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Eeg Signal Processing and Machine Learning de Saeid Sanei

Eeg Signal Processing and Machine Learning

Autor Saeid Sanei, Jonathon A Chambers
en Limba Engleză Hardback – 27 sep 2021

Proiectarea unor sisteme capabile să interpreteze corect activitatea cerebrală necesită o convergență precisă între neurofiziologie și algoritmi computaționali avansați. În volumul Eeg Signal Processing and Machine Learning, publicat de Wiley, Saeid Sanei și Jonathon A Chambers oferă un cadru tehnic riguros pentru analiza stărilor creierului, de la generarea potențialului de acțiune până la clasificarea automată a anomaliilor. Remarcăm faptul că această a doua ediție extinde considerabil fundamentul teoretic, introducând peste 50% material nou dedicat în special fuziunii senzorilor și utilizării tensorilor în analiza EEG. Structura lucrării urmează o progresie logică, de la bazele biologice și instrumentația de înregistrare, către modele matematice complexe pentru semnale normale și patologice. Subliniem includerea unor capitole esențiale despre conectivitatea cerebrală și screening-ul multimodal, elemente care lipsesc adesea din manualele clasice. Abordarea autorilor diferă de cea din Advanced Electroencephalography Analytical Methods de Ganesh Naik prin faptul că este mult mai aplicată, integrând algoritmi de învățare automată în aproape fiecare capitol dedicat diagnosticării, nu doar ca o secțiune teoretică separată. Față de lucrările anterioare ale lui Saeid Sanei, precum Smart and Sustainable Engineering for Next Generation Applications, unde accentul cădea pe IoT și sisteme de comunicații, volumul de față specializează expertiza autorului în domeniul ingineriei biomedicale. Reținem tratamentul detaliat al rețelelor neurale profunde și al procesării semnalelor cu valori complexe, oferind inginerilor instrumentele necesare pentru a dezvolta soluții robuste în detectarea crizelor epileptice sau în monitorizarea oboselii mentale.

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Specificații

ISBN-13: 9781119386940
ISBN-10: 1119386942
Pagini: 752
Dimensiuni: 170 x 244 x 40 mm
Greutate: 1.38 kg
Ediția:2nd edition
Editura: Wiley
Locul publicării:Chichester, United Kingdom

De ce să citești această carte

Această resursă este esențială pentru inginerii biomedicali și cercetătorii în neuroștiințe care doresc să implementeze soluții practice de Machine Learning pe date EEG reale. Cititorul câștigă o înțelegere profundă a tehnicilor de filtrare, eliminare a zgomotului și clasificare, fiind un ghid tehnic complet pentru dezvoltarea sistemelor de diagnostic asistat de calculator și a interfețelor creier-calculator de ultimă generație.

Despre autor

Saeid Sanei și Jonathon A Chambers sunt autorități recunoscute în domeniul procesării semnalelor. Saeid Sanei a publicat anterior lucrări de referință precum Emerging Trends in Electrical, Electronic and Communications Engineering, demonstrând o expertiză vastă în ingineria electrică și computațională. Contribuțiile lor la editura Wiley se concentrează pe aplicarea metodelor matematice avansate în probleme complexe de inginerie și medicină, echilibrând rigoarea academică cu necesitățile practice ale industriei tehnologiilor medicale.

