Adaptive Processing of Brain Signals

Informationen zum Autor 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. Klappentext In this book, the field of adaptive learning and processing is extended to arguably one of its most important contexts which is the understanding and analysis of brain signals. No attempt is made to comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Recent developments in detection, estimation and separation of diagnostic cues from different modality neuroimaging systems are discussed.These include constrained nonlinear signal processing techniques which incorporate sparsity, nonstationarity, multimodal data, and multiway techniques.Key features:* Covers advanced and adaptive signal processing techniques for the processing of electroencephalography (EEG) and magneto-encephalography (MEG) signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI)* Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain* Puts a major emphasis on brain dynamics and how this can be evaluated for the assessment of brain activity in various states such as for brain-computer interfacing emotions and mental fatigue analysis* Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research Zusammenfassung In this book, the field of adaptive learning and processing is extended to arguably one of its most important contexts which is the understanding and analysis of brain signals. No attempt is made to comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Inhaltsverzeichnis Preface xiii 1 Brain Signals, Their Generation, Acquisition and Properties 1 1.1 Introduction 1 1.2 Historical Review of the Brain 1 1.3 Neural Activities 5 1.4 Action Potentials 5 1.5 EEG Generation 8 1.6 Brain Rhythms 10 1.7 EEG Recording and Measurement 14 1.8 Abnormal EEG Patterns 19 1.9 Aging 22 1.10 Mental Disorders 23 1.11 Memory and Content Retrieval 30 1.12 MEG Signals and Their Generation 32 1.13 Conclusions 32 References 33 2 Fundamentals of EEG Signal Processing 37 2.1 Introduction 37 2.2 Nonlinearity of the Medium 38 2.3 Nonstationarity 39 2.4 Signal Segmentation 40 2.5 Other Properties of Brain Signals 43 2.6 Conclusions 44 References 44 3 EEG Signal M...

List of contents

Preface xiii
 
1 Brain Signals, Their Generation, Acquisition and Properties 1
 
1.1 Introduction 1
 
1.2 Historical Review of the Brain 1
 
1.3 Neural Activities 5
 
1.4 Action Potentials 5
 
1.5 EEG Generation 8
 
1.6 Brain Rhythms 10
 
1.7 EEG Recording and Measurement 14
 
1.8 Abnormal EEG Patterns 19
 
1.9 Aging 22
 
1.10 Mental Disorders 23
 
1.11 Memory and Content Retrieval 30
 
1.12 MEG Signals and Their Generation 32
 
1.13 Conclusions 32
 
References 33
 
2 Fundamentals of EEG Signal Processing 37
 
2.1 Introduction 37
 
2.2 Nonlinearity of the Medium 38
 
2.3 Nonstationarity 39
 
2.4 Signal Segmentation 40
 
2.5 Other Properties of Brain Signals 43
 
2.6 Conclusions 44
 
References 44
 
3 EEG Signal Modelling 45
 
3.1 Physiological Modelling of EEG Generation 45
 
3.2 Mathematical Models 54
 
3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 61
 
3.4 Electronic Models 64
 
3.5 Dynamic Modelling of the Neuron Action Potential Threshold 68
 
3.6 Conclusions 68
 
References 68
 
4 Signal Transforms and Joint Time-Frequency Analysis 72
 
4.1 Introduction 72
 
4.2 Parametric Spectrum Estimation and Z-Transform 73
 
4.3 Time-Frequency Domain Transforms 74
 
4.4 Ambiguity Function and the Wigner-Ville Distribution 82
 
4.5 Hermite Transform 85
 
4.6 Conclusions 88
 
References 88
 
5 Chaos and Dynamical Analysis 90
 
5.1 Entropy 91
 
5.2 Kolmogorov Entropy 91
 
5.3 Lyapunov Exponents 92
 
5.4 Plotting the Attractor Dimensions from Time Series 93
 
5.5 Estimation of Lyapunov Exponents from Time Series 94
 
5.6 Approximate Entropy 98
 
5.7 Using Prediction Order 98
 
5.8 Conclusions 99
 
References 100
 
6 Classification and Clustering of Brain Signals 101
 
6.1 Introduction 101
 
6.2 Linear Discriminant Analysis 102
 
6.3 Support Vector Machines 103
 
6.4 k-Means Algorithm 109
 
6.5 Common Spatial Patterns 112
 
6.6 Conclusions 115
 
References 116
 
7 Blind and Semi-Blind Source Separation 118
 
7.1 Introduction 118
 
7.2 Singular Spectrum Analysis 119
 
7.3 Independent Component Analysis 121
 
7.4 Instantaneous BSS 125
 
7.5 Convolutive BSS 130
 
7.6 Sparse Component Analysis 133
 
7.7 Nonlinear BSS 134
 
7.8 Constrained BSS 135
 
7.9 Application of Constrained BSS; Example 136
 
7.10 Nonstationary BSS 137
 
7.11 Tensor Factorization for Underdetermined Source Separation 151
 
7.12 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 153
 
7.13 Separation of Correlated Sources via Tensor Factorization 153
 
7.14 Conclusions 154
 
References 154
 
8 Connectivity of Brain Regions 159
 
8.1 Introduction 159
 
8.2 Connectivity Through Coherency 161
 
8.3 Phase-Slope Index 163
 
8.4 Multivariate Directionality Estimation 163
 
8.5 Modelling the Connectivity by Structural Equation Modelling 166
 
8.6 EEG Hyper-Scanning and Inter-Subject Connectivity 168
 
8.7 State-Space Model for Estimation of Cortical Interactions 173
 
8.8 Application of Adaptive Filters 175
 
8.9 Tensor Factorization Approach 182
 
8.10 Conclusions 184
 
References 185
 
9 Detection

Product details

• Authors: S Sanei, Saeid Sanei, Saeid (Reader in Biomedical Signal Processi Sanei, Sanei Saeid
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 05.07.2013
• EAN: 9780470686133
• ISBN: 978-0-470-68613-3
• No. of pages: 472
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Neuronale Netze, Signalverarbeitung, TECHNOLOGY & ENGINEERING / Telecommunications, Neural Networks, Neurology and clinical neurophysiology, Signal Processing, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Electronics and communications engineering