classical Spectral Analysis of EEGs
3.1.2 Parametric Model of the EEG Time Series
3.1.3 Nonstationarity in EEG and Time-Frequency Analysis
3.2 Nonlinear Description of EEGs
3.2.1 Higher-Order Statistical Analysis of EEGs
3.2.2 Nonlinear Dynamic Measures of EEGs
3.3 Information Theory-Based Quantitative EEG Analysis
3.3.1 Information Theory in Neural Signal Processing
3.3.2 Estimating the Entropy of EEG Signals
3.3.3 Time-Dependent Entropy Analysis of EEG Signals
References
Chapter 4: Bivariable Analysis of EEG Signals
4.1 Cross-Correlation Function
4.2 Coherence Estimation
4.3 Mutual Information Analysis
4.4 Phase Synchronization
4.5 Conclusion
References
Chapter 5: Theory of the EEG Inverse Problem
5.1 Introduction
5.2 EEG Generation
5.2.1 The Electrophysiological and Neuroanatomical Basis of the EEG
5.2.2 The Equivalent Current Dipole
5.3 Localization of the Electrically Active Neurons as a Small Number of "Hot Spots"
5.3.1 Single-Dipole Fitting
5.3.2 Multiple-Dipole Fitting
5.4 Discrete, Three-Dimensional Distributed Tomographic Methods
5.4.1 The Reference Electrode Problem
5.4.2 The Minimum Norm Inverse Solution
5.4.3 Low-Resolution Brain Electromagnetic Tomography
5.4.4 Dynamic Statistical Parametric Maps
5.4.5 Standardized Low-Resolution Brain Electromagnetic Tomography
5.4.6 Exact Low-Resolution Brain Electromagnetic Tomography
5.4.7 Other Formulations and Methods
5.5 Selecting the Inverse Solution
References
Chapter 6: Epilepsy Detection and Monitoring
6.1 Epilepsy: Seizures, Causes, Classification, and Treatment
6.2 Epilepsy as a Dynamic Disease
6.3 Seizure Detection and Prediction
6.4 Univariate Time-Series Analysis
6.4.1 Short-Term Fourier Transform
6.4.2 Discrete Wavelet Transforms
6.4.3 Statistical Moments
6.4.4 Recurrence Time Statistics
6.4.5 Lyapunov Exponent
6.5 Multivariate Measures
6.5.1 Simple Synchronization Measure
6.5.2 Lag Synchronization
6.6 Principal Component Analysis
6.7 Correlation Structure
6.8 Multidimensional Probability Evolution
6.9 Self-Organizing Map
6.10 Support Vector Machine
6.11 Phase Correlation
6.12 Seizure Detection and Prediction
6.13 Performance of Seizure Detection/Prediction Schemes
6.13.1 Optimality Index
6.13.2 Specificity Rate
6.14 Closed-Loop Seizure Prevention Systems
6.15 Conclusion
References
Chapter 7: Monitoring Neurological Injury by qEEG
7.1 Introduction: Global Ischemic Brain Injury After Cardiac Arrest
7.1.1 Hypothermia Therapy and the Effects on Outcome After Cardiac Arrest
7.2 Brain Injury Monitoring Using EEG
7.3 Entropy and Information Measures of EEG
7.3.1 Information Quantity
7.3.2 Subband Information Quantity
7.4 Experimental Methods
7.4.1 Experimental Model of CA, Resuscitation, and Neurological Evaluation
7.4.2 Therapeutic Hypothermia
7.5 Experimental Results
7.5.1 qEEG-IQ Analysis of Brain Recovery After Temperature Manipulation
7.5.2 qEEG-IQ Analysis of Brain Recovery After Immediate Versus Conventional Hypothermia
7.5.3 qEEG Markers Predict Survival and Functional Outcome
7.6 Discussion of the Results
References
Chapter 8: Quantitative EEG-Based Brain-Computer Interface
8.1 Introduction to the qEEG-Based Brain-Computer Interface
8.1.1 Quantitative EEG as a Noninvasive Link Between Brain and Computer
8.1.2 Components of a qEEG-Based BCI System
8.1.3 Oscillatory EEG as a Robust BCI Signal
8.2 SSVEP-Based BCI
8.2.1 Physiological Background and BCI Paradigm
8.2.2 A Practical BCI System Based on SSVEP
8.2.3 Alternative Approaches and Related Issues
8.3 Sensorimotor Rhythm-Based BCI
8.3.1 Physiological Background and BCI Paradigm
8.3.2 Spatial Filter for SMR Feature Enhancing
8.3.3 Online Three-Class SMR-Based BCI
8.3.4 Alternative Approaches and Related Issues
8.4 Concluding Remarks
8.4.1 BCI as a Modulation and Demodulation System
8.4.2 System Design for Practical Applications
Acknowledgments
References
Chapter 9: EEG Signal Analysis in Anesthesia
9.1 Rationale for Monitoring EEG in the Operating Room
9.2 Nature of the OR Environment