Cuprins

Preface to the Second Edition xvii Preface to the First Edition xxi List of Abbreviations xxiii 1 Introduction to Electroencephalography 1 1.1 Introduction 1 1.2 History 2 1.3 Neural Activities 5 1.4 Action Potentials 6 1.5 EEG Generation 8 1.6 The Brain as a Network 12 1.7 Summary 12 References 13 2 EEG Waveforms 15 2.1 Brain Rhythms 15 2.2 EEG Recording and Measurement 18 2.2.1 Conventional Electrode Positioning 21 2.2.2 Unconventional and Special Purpose EEG Recording Systems 24 2.2.3 Invasive Recording of Brain Potentials 26 2.2.4 Conditioning the Signals 27 2.3 Sleep 28 2.4 Mental Fatigue 30 2.5 Emotions 30 2.6 Neurodevelopmental Disorders 31 2.7 Abnormal EEG Patterns 32 2.8 Ageing 33 2.9 Mental Disorders 34 2.9.1 Dementia 34 2.9.2 Epileptic Seizure and Nonepileptic Attacks 35 2.9.3 Psychiatric Disorders 39 2.9.4 External Effects 40 2.10 Summary 41 References 42 3 EEG Signal Modelling 47 3.1 Introduction 47 3.2 Physiological Modelling of EEG Generation 47 3.2.1 Integrate-and-Fire Models 49 3.2.2 Phase-Coupled Models 49 3.2.3 Hodgkin-Huxley Model 51 3.2.4 Morris-Lecar Model 54 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 57 3.4 Mathematical Models Derived Directly from the EEG Signals 61 3.4.1 Linear Models 61 3.4.1.1 Prediction Method 61 3.4.1.2 Prony's Method 62 3.4.2 Nonlinear Modelling 64 3.4.3 Gaussian Mixture Model 66 3.5 Electronic Models 67 3.5.1 Models Describing the Function of the Membrane 67 3.5.1.1 Lewis Membrane Model 68 3.5.1.2 Roy Membrane Model 68 3.5.2 Models Describing the Function of a Neuron 68 3.5.2.1 Lewis Neuron Model 68 3.5.2.2 The Harmon Neuron Model 71 3.5.3 A Model Describing the Propagation of the Action Pulse in an Axon 72 3.5.4 Integrated Circuit Realizations 72 3.6 Dynamic Modelling of Neuron Action Potential Threshold 72 3.7 Summary 73 References 73 4 Fundamentals of EEG Signal Processing 77 4.1 Introduction 77 4.2 Nonlinearity of the Medium 78 4.3 Nonstationarity 79 4.4 Signal Segmentation 80 4.5 Signal Transforms and Joint Time-Frequency Analysis 83 4.5.1 Wavelet Transform 87 4.5.1.1 Continuous Wavelet Transform 87 4.5.1.2 Examples of Continuous Wavelets 89 4.5.1.3 Discrete-Time Wavelet Transform 89 4.5.1.4 Multiresolution Analysis 90 4.5.1.5 Wavelet Transform Using Fourier Transform 93 4.5.1.6 Reconstruction 94 4.5.2 Synchro-Squeezed Wavelet Transform 95 4.5.3 Ambiguity Function and the Wigner-Ville Distribution 96 4.6 Empirical Mode Decomposition 100 4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function 101 4.8 Filtering and Denoising 104 4.9 Principal Component Analysis 107 4.9.1 Singular Value Decomposition 108 4.10 Summary 110 References 110 5 EEG Signal Decomposition 115 5.1 Introduction 115 5.2 Singular Spectrum Analysis 115 5.2.1 Decomposition 116 5.2.2 Reconstruction 117 5.3 Multichannel EEG Decomposition 118 5.3.1 Independent Component Analysis 118 5.3.2 Instantaneous BSS 122 5.3.3 Convolutive BSS 126 5.3.3.1 General Applications 127 5.3.3.2 Application of Convolutive BSS to EEG 128 5.4 Sparse Component Analysis 129 5.4.1 Standard Algorithms for Sparse Source Recovery 130 5.4.1.1 Greedy-Based Solution 130 5.4.1.2 Relaxation-Based Solution 131 5.4.2 k-Sparse Mixtures 131 5.5 Nonlinear BSS 133 5.6 Constrained BSS 134 5.7 Application of Constrained BSS; Example 135 5.8 Multiway EEG Decompositions 136 5.8.1 Tensor Factorization for BSS 139 5.8.2 Solving BSS of Nonstationary Sources Using Tensor Factorization 143 5.9 Tensor Factorization for Underdetermined Source Separation 149 5.10 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 153 5.11 Separation of Correlated Sources via Tensor Factorization 153 5.12 Common Component Analysis 154 5.13 Canonical Correlation Analysis 154 5.14 Summary 155 References 155 6 Chaos and Dynamical Analysis 165 6.1 Introduction to Chaos and Dynamical Systems 165 6.2 Entropy 166 6.3 Kolmogorov Entropy 166 6.4 Multiscale Fluctuation-Based Dispersion Entropy 167 6.5 Lyapunov Exponents 167 6.6 Plotting the Attractor Dimensions from Time Series 169 6.7 Estimation of Lyapunov Exponents from Time Series 169 6.7.1 Optimum Time Delay 172 6.7.2 Optimum