Adaptive processing of brain signals - Sanei, Saeid (Chichester, West Sussex, United Kingdom : Wiley, 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"--
"Covers the fundamentals of brain signal processing, before developing the subject at advanced level"--
9 Detection and Tracking of Event-Related Potentials 188 -- 9.1 ERP Generation and Types 188 -- 9.1.1 P300 and Its Subcomponents 191 -- 9.2 Detection, Separation, and Classification of P300 Signals 192 -- 9.2.1 Using ICA 193 -- 9.2.2 Estimation of Single Trial Brain Responses by Modelling the ERP Waveforms 195 -- 9.2.3 ERP Source Tracking in Time 197 -- 9.2.4 Time-Frequency Domain Analysis 200 -- 9.2.5 Application of Kalman Filter 203 -- 9.2.6 Particle Filtering and Its Application to ERP Tracking 206 -- 9.2.7 Variational Bayes Method 209 -- 9.2.8 Prony's Approach for Detection of P300 Signals 211 -- 9.2.9 Adaptive Time-Frequency Methods 214 -- 9.3 Brain Activity Assessment Using ERP 216 -- 9.4 Application of P300 to BCI 217 -- 9.5 Conclusions 218 -- References 219 -- 10 Mental Fatigue 223 -- 10.1 Introduction 223 -- 10.2 Measurement of Brain Synchronization and Coherency 224 -- 10.2.1 Linear Measure of Synchronization 224 -- 10.2.2 Nonlinear Measure of Synchronization 226 -- 10.3 Evaluation of ERP for Mental Fatigue 227 -- 10.4 Separation of P3a and P3b 234 -- 10.5 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm 238 -- 10.6 Conclusions 243 -- References 243 -- 11 Emotion Encoding, Regulation and Control 245 -- 11.1 Theories and Emotion Classification 246 -- 11.2 The Effects of Emotions 248 -- 11.3 Psychology and Psychophysiology of Emotion 251 -- 11.4 Emotion Regulation 252 -- 11.5 Emotion-Provoking Stimuli 257 -- 11.6 Change in the ERP and Normal Brain Rhythms 259 -- 11.6.1 ERP and Emotion 259 -- 11.6.2 Changes in Normal Brain Waves with Emotion 261 -- 11.7 Perception of Odours and Emotion: Why Are They Related? 262 -- 11.8 Emotion-Related Brain Signal Processing 263 -- 11.9 Other Neuroimaging Modalities Used for Emotion Study 264 -- 11.10 Applications 267 -- 11.11 Conclusions 268 -- References 268 -- 12 Sleep and Sleep Apnoea 274 -- 12.1 Introduction 274 -- 12.2 Stages of Sleep 275 -- 12.2.1 NREM Sleep 275 -- 12.2.2 REM Sleep 277 -- 12.3 The Influence of Circadian Rhythms 278 -- 12.4 Sleep Deprivation 279 -- 12.5 Psychological Effects 280 -- 12.6 Detection and Monitoring of Brain Abnormalities During Sleep by EEG Analysis 281 -- 12.6.1 Analysis of Sleep Apnoea 281 -- 12.6.2 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation 282 -- 12.6.3 Application of Matching Pursuit 282 -- 12.6.4 Detection of Normal Rhythms and Spindles Using Higher Order Statistics 285 -- 12.6.5 Application of Neural Networks 287 -- 12.6.6 Model-Based Analysis 288 -- 12.6.7 Hybrid Methods 290 -- 12.7 EEG and Fibromyalgia Syndrome 290 -- 12.8 Sleep Disorders of Neonates 291 -- 12.9 Dreams and Nightmares 291 -- 12.10 Conclusions 292 -- References 292 -- 13 Brain-Computer Interfacing 295 -- 13.1 Introduction 295 -- 13.2 State of the Art in BCI 296 -- 13.3 BCI-Related EEG Features 300 -- 13.3.1 Readiness Potential and Its Detection 300 -- 13.3.2 ERD and ERS 300 -- 13.3.3 Transient Beta Activity after the Movement 302 -- 13.3.4 Gamma Band Oscillations 302 -- 13.3.5 Long Delta Activity 303 -- 13.4 Major Problems in BCI 