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<p>Preface xiii</p> <p><b>1 Brain Signals, Their Generation, Acquisition and Properties 1</b></p> <p>1.1 Introduction 1</p> <p>1.2 Historical Review of the Brain 1</p> <p>1.3 Neural Activities 5</p> <p>1.4 Action Potentials 5</p> <p>1.5 EEG Generation 8</p> <p>1.6 Brain Rhythms 10</p> <p>1.7 EEG Recording and Measurement 14</p> <p>1.8 Abnormal EEG Patterns 19</p> <p>1.9 Aging 22</p> <p>1.10 Mental Disorders 23</p> <p>1.11 Memory and Content Retrieval 30</p> <p>1.12 MEG Signals and Their Generation 32</p> <p>1.13 Conclusions 32</p> <p>References 33</p> <p><b>2 Fundamentals of EEG Signal Processing 37</b></p> <p>2.1 Introduction 37</p> <p>2.2 Nonlinearity of the Medium 38</p> <p>2.3 Nonstationarity 39</p> <p>2.4 Signal Segmentation 40</p> <p>2.5 Other Properties of Brain Signals 43</p> <p>2.6 Conclusions 44</p> <p>References 44</p> <p><b>3 EEG Signal Modelling 45</b></p> <p>3.1 Physiological Modelling of EEG Generation 45</p> <p>3.2 Mathematical Models 54</p> <p>3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 61</p> <p>3.4 Electronic Models 64</p> <p>3.5 Dynamic Modelling of the Neuron Action Potential Threshold 68</p> <p>3.6 Conclusions 68</p> <p>References 68</p> <p><b>4 Signal Transforms and Joint Time–Frequency Analysis 72</b></p> <p>4.1 Introduction 72</p> <p>4.2 Parametric Spectrum Estimation and Z-Transform 73</p> <p>4.3 Time–Frequency Domain Transforms 74</p> <p>4.4 Ambiguity Function and the Wigner–Ville Distribution 82</p> <p>4.5 Hermite Transform 85</p> <p>4.6 Conclusions 88</p> <p>References 88</p> <p><b>5 Chaos and Dynamical Analysis 90</b></p> <p>5.1 Entropy 91</p> <p>5.2 Kolmogorov Entropy 91</p> <p>5.3 Lyapunov Exponents 92</p> <p>5.4 Plotting the Attractor Dimensions from Time Series 93</p> <p>5.5 Estimation of Lyapunov Exponents from Time Series 94</p> <p>5.6 Approximate Entropy 98</p> <p>5.7 Using Prediction Order 98</p> <p>5.8 Conclusions 99</p> <p>References 100</p> <p><b>6 Classification and Clustering of Brain Signals 101</b></p> <p>6.1 Introduction 101</p> <p>6.2 Linear Discriminant Analysis 102</p> <p>6.3 Support Vector Machines 103</p> <p>6.4 k-Means Algorithm 109</p> <p>6.5 Common Spatial Patterns 112</p> <p>6.6 Conclusions 115</p> <p>References 116</p> <p><b>7 Blind and Semi-Blind Source Separation 118</b></p> <p>7.1 Introduction 118</p> <p>7.2 Singular Spectrum Analysis 119</p> <p>7.3 Independent Component Analysis 121</p> <p>7.4 Instantaneous BSS 125</p> <p>7.5 Convolutive BSS 130</p> <p>7.6 Sparse Component Analysis 133</p> <p>7.7 Nonlinear BSS 134</p> <p>7.8 Constrained BSS 135</p> <p>7.9 Application of Constrained BSS; Example 136</p> <p>7.10 Nonstationary BSS 137</p> <p>7.11 Tensor Factorization for Underdetermined Source Separation 151</p> <p>7.12 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 153</p> <p>7.13 Separation of Correlated Sources via Tensor Factorization 153</p> <p>7.14 Conclusions 154</p> <p>References 154</p> <p><b>8 Connectivity of Brain Regions 159</b></p> <p>8.1 Introduction 159</p> <p>8.2 Connectivity Through Coherency 161</p> <p>8.3 Phase-Slope Index 163</p> <p>8.4 Multivariate Directionality Estimation 163</p> <p>8.5 Modelling the Connectivity by Structural Equation Modelling 166</p> <p>8.6 EEG Hyper-Scanning and Inter-Subject Connectivity 168</p> <p>8.7 State-Space Model for Estimation of Cortical Interactions 173</p> <p>8.8 Application of Adaptive Filters 175</p> <p>8.9 Tensor Factorization Approach 182</p> <p>8.10 Conclusions 184</p> <p>References 185</p> <p><b>9 Detection and Tracking of Event-Related Potentials 188</b></p> <p>9.1 ERP Generation and Types 188</p> <p>9.2 Detection, Separation, and Classification of P300 Signals 192</p> <p>9.3 Brain Activity Assessment Using ERP 216</p> <p>9.4 