EEG signal processing and machine learning:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2022
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxxiv, 714 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119386940 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV048867782 | ||
| 003 | DE-604 | ||
| 005 | 20230428 | ||
| 007 | t | ||
| 008 | 230320s2022 a||| |||| 00||| eng d | ||
| 020 | |a 9781119386940 |9 978-1-119-38694-0 | ||
| 035 | |a (OCoLC)1376409735 | ||
| 035 | |a (DE-599)BVBBV048867782 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-739 |a DE-355 | ||
| 082 | 0 | |a 616.8/047547 |2 23 | |
| 084 | |a CZ 1300 |0 (DE-625)19229: |2 rvk | ||
| 084 | |a YG 2400 |0 (DE-625)153482:12905 |2 rvk | ||
| 100 | 1 | |a Sanei, Saeid |e Verfasser |0 (DE-588)104607041X |4 aut | |
| 240 | 1 | 0 | |a EEG signal processing |
| 245 | 1 | 0 | |a EEG signal processing and machine learning |c Saeid Sanei ; Jonathon A. Chambers |
| 250 | |a Second edition | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2022 | |
| 300 | |a xxxiv, 714 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Electroencephalography |a Signal processing |a Machine learning | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Signalverarbeitung |0 (DE-588)4054947-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Signalverarbeitung |0 (DE-588)4054947-1 |D s |
| 689 | 0 | 2 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Chambers, Jonathon A. |e Verfasser |0 (DE-588)1073169103 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 978-1-119-38692-6 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-1-119-38693-3 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034132745&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-034132745 | ||
Datensatz im Suchindex
| _version_ | 1804185002024370176 |
|---|---|
| adam_text | Contents Preface to the Second Edition xvii Preface to the First Edition xxi List of Abbreviations xxiii 1 Introduction to Electroencephalography 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Introduction 1 History 2 Neural Activities 5 Action Potentials 6 EEG Generation 8 The Brain as a Network Summary 12 References 13 2 EEG Waveforms 2.1 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.9.1 2.9.2 2.9.3 1 12 15 Brain Rhythms 15 EEG Recording and Measurement 18 Conventional Electrode Positioning 21 Unconventional and Special Purpose EEG Recording Systems 24 Invasive Recording of Brain Potentials 26 Conditioning the Signals 27 Sleep 28 Mental Fatigue 30 Emotions 30 Neurodevelopmental Disorders 31 Abnormal EEG Patterns 32 Ageing 33 Mental Disorders 34 Dementia 34 Epileptic Seizure and Nonepileptic Attacks 35 Psychiatric Disorders 39
vi Contents 2.9.4 2.10 External Effects Summary 41 References 42 3 EEG Signal Modelling 3.1 3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.3 3.4 3.4.1 3.4.1.1 3.4.1.2 3.4.2 3.4.3 3.5 3.5.1 3.5.1.1 3.5.1.2 3.5.2 3.5.2.1 3.5.2.2 3.5.3 3.5.4 3.6 3.7 Introduction 47 Physiological Modelling of EEG Generation 47 Integrate-and-Fire Models 49 Phase-Coupled Models 49 Hodgkin-Huxley Model 51 Morris-Lecar Model 54 Generating EEG Signals Based on Modelling the Neuronal Activities 57 Mathematical Models Derived Directly from the EEG Signals 61 Linear Models 61 Prediction Method 61 Prony’s Method 62 Nonlinear Modelling 64 Gaussian Mixture Model 66 Electronic Models 67 Models Describing the Function of the Membrane 67 Lewis Membrane Model 68 Roy Membrane Model 68 Models Describing the Function of a Neuron 68 Lewis Neuron Model 68 The Harmon Neuron Model 71 A Model Describing the Propagation of the Action Pulse in an Axon 72 Integrated Circuit Realizations 72 Dynamic Modelling of Neuron Action Potential Threshold 72 Summary 73 References 73 4 77 Introduction 77 Nonlinearity of the Medium 78 Nonstationarity 79 Signal Segmentation 80 Signal Transforms and Joint Time-Frequency Analysis Wavelet Transform 87 Continuous Wavelet Transform 87 Examples of Continuous Wavelets 89 Discrete-Time Wavelet Transform 89 Multiresolution Analysis 90 Wavelet Transform Using Fourier Transform 93 Reconstruction 94 Synchro-Squeezed Wavelet Transform 95 4.1 4.2 4.3 4.4 4.5 4.5.1 4.5.1.1 4.5.1.2 4.5.1.3 4.5.1.4 4.5.1.5 4.5.1.6 4.5.2 40 47 Fundamentals of EEG Signal Processing 83