Embedding Dimension 172 6.8 Approximate Entropy 173 6.9 Using Prediction Order 174 6.10 Summary 175 References 175 7 Machine Learning for EEG Analysis 177 7.1 Introduction 177 7.2 Clustering Approaches 181 7.2.1 k-Means Clustering Algorithm 181 7.2.2 Iterative Self-Organizing Data Analysis Technique 183 7.2.3 Gap Statistics 183 7.2.4 Density-Based Clustering 184 7.2.5 Affinity-Based Clustering 184 7.2.6 Deep Clustering 184 7.2.7 Semi-Supervised Clustering 185 7.2.7.1 Basic Semi-Supervised Techniques 185 7.2.7.2 Deep Semi-Supervised Techniques 186 7.2.8 Fuzzy Clustering 186 7.3 Classification Algorithms 187 7.3.1 Decision Trees 188 7.3.2 Random Forest 189 7.3.3 Linear Discriminant Analysis 190 7.3.4 Support Vector Machines 191 7.3.5 k-Nearest Neighbour 199 7.3.6 Gaussian Mixture Model 200 7.3.7 Logistic Regression 200 7.3.8 Reinforcement Learning 201 7.3.9 Artificial Neural Networks 201 7.3.9.1 Deep Neural Networks 203 7.3.9.2 Convolutional Neural Networks 205 7.3.9.3 Autoencoders 207 7.3.9.4 Variational Autoencoder 208 7.3.9.5 Recent DNN Approaches 209 7.3.9.6 Spike Neural Networks 210 7.3.9.7 Applications of DNNs to EEG 212 7.3.10 Gaussian Processes 212 7.3.11 Neural Processes 213 7.3.12 Graph Convolutional Networks 213 7.3.13 Naïve Bayes Classifier 213 7.3.14 Hidden Markov Model 214 7.3.14.1 Forward Algorithm 216 7.3.14.2 Backward Algorithm 216 7.3.14.3 HMM Design 216 7.4 Common Spatial Patterns 218 7.5 Summary 222 References 223 8 Brain Connectivity and Its Applications 235 8.1 Introduction 235 8.2 Connectivity through Coherency 238 8.3 Phase-Slope Index 240 8.4 Multivariate Directionality Estimation 240 8.4.1 Directed Transfer Function 241 8.4.2 Direct DTF 242 8.4.3 Partial Directed Coherence 243 8.5 Modelling the Connectivity by Structural Equation Modelling 243 8.6 Stockwell Time-Frequency Transform for Connectivity Estimation 246 8.7 Inter-Subject EEG Connectivity 247 8.7.1 Objectives 247 8.7.2 Technological Relevance 247 8.8 State-Space Model for Estimation of Cortical Interactions 249 8.9 Application of Cooperative Adaptive Filters 251 8.9.1 Use of Cooperative Kalman Filter 253 8.9.2 Task-Related Adaptive Connectivity 254 8.9.3 Diffusion Adaptation 255 8.9.4 Brain Connectivity for Cooperative Adaptation 256 8.9.5 Other Applications of Cooperative Learning and Brain Connectivity Estimation 257 8.10 Graph Representation of Brain Connectivity 258 8.11 Tensor Factorization Approach 259 8.12 Summary 262 References 263 9 Event-Related Brain Responses 269 9.1 Introduction 269 9.2 ERP Generation and Types 269 9.2.1 P300 and its Subcomponents 273 9.3 Detection, Separation, and Classification of P300 Signals 274 9.3.1 Using ICA 275 9.3.2 Estimation of Single-Trial Brain Responses by Modelling the ERP Waveforms 277 9.3.3 ERP Source Tracking in Time 278 9.3.4 Time-Frequency Domain Analysis 280 9.3.5 Application of Kalman Filter 284 9.3.6 Particle Filtering and its Application to ERP Tracking 286 9.3.7 Variational Bayes Method 291 9.3.8 Prony's Approach for Detection of P300 Signals 293 9.3.9 Adaptive Time-Frequency Methods 297 9.4 Brain Activity Assessment Using ERP 298 9.5 Application of P300 to BCI 299 9.6 Summary 300 References 301 10 Localization of Brain Sources 307 10.1 Introduction 307 10.2 General Approaches to Source Localization 308 10.2.1 Dipole Assumption 309 10.3 Head Model 311 10.4 Most Popular Brain Source Localization Approaches 313 10.4.1 EEG Source Localization Using Independent Component Analysis 313 10.4.2 MUSIC Algorithm 313 10.4.3 LORETA Algorithm 317 10.4.4 FOCUSS Algorithm 318 10.4.5 Standardized LORETA 319 10.4.6 Other Weighted Minimum Norm Solutions 320 10.4.7 Evaluation Indices 323 10.4.8 Joint ICA-LORETA Approach 323 10.5 Forward Solutions to the Localization Problem 325 10.5.1 Partially Constrained BSS Method 325 10.5.2 Constrained Least-Squares Method for Localization of P3a and P3b 326 10.5.3 Spatial Notch Filtering Approach 328 10.6 The Methods Based on Source Tracking 333 10.6.1 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization 333 10.6.2 Hybrid Beamforming - Particle Filtering 336 10.7 Determination of the Number