Adaptive Processing of Brain Signals
, by Sanei, Saeid
- ISBN: 9780470686133 | 0470686138
- Cover: Hardcover
- Copyright: 7/15/2013
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. It will provide advanced tools for the detection, monitoring, separation, localizing and understanding of brain functional, anatomical, and physiological abnormalities. The focus will be on advanced and adaptive signal processing techniques for the processing of electroencephalography and magneto-encephalography signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI). Multimodal processing of brain signals, the new focus for brain signal research, will also be explored. The book covers the broad remit of neuro-imaging, ensuring comprehensive coverage of all issues related to brain signal processing. Topics such as mental fatigue, brain connectivity and new recording techniques will also be covered. This book will be a progression/follow on from Dr Sanei’s first book with Wiley, EEG Signal Processing.
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.
CHAPTER 1 BRAIN SIGNALS, THEIR GENERATION, ACQUISITION AND PROPERTIES
1.1 Introduction
1.2 Historical Review of the Brain
1.3 Neural Activities
1.4 Action Potentials
1.5 EEG Generation
1.6 Brain Rhythms
1.7 EEG Recording and Measurement
1.7.1 Conventional EEG Electrode Positioning
1.7.2 Conditioning the Signals
1.8 Abnormal EEG Patterns
1.9 Aging
1.10 Mental Disorders
1.10.1 Dementia
1.10.2 Epileptic Seizure and Nonepileptic Attacks
1.10.3 Psychiatric Disorders
1.10.4 External Effects
1.11 Memory and Content Retrieval
1.12 MEG Signals and their Generation
1.13 Summary and Conclusions
References
CHAPTER 2 FUNDAMENTALS OF EEG SIGNAL PROCESSING
2.1 Introduction
2.2 Nonlinearity of the Medium
2.3 Nonstationarity
2.4 Signal Segmentation
2.5 Other properties of brain signals
2.6 Summary and Conclusions
References
CHAPTER 3 EEG SIGNAL MODELLING
3.1 Physiological Modelling of EEG generation:
3.1.1 Integrate and Fire Models
3.1.2 Phase-Coupled Models
3.1.3 Hodgkin and Huxley Model
3.2 Mathematical Models
3.2.1 Linear Models
3.2.1.1 Prediction method
3.2.1.2 Prony’s method
3.2.2 Nonlinear Modelling
3.2.3 Gaussian Mixture Model
3.3 Generating EEG Signals Based on Modelling the Neuronal Activities
3.4 Electronic Models
3.4.1 Models Describing the Function of the Membrane
3.4.1.1 Lewis Membrane Model
3.4.1.2 Roy Membrane Model
3.4.2 Models Describing the Function of Neuron
3.4.2.1 Lewis Neuron Model
3.4.2.2 Harmon Neuron Model
3.4.3 A Model Describing the Propagation of Action Pulse in Axon
3.4.4 Integrated Circuit Realizations
3.5 Dynamic Modelling of Neuron Action Potential Threshold
3.6 Summary and Conclusion
References
CHAPTER 4 SIGNAL TRANSFORMS AND JOINT TIME-FREQUENCY ANALYSIS
4.1 Introduction
4.2 Parametric Spectrum Estimation and Z-Transform
4.3 Time – Frequency Domain Transforms
4.3.1 Short Time Fourier Transform
4.3.2 Wavelet Transform
4.3.2.1 Continuous wavelet transform
4.3.2.2 Examples of continuous wavelets
4.3.2.3 Discrete time wavelet transform
4.3.3 Multiresolution analysis
4.3.3.1 Wavelet transform using Fourier transform
4.3.3.2 Reconstruction
4.4 Ambiguity Function and the Wigner-Ville Distribution
4.5 Hermite Transform
4.6 Concluding Remarks
References
CHAPER 5 CHAOS AND DYNAMICAL ANALYSIS
5.1 Entropy
5.2 Kolmogorov Entropy
