9780470686133

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 Saneiâ€™s first book with Wiley, EEG Signal Processing.

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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

8.8.3 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?

11.9 Other Neuroimaging Modalities used for Emotion Study

11.10 Applications

11.11 Concluding Remarks

References

CHAPTER 12 SLEEP AND SLEEP APNEA

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

14.3.1 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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Adaptive Processing of Brain Signals

Adaptive Processing of Brain Signals

0470686138

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