of Sources from the EEG/MEG Signals 337 10.8 Other Hybrid Methods 340 10.9 Application of Machine Learning for EEG/MEG Source Localization 340 10.10 Summary 342 References 343 11 Epileptic Seizure Prediction, Detection, and Localization 351 11.1 Introduction 351 11.2 Seizure Detection 357 11.2.1 Adult Seizure Detection from EEGs 357 11.2.2 Detection of Neonatal Seizure 363 11.3 Chaotic Behaviour of Seizure EEG 366 11.4 Seizure Detection from Brain Connectivity 369 11.5 Prediction of Seizure Onset from EEG 369 11.6 Intracranial and Joint Scalp-Intracranial Recordings for IED Detection 384 11.6.1 Introduction to IED 384 11.6.2 iEED-Times IED Detection from Scalp EEG 386 11.6.3 A Multiview Approach to IED Detection 391 11.6.4 Coupled Dictionary Learning for IED Detection 391 11.6.5 A Deep Learning Approach to IED Detection 392 11.7 Fusion of EEG-fMRI Data for Seizure Prediction 396 11.8 Summary 398 References 399 12 Sleep Recognition, Scoring, and Abnormalities 407 12.1 Introduction 407 12.1.1 Definition of Sleep 407 12.1.2 Sleep Disorder 408 12.2 Stages of Sleep 409 12.2.1 NREM Sleep 409 12.2.2 REM Sleep 411 12.3 The Influence of Circadian Rhythms 414 12.4 Sleep Deprivation 415 12.5 Psychological Effects 416 12.6 EEG Sleep Analysis and Scoring 416 12.6.1 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation 416 12.6.2 Time-Frequency Analysis of Sleep EEG Using Matching Pursuit 417 12.6.3 Detection of Normal Rhythms and Spindles Using Higher-Order Statistics 421 12.6.4 Sleep Scoring Using Tensor Factorization 423 12.6.5 Application of Neural Networks 425 12.6.6 Model-Based Analysis 426 12.7 Detection and Monitoring of Brain Abnormalities during Sleep by EEG and Multimodal PSG Analysis 428 12.7.1 Analysis of Sleep Apnoea 428 12.7.2 EEG and Fibromyalgia Syndrome 431 12.7.3 Sleep Disorders of Neonates 431 12.8 Dreams and Nightmares 432 12.9 EEG and Consciousness 433 12.10 Functional Brain Connectivity for Sleep Analysis 433 12.11 Summary 434 References 435 13 EEG-Based Mental Fatigue Monitoring 441 13.1 Introduction 441 13.2 Feature-Based Machine Learning Approaches 443 13.2.1 Hidden Markov Model Application 443 13.2.2 Kernel Principal Component Analysis and Hidden Markov Model 444 13.2.3 Regression-Based Fatigue Estimation 444 13.2.4 Regularized Regression 445 13.2.5 Other Feature-Based Approaches 445 13.3 Measurement of Brain Synchronization and Coherency 446 13.3.1 Linear Measure of Synchronization 446 13.3.2 Nonlinear Measure of Synchronization 448 13.4 Evaluation of ERP for Mental Fatigue 451 13.5 Separation of P3a and P3b 457 13.6 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm 463 13.7 Assessing Mental Fatigue by Measuring Functional Connectivity 465 13.8 Deep Learning Approaches for Fatigue Evaluation 472 13.9 Summary 474 References 474 14 EEG-Based Emotion Recognition and Classification 479 14.1 Introduction 479 14.1.1 Theories and Emotion Classification 480 14.1.2 The Physiological Effects of Emotions 482 14.1.3 Psychology and Psychophysiology of Emotion 485 14.1.4 Emotion Regulation 487 14.1.4.1 Agency and Intentionality 490 14.1.4.2 Norm Violation 490 14.1.4.3 Guilt 491 14.1.4.4 Shame 491 14.1.4.5 Embarrassment 491 14.1.4.6 Pride 491 14.1.4.7 Indignation and Anger 491 14.1.4.8 Contempt 491 14.1.4.9 Pity and Compassion 492 14.1.4.10 Awe and Elevation 492 14.1.4.11 Gratitude 492 14.1.5 Emotion-Provoking Stimuli 492 14.2 Effect of Emotion on the Brain 494 14.2.1 ERP Change Due to Emotion 494 14.2.2 Changes of Normal Brain Rhythms with Emotion 497 14.2.3 Emotion and Lateral Brain Engagement 498 14.2.4 Perception of Odours and Emotion: Why Are They Related? 