5.3 Lyapunov Exponents
2.4 Plotting the Attractor Dimensions from Time Series
5.5 Estimation of Lyapunov Exponents from Time Series
5.5.1 Optimum time delay
5.5.2 Optimum embedding dimension
5.6 Approximate Entropy
5.7 Using Prediction Order
5.8 Conclusions
References
CHAPTER 6 CLASSIFICATION AND CLUSTERING OF BRAIN SIGNALS
6.1 Introduction
6.2 Linear Discriminant Analysis
6.3 Support Vector Machines
6.4 K-means Algorithm
6.5 Common Spatial Patterns
6.6 Conclusions
References
CHAPTER 7 BLIND AND SEMI-BLIND SOURCE SEPARATION
7.1 Introduction
7.2 Singular Spectrum Analysis
7.2.1 Decomposition
7.2.2 Reconstruction
7.3 Independent Component Analysis
7.4 Instantaneous BSS
7.5 Convolutive BSS
7.5.1 General Applications
7.5.2 Application of Convolutive BSS to EEG
7.6 Sparse Component Analysis
7.7 Nonlinear BSS
7.8 Constrained BSS
7.9 Application of Constrained BSS; Example
7.10 Nonstationary Blind Source Separation
7.10.1 Tensor Factorization for BSS
7.10.2 Solving BSS of Nonstationary Sources using Tensor Factorization
7.11 Tensor Factorization for Underdetermined Source Separation
7.12 Tensor Factorization for Separation of Convolutive Mixtures in Time Domain
7.13 Separation of Correlated Sources via Tensor Factorization
7.14 Summary and Conclusions
References
CHAPTER 8 CONNECTIVITY OF BRAIN REGIONS
8.1 Introduction
8.2 Connectivity through Coherency
8.3 Phase-Slope Index
8.4 Multivariate Directionality Estimation
8.4.1 Directed Transfer Function
8.5Modelling the Connectivity by Structural Equation Modelling
8.6 EEG Hyper-scanning and Inter-subject Connectivity
8.6.1 Objectives
8.6.2 Technological Relevance
8.7 State-Space Model for Estimation of Cortical Interactions
8.8.1 Use of Kalman Filter
8.8.2 Task-related Adaptive Connectivity
8.8.3 Diffusion Adaptation
8.9 Tensor Factorization Approach
8.10 Summary and Conclusions
References
CHAPTER 9 DETECTION AND TRACKING OF EVENT RELATED POTENTIALS
9.1 ERP Generation and Types
9.1.1 P300 and its Subcomponents
9.2 Detection, Separation, and Classification of P300 Signals
9.2.1 Using ICA
9.2.2 Estimation of Single Trial Brain Responses by Modelling the ERP Waveforms
9.2.3 ERP Source Tracking in Time
9.2.4 Time-Frequency Domain Analysis
9.2.5 Application of Kalman Filter
9.2.6 Particle Filtering and its Application to ERP Tracking
9.2.7 Variational Bayes Method
9.2.8 Prony’s Approach for Detection of P300 Signals
9.2.9 Adaptive Time-Frequency Methods
9.3 Brain Activity Assessment Using ERP
9.4 Application of P300 to BCI
9.5 Summary and Conclusions
References
CHAPTER 10 MENTAL FATIGUE
10.1 Introduction
10.2 Measurement of Brain Synchronization and Coherency
10.2.1 Linear Measure of Synchronization
10.2.2 Nonlinear Measure of Synchronization
10.3 Evaluation of ERP for Mental Fatigue
10.4 Separation of P3a and P3b
10.5 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm
10.6 Conclusions
REFERENCES
CHAPTER 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
11.5 Emotion-Provoking Stimuli
11.6 Change in the ERP and Normal Brain Rhythms
11.6.1 ERP and Emotion:
11.6.2 Changes of Normal Brain Waves with Emotion:
11.7 Perception of Odours and Emotion: why are they related?
11.8 Emotion-related Brain Signal Processing
11.9 Other Neuroimaging Modalities used for Emotion Study
11.10 Applications
11.11 Concluding Remarks
References