9.3 Data Acquisition and Preprocessing for the OR
9.3.1 Amplifiers
9.3.2 Signal Processing
9.4 Time-Domain EEG Algorithms
9.4.1 Clinical Applications of Time-Domain Methods
9.4.2 Entropy
9.5 Frequency-Domain EEG Algorithms
9.5.1 Fast Fourier Transform
9.5.2 Mixed Algorithms: Bispectrum
9.5.3 Bispectral Index: Implementation
9.5.4 Bispectral Index: Clinical Results
9.6 Conclusions
References
Chapter 10: Quantitative Sleep Monitoring
10.1 Overview of Sleep Stages and Cycles
10.2 Sleep Architecture Definitions
10.3 Differential Amplifiers, Digital Polysomnography, Sensitivity, and Filters
10.4 Introduction to EEG Terminology and Monitoring
10.5 EEG Monitoring Techniques
10.6 Eye Movement Recording
10.7 Electromyographic Recording
10.8 Sleep Stage Characteristics
10.8.1 Atypical Sleep Patterns
10.8.2 Sleep Staging in Infants and Children
10.9 Respiratory Monitoring
10.10 Adult Respiratory Definitions
10.11 Pediatric Respiratory Definitions
10.12 Leg Movement Monitoring
10.13 Polysomnography, Biocalibrations, and Technical Issues
10.14 Quantitative Polysomnography
10.14.1 EEG
10.14.2 EOG
10.14.3 EMG
10.15 Advanced EEG Monitoring
10.15.1 Wavelet Analysis
10.15.2 Matching Pursuit
10.16 Statistics of Sleep State Detection Schemes
10.16.1 M Binary Classification Problems
10.16.2 Contingency Table
10.17 Positive Airway Pressure Treatment for Obstructive Sleep Apnea
10.17.1 APAP with Forced Oscillations
10.17.2 Measurements for FOT
References
Chapter 11: EEG Signals in Psychiatry: Biomarkers for Depression Management
11.1 EEG in Psychiatry
11.1.1 Application of EEGs in Psychiatry: From Hans Berger to qEEG
11.1.2 Challenges to Acceptance: What Do the Signals Mean?
11.1.3 Interpretive Frameworks to Relate qEEG to Other Neurobiological Measures
11.2 qEEG Measures as Clinical Biomarkers in Psychiatry
11.2.1 Biomarkers in Clinical Medicine
11.2.2 Potential for the Use of Biomarkers in the Clinical Care of Psychiatric Patients
11.2.3 Pitfalls
11.3 Research Applications of EEG to Examine Pathophysiology in Depression
11.3.1 Resting State or Task-Related Differences Between Depressed and Healthy Subjects
11.3.2 Toward Physiological Endophenotypes
11.4 Conclusions
Acknowledgments
References
Chapter 12: Combining EEG and MRI Techniques
12.1 EEG and MRI
12.1.1 Coregistration
12.1.2 Volume Conductor Models
12.1.3 Source Space
12.1.4 Source Localization Techniques
12.1.5 Communication and Visualization of Results
12.2 Simultaneous EEG and fMRI
12.2.1 Introduction
12.2.2 Technical Challenges
12.2.3 Using fMRI to Study EEG Phenomena
12.2.4 EEG in Generation of Better Functional MR Images
12.2.5 The Inverse EEG Problem: fMRI Constrained EEG Source Localization
12.2.6 Ongoing and Future Directions
Acknowledgments
References
Chapter 13: Cortical Functional Mapping by High-Resolution EEG
13.1 HREEG: An Overview
13.2 The Solution of the Linear Inverse Problem: The Head Modelsand the Cortical Source Estimation
13.3 Frequency-Domain Analysis: Cortical Power Spectra Computation
13.4 Statistical Analysis: A Method to Assess Differences Between Brain Activities During Different Experimental Tasks
13.5 Group Analysis: The Extraction of Common Features Within the Population
13.6 Conclusions
References
Chapter 14: Cortical Function Mapping with Intracranial EEG
14.1 Strengths and Limitations of iEEG
14.2 Intracranial EEG Recording Methods
14.3 Localizing Cortical Function
14.3.1 Analysis of Phase-Locked iEEG Responses
14.3.2 Application of Phase-Locked iEEG Responses to Cortical Function Mapping
14.3.3 Analysis of Nonphase-Locked Responses in iEEG
14.3.4 Application of Nonphase-Locked Responses to Cortical Function Mapping
14.4 Cortical Network Dynamics
14.4.1 Analysis of Causality in Cortical Networks
14.4.2 Application of ERC to Cortical Function Mapping
14.5 Future Applications of iEEG
Acknowledgments
References
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Quantitative EEG Analysis Methods and Applications

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