303 -- 13.4.1 Pre-Processing of the EEGs 304 -- 13.5 Multidimensional EEG Decomposition 306 -- 13.5.1 Space-Time-Frequency Method 308 -- 13.5.2 Parallel Factor Analysis 309 -- 13.6 Detection and Separation of ERP Signals 310 -- 13.7 Estimation of Cortical Connectivity 311 -- 13.8 Application of Common Spatial Patterns 314 -- 13.9 Multiclass Brain-Computer Interfacing 316 -- 13.10 Cell-Cultured BCI 318 -- 13.11 Conclusions 319 -- References 320 -- 14 EEG and MEG Source Localization 325 -- 14.1 Introduction 325 -- 14.2 General Approaches to Source Localization 326 -- 14.2.1 Dipole Assumption 327 -- 14.3 Most Popular Brain Source Localization Approaches 329 -- 14.3.1 ICA Method 329 -- 14.3.2 MUSIC Algorithm 329 -- 14.3.3 LORETA Algorithm 333 -- 14.3.4 FOCUSS Algorithm 335 -- 14.3.5 Standardised LORETA 335 -- 14.3.6 Other Weighted Minimum Norm Solutions 336 -- 14.3.7 Evaluation Indices 338 -- 14.3.8 Joint ICA-LORETA Approach 338 -- 14.3.9 Partially Constrained BSS Method 340 -- 14.3.10 Constrained Least-Squares Method for Localization of P3a and P3b 341 -- 14.3.11 Spatial Notch Filtering Approach 342 -- 14.3.12 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization 347 -- 14.3.13 Hybrid Beamforming -- Particle Filtering 351 -- 14.4 Determination of the Number of Sources from the EEG/MEG Signals 353 -- 14.5 Conclusions 355 -- References 356 -- 15 Seizure and Epilepsy 360 -- 15.1 Introduction 360 -- 15.2 Types of Epilepsy 362 -- 15.3 Seizure Detection 365 -- 15.3.1 Adult Seizure Detection 365 -- 15.3.2 Detection of Neonate Seizure 371 -- 15.4 Chaotic Behaviour of EEG Sources 376 -- 15.5 Predictability of Seizure from the EEGs 378 -- 15.6 Fusion of EEG -- fMRI Data for Seizure Detection and Prediction 391 -- 15.7 Conclusions 391 -- References 392 -- 16 Joint Analysis of EEG and fMRI 397 -- 16.1 Fundamental Concepts 397 -- 16.1.1 Blood Oxygenation Level Dependent 399 -- 16.1.2 Popular fMRI Data Formats 400 -- 16.1.3 Preprocessing of fMRI Data 401 -- 16.1.4 Relation between EEG and fMRI 401 -- 16.2 Model-Based Method for BOLD Detection 403 -- 16.3 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG 405 -- 16.3.1 Gradient Artefact Removal 405 -- 16.3.2 Ballistocardiogram Artefact Removal 406 -- 16.4 BOLD Detection in fMRI 413 -- 16.4.1 Implementation of Different NMF Algorithms for BOLD Detection 414 -- 16.4.2 BOLD Detection Experiments 416 -- 16.5 Fusion of EEG and fMRI 419 -- 16.5.1 Extraction of fMRI Time-Course from EEG 419 -- 16.5.2 Fusion of EEG and fMRI, Blind Approach 241 -- 16.5.3 Fusion of EEG and fMRI, Model-Based Approach 425 -- 16.6 Application to Seizure Detection 425 -- 16.7 Conclusions 427 -- References 427.
Includes bibliographical references and index.
Description based on print version record and CIP data provided by publisher.
Keywords: Neural networks (Neurobiology); Brain -- Physiology; Signal processing -- Digital techniques; SCIENCE / Waves & Wave Mechanics; Brain -- Physiology; Neural networks (Neurobiology); Signal processing -- Digital techniques; Brain -- physiology; Signal Processing, Computer-Assisted.; Electroencephalography; Magnetoencephalography; Neurobiology; Brain -- Physiology; Neural networks (Neurobiology); Signal processing -- Digital techniques; SOCIAL SCIENCE / Anthropology / Physical; Electronic books; Electronic books