498 14.3 Emotion-Related Brain Signal Processing and Machine Learning 499 14.3.1 Evaluation of Emotion Based on the Changes in Brain Rhythms 500 14.3.2 Brain Asymmetricity and Connectivity for Emotion Evaluation 501 14.3.3 Changes in ERPs for Emotion Recognition 504 14.3.4 Combined Features for Emotion Analysis 504 14.4 Other Physiological Measurement Modalities Used for Emotion Study 507 14.5 Applications 510 14.6 Pain Assessment Using EEG 510 14.7 Emotion Elicitation and Induction through Virtual Reality 512 14.8 Summary 513 References 514 15 EEG Analysis of Neurodegenerative Diseases 525 15.1 Introduction 525 15.2 Alzheimer's Disease 527 15.2.1 Application of Brain Connectivity Estimation to AD and MCI 528 15.2.2 ERP-Based AD Monitoring 532 15.2.3 Other Approaches to EEG-Based AD Monitoring 532 15.3 Motor Neuron Disease 537 15.4 Parkinson's Disease 537 15.5 Huntington's Disease 541 15.6 Prion Disease 542 15.7 Behaviour Variant Frontotemporal Dementia 544 15.8 Lewy Body Dementia 545 15.9 Summary 545 References 546 16 EEG As A Biomarker for Psychiatric and Neurodevelopmental Disorders 551 16.1 Introduction 551 16.1.1 History 551 16.1.1.1 Different Psychiatric and Neurodevelopmental Disorders 553 16.1.1.2 NDD Diagnosis 554 16.2 EEG Analysis for Different NDDs 554 16.2.1 ADHD 554 16.2.1.1 ADHD Symptoms and Possible Treatment 554 16.2.1.2 EEG-Based Diagnosis of ADHD 555 16.2.2 ASD 559 16.2.2.1 ASD Symptoms and Possible Treatment 559 16.2.2.2 EEG-Based Diagnosis of ASD 560 16.2.3 Mood Disorder 561 16.2.3.1 EEG for Monitoring Depression 562 16.2.3.2 EEG for Monitoring Bipolar Disorder 564 16.2.4 Schizophrenia 565 16.2.4.1 Schizophrenia Symptoms and Management 565 16.2.4.2 EEG as the Biomarker for Schizophrenia 566 16.2.5 Anxiety (and Panic) Disorder 568 16.2.5.1 Definition and Symptoms 568 16.2.5.2 EEG for Assessing Anxiety 569 16.2.6 Insomnia 571 16.2.6.1 Symptoms of Insomnia 571 16.2.6.2 EEG for Insomnia Analysis 572 16.2.7 Schizotypal Personality Disorder 572 16.2.7.1 What Is Schizotypal Disorder? 572 16.2.7.2 EEG Manifestation of Schizotypal 573 16.3 Summary 573 References 574 17 Brain-Computer Interfacing Using EEG 581 17.1 Introduction 581 17.1.1 State of the Art in BCI 584 17.1.2 BCI Terms and Definitions 585 17.1.3 Popular BCI Directions 585 17.1.4 Virtual Environment for BCI 586 17.1.5 Evolution of BCI Design 587 17.2 BCI-Related EEG Components 588 17.2.1 Readiness Potential and Its Detection 588 17.2.2 ERD and ERS 588 17.2.3 Transient Beta Activity after the Movement 593 17.2.4 Gamma Band Oscillations 593 17.2.5 Long Delta Activity 593 17.2.6 ERPs 594 17.3 Major Problems in BCI 594 17.3.1 Preprocessing of the EEGs 595 17.4 Multidimensional EEG Decomposition 597 17.4.1 Space-Time-Frequency Method 599 17.4.2 Parallel Factor Analysis 599 17.5 Detection and Separation of ERP Signals 601 17.6 Estimation of Cortical Connectivity 603 17.7 Application of Common Spatial Patterns 606 17.8 Multiclass Brain-Computer Interfacing 609 17.9 Cell-Cultured BCI 610 17.10 Recent BCI Applications 610 17.11 Neurotechnology for BCI 614 17.12 Joint EEG and Other Brain-Scanning Modalities for BCI 617 17.12.1 Joint EEG-fNIRS for BCI 617 17.12.2 Joint EEG-MEG for BCI 618 17.13 Performance Measures for BCI Systems 618 17.14 Summary 619 References 620 18 Joint Analysis of EEG and Other Simultaneously Recorded Brain Functional Neuroimaging Modalities 631 18.1 Introduction 631 18.2 Fundamental Concepts 631 18.2.1 Functional Magnetic Resonance Imaging 631 18.2.1.1 Blood Oxygenation Level Dependence 633 18.2.1.2 Popular fMRI Data Formats 635 18.2.1.3 Preprocessing of fMRI Data 635 18.2.2 Functional Near-Infrared Spectroscopy 636 18.2.3 Magnetoencephalography 640 18.3 Joint EEG-fMRI 640 18.3.1 Relation Between EEG and fMRI 640 18.3.2 Model-Based Method for BOLD Detection 642 18.3.3 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG 644 18.3.3.1 Gradient Artefact Removal from EEG 644 18.3.3.2 Ballistocardiogram Artefact Removal from EEG 645 18.3.4 BOLD Detection in fMRI 652 18.3.4.1 Implementation of Different NMF Algorithms for BOLD Detection 653 18.3.4.2 BOLD Detection Experiments 654 18.3.5 Fusion of EEG and fMRI 659 18.3.5.1 Extraction of fMRI Time Course from EEG 659 18.3.5.2 Fusion of EEG and fMRI; Blind Approach 659 18.3.5.3 Fusion of EEG and fMRI; Model-Based Approach 664 18.3.6 Application to Seizure Detection 664 18.3.7 Investigation of Decision Making in the Brain 666 18.3.8 Application to Schizophrenia 666 18.3.9 Other Applications 667 18.4 EEG-NIRS Joint Recording and Fusion 668 18.5 MEG-EEG Fusion 672 18.6 Summary 672 References 673 Index 681