CHAPTER 12 SLEEP AND SLEEP APNEA
12.1 Introduction
12.2 Stages of Sleep
12.2.1 NREM Sleep
12.2.2 REM Sleep
12.3 The Influence of Circadian Rhythms
12.4 Sleep Deprivation
12.5 Psychological Effects
12.6 Detection and Monitoring of Brain Abnormalities during Sleep by EEG Analysis
12.6.1 Analysis of Sleep Apnea
12.6.2 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation
12.6.3 Application of Matching Pursuit
12.6.4 Detection of Normal Rhythms and Spindles Using Higher Order Statistics
12.6.5 Application of Neural Networks
12.6.6 Model-Based Analysis
12.6.7 Hybrid Methods
12.8 Sleep Disorders of Neonates
12.9 Dreams and Nightmares
12.10 Concluding Remarks
References
CHAPTER 13 BRAIN-COMPUTER INTERFACING
13.1 Introduction
13.2 State of the Art in BCI
13.3 BCI Related EEG Features
13.3.1 Readiness Potential and its Detection
13.3.2 ERD and ERS
13.3.3 Transient Beta Activity after the Movement
13.3.4 Gamma Band Oscillations
13.3.5 Long Delta Activity
13.4 Major Problems in BCI
13.4.1 Pre-processing of the EEGs
13.5 Multi-Dimensional EEG Decomposition
13.5.1 Space-Time-Frequency Method
13.5.2 Parallel Factor Analysis
13.6 Detection and Separation of ERP Signals
13.7 Estimation of Cortical Connectivity
13.8 Application of Common Spatial Patterns
13.9 Multiclass Brain Computer Interfacing
13.10 Cell-Cultured BCI
13.11 Summary and Conclusions
References
CHAPTER 14 EEG AND MEG SOURCE LOCALIZATION
14.1 Introduction
14.2 General Approaches to Source Localization
14.2.1 Dipole Assumption
14.3 Most Popular Brain Source Localization Approaches
14.3.1 ICA Method
14.3.2 MUSIC Algorithm
14.3.3 LORETA Algorithm
14.3.4 FOCUSS Algorithm
14.3.5 Standardised LORETA
14.3.6 Other Weighted Minimum Norm Solutions
14.3.7 Evaluation Indices
14.3.8 Joint ICA-LORETA Approach
14.3.9 Partially Constrained BSS Method
14.3.10 Constrained Least Squares Method for localization of P3a and P3b
14.3.11 Spatial Notch Filtering Approach
14.3.12 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization
14.3.13 Hybrid Beamforming – Particle Filtering
14.4 Determination of the Number of Sources from the EEG/MEG Signals
14.5 Summary and Conclusions
References
CHAPTER 15 SEIZURE AND EPILEPSY
15.1 Introduction
15.2 Types of Epilepsy
15.3 Seizure Detection
15.3.1 Adult Seizure Detection
15.3.2 Detection of Neonate Seizure
15.4 Chaotic Behaviour of EEG Sources
15.5 Predictability of Seizure from EEG
15.6 Fusion of EEG – fMRI data for Seizure Detection and Prediction
15.7 Summary and Conclusions
References
CHAPTER 16 JOINT ANALYSIS OF EEG AND FMRI
16.1 Fundamental Concepts
16.1.1 Blood oxygenation level dependent
16.1.2 Popular fMRI Data Formats
16.1.3 Pre-processing of fMRI Data
16.1.4 Relation Between EEG and fMRI
16.2 Model-based Method for BOLD detection
16.3 Simultaneous EEG-fMRI Recording: Artifact Removal from EEG
16.3.1 Gradient artifact removal
16.3.2. Ballistocardiogram Artifact Removal
16.4 BOLD Detection in fMRI
16.4.1 Implementation of Different NMF Algorithms for BOLD detection
16.4.2 BOLD Detection Experiments
16.5 Fusion of EEG and fMRI
16.5.1 Extraction of fMRI Time-Course from EEG
16.5.2 Fusion of EEG and fMRI; Blind Approach
16.5.3 Fusion of EEG and fMRI; Model-based Approach
16.6 Application to Seizure Detection
16.7 Concluding Remarks
References
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