Application of P300 to BCI 217</p> <p>9.5 Conclusions 218</p> <p>References 219</p> <p><b>10 Mental Fatigue 223</b></p> <p>10.1 Introduction 223</p> <p>10.2 Measurement of Brain Synchronization and Coherency 224</p> <p>10.3 Evaluation of ERP for Mental Fatigue 227</p> <p>10.4 Separation of P3a and P3b 234</p> <p>10.5 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm 238</p> <p>10.6 Conclusions 243</p> <p>References 243</p> <p><b>11 Emotion Encoding, Regulation and Control 245</b></p> <p>11.1 Theories and Emotion Classification 246</p> <p>11.2 The Effects of Emotions 248</p> <p>11.3 Psychology and Psychophysiology of Emotion 251</p> <p>11.4 Emotion Regulation 252</p> <p>11.5 Emotion-Provoking Stimuli 257</p> <p>11.6 Change in the ERP and Normal Brain Rhythms 259</p> <p>11.7 Perception of Odours and Emotion: Why Are They Related? 262</p> <p>11.8 Emotion-Related Brain Signal Processing 263</p> <p>11.9 Other Neuroimaging Modalities Used for Emotion Study 264</p> <p>11.10 Applications 267</p> <p>11.11 Conclusions 268</p> <p>References 268</p> <p><b>12 Sleep and Sleep Apnoea 274</b></p> <p>12.1 Introduction 274</p> <p>12.2 Stages of Sleep 275</p> <p>12.3 The Influence of Circadian Rhythms 278</p> <p>12.4 Sleep Deprivation 279</p> <p>12.5 Psychological Effects 280</p> <p>12.6 Detection and Monitoring of Brain Abnormalities During Sleep by EEG Analysis 281</p> <p>12.7 EEG and Fibromyalgia Syndrome 290</p> <p>12.8 Sleep Disorders of Neonates 291</p> <p>12.9 Dreams and Nightmares 291</p> <p>12.10 Conclusions 292</p> <p>References 292</p> <p><b>13 Brain–Computer Interfacing 295</b></p> <p>13.1 Introduction 295</p> <p>13.2 State of the Art in BCI 296</p> <p>13.3 BCI-Related EEG Features 300</p> <p>13.4 Major Problems in BCI 303</p> <p>13.5 Multidimensional EEG Decomposition 306</p> <p>13.6 Detection and Separation of ERP Signals 310</p> <p>13.7 Estimation of Cortical Connectivity 311</p> <p>13.8 Application of Common Spatial Patterns 314</p> <p>13.9 Multiclass Brain–Computer Interfacing 316</p> <p>13.10 Cell-Cultured BCI 318</p> <p>13.11 Conclusions 319</p> <p>References 320</p> <p><b>14 EEG and MEG Source Localization 325</b></p> <p>14.1 Introduction 325</p> <p>14.2 General Approaches to Source Localization 326</p> <p>14.3 Most Popular Brain Source Localization Approaches 329</p> <p>14.4 Determination of the Number of Sources from the EEG/MEG Signals 353</p> <p>14.5 Conclusions 355</p> <p>References 356</p> <p><b>15 Seizure and Epilepsy 360</b></p> <p>15.1 Introduction 360</p> <p>15.2 Types of Epilepsy 362</p> <p>15.3 Seizure Detection 365</p> <p>15.4 Chaotic Behaviour of EEG Sources 376</p> <p>15.5 Predictability of Seizure from the EEGs 378</p> <p>15.6 Fusion of EEG – fMRI Data for Seizure Detection and Prediction 391</p> <p>15.7 Conclusions 391</p> <p>References 392</p> <p><b>16 Joint Analysis of EEG and fMRI 397</b></p> <p>16.1 Fundamental Concepts 397</p> <p>16.2 Model-Based Method for BOLD Detection 403</p> <p>16.3 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG 405</p> <p>16.4 BOLD Detection in fMRI 413</p> <p>16.5 Fusion of EEG and fMRI 419</p> <p>16.6 Application to Seizure Detection 425</p> <p>16.7 Conclusions 427</p> <p>References 427</p> <p>Index 433</p>

<p><b>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.<br /> <br /> </b>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.</p>

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. <p>Key features:</p> <ul> <li>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)</li> <li>Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain</li> <li>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</li> <li>Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research</li> </ul>

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