Descriere

EEG Signal Processing and Machine Learning

Explore cutting edge techniques at the forefront of electroencephalogram research and artificial intelligence from leading voices in the field

The newly revised Second Edition of EEG Signal Processing and Machine Learning delivers an inclusive and thorough exploration of new techniques and outcomes in electroencephalogram (EEG) research in the areas of analysis, processing, and decision making about a variety of brain states, abnormalities, and disorders using advanced signal processing and machine learning techniques. The book content is substantially increased upon that of the first edition and, while it retains what made the first edition so popular, is composed of more than 50% new material.

The distinguished authors have included new material on tensors for EEG analysis and sensor fusion, as well as new chapters on mental fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and psychiatric abnormalities. In addition to including a comprehensive chapter on machine learning, machine learning applications have been added to almost all the chapters. Moreover, multimodal brain screening, such as EEG-fMRI, and brain connectivity have been included as two new chapters in this new edition.

Readers will also benefit from the inclusion of:

  • A thorough introduction to EEGs, including neural activities, action potentials, EEG generation, brain rhythms, and EEG recording and measurement
  • An exploration of brain waves, including their generation, recording, and instrumentation, abnormal EEG patterns and the effects of ageing and mental disorders
  • A treatment of mathematical models for normal and abnormal EEGs
  • Discussions of the fundamentals of EEG signal processing, including statistical properties, linear and nonlinear systems, frequency domain approaches, tensor factorization, diffusion adaptive filtering, deep neural networks, and complex-valued signal processing

Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, students and researchers in the above areas, the Second Edition of EEG Signal Processing and Machine Learning will also earn a place in the libraries of undergraduate and postgraduate students studying Biomedical Engineering, Neuroscience and Epileptology.

Notă biografică

Saeid Sanei, PhD, DIC, FBCS, is Professor of Signal Processing and Machine Learning at Nottingham Trent University, UK, and a Visiting Professor at Imperial College London, UK. He received his doctorate in Biomedical Signal and Image Processing from Imperial College London in 1991. He is an internationally renowned expert in signal processing, biomedical signal processing, and pattern recognition. Jonathon A Chambers, FREng, FIEEE, DSc (Imperial), is Emeritus Professor of Signal and Information Processing within the College of Science and Engineering at the University of Leicester, UK. His research interests are focused upon adaptive signal processing and machine learning and their application in biomedicine, communications, defense, and navigation systems.