Name: Adaptive Processing Of Brain Signals
Price: 128350.00 CLP
Availability: InStock
Author: Saeid Sanei
ISBN: 9780470686133

portada Adaptive Processing Of Brain Signals

Formato

Libro Físico

Editorial

Wiley

Autor

Saeid Sanei

Tema

Signal Processing

N° páginas

472

ISBN

0470686138

ISBN13

9780470686133

Categorías

Mecánica ondulatoria

Adaptive Processing Of Brain Signals

Saeid Sanei (Autor) · Wiley · Libro Físico

Libro Físico

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Reseña del libro "Adaptive Processing Of Brain Signals"

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 Pronys 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 Pronys 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