Contents 4.5.3 4.6 4.7 4.8 4.9 4.9.1 4.10 5 5.1 5.2 5.2.1 5.2.2 5.3 5.3.1 5.3.2 5.3.3 5.3.3.1 5.3.3.2 5.4 5.4.1 5.4.1.1 5.4.1.2 5.4.2 5.5 5.6 5.7 5.8 5.8.1 5.8.2 5.9 5.10 5.11 5.12 5.13 5.14 6 6.1 6.2 6.3 Ambiguity Function and the Wigner-Ville Distribution 96 Empirical Mode Decomposition 100 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function 101 Filtering and Denoising 104 Principal Component Analysis 107 Singular Value Decomposition 108 Summary 110 References 110 EEG Signal Decomposition 115 Introduction 115 Singular Spectrum Analysis 115 Decomposition 116 Reconstruction 117 Multichannel EEG Decomposition 118 Independent Component Analysis 118 Instantaneous BSS 122 Convolutive BSS 126 General Applications 127 Application of Convolutive BSS to EEG 128 Sparse Component Analysis 129 Standard Algorithms for Sparse Source Recovery 130 Greedy-Based Solution 130 Relaxation-Based Solution 131 k-Sparse Mixtures 131 Nonlinear BSS 133 Constrained BSS 134 Application of Constrained BSS; Example 135 Multiway EEG Decompositions 136 Tensor Factorization for BSS 139 Solving BSS of Nonstationary Sources Using Tensor Factorization 143 Tensor Factorization for Underdetermined Source Separation 149 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 153 Separation of Correlated Sources via Tensor Factorization 153 Common Component Analysis 154 Canonical Correlation Analysis 154 Summary 155 References 155 165 Introduction to Chaos and Dynamical Systems Entropy 166 Kolmogorov Entropy 166 Chaos and Dynamical Analysis 165 vii
viii Contents 6.4 6.5 6.6 6.7 6.7.1 6.7.2 6.8 6.9 6.10 7 7.1 7.2 7.2.1 7.2.2 7.2.3 7.2.4 7.2.5 7.2.6 7.2.7 7.2.7.1 7.2.7.2 7.2.8 7.3 7.3.1 7.3.2 7.3.3 7.3.4 7.3.5 7.3.6 7.3.7 7.3.8 7.3.9 7.3.9.1 7.3.9.2 7.3.9.3 7.3.9.4 7.3.9.5 7.3.9.6 7.3.9.7 7.3.10 7.3.11 7.3.12 7.3.13 Multiscale Fluctuation-Based Dispersion Entropy 167 Lyapunov Exponents 167 Plotting the Attractor Dimensions from Time Series 169 Estimation of Lyapunov Exponents from Time Series 169 Optimum Time Delay 172 Optimum Embedding Dimension 172 Approximate Entropy 173 Using Prediction Order 174 Summary 175 References 175 Machine Learning for EEG Analysis 177 Introduction 177 Clustering Approaches 181 k-Means Clustering Algorithm 181 Iterative Self-Organizing Data Analysis Technique Gap Statistics 183 Density-Based Clustering 184 Affinity-Based Clustering 184 Deep Clustering 184 Semi-Supervised Clustering 185 Basic Semi-Supervised Techniques 185 Deep Semi-Supervised Techniques 186 Fuzzy Clustering 186 Classification Algorithms 187 Decision Trees 188 Random Forest 189 Linear Discriminant Analysis 190 Support Vector Machines 191 k-Nearest Neighbour 199 Gaussian Mixture Model 200 Logistic Regression 200 Reinforcement Learning 201 Artificial Neural Networks 201 Deep Neural Networks 203 Convolutional Neural Networks 205 Autoencoders 207 Variational Autoencoder 208 Recent DNN Approaches 209 Spike Neural Networks 210 Applications of DNNs to EEG 212 Gaussian Processes 212 Neural Processes 213 Graph Convolutional Networks 213 Naïve Bayes Classifier 213 183
Contents 7.3.14 7.3.14.1 7.3.14.2 7.3.14.3 7.4 7.5 Hidden Markov Model 214 Forward Algorithm 216 Backward Algorithm 216 HMM Design 216 Common Spatial Patterns 218 Summary 222 References 223 8 Brain Connectivity and Its Applications 235 Introduction 235 Connectivity through Coherency 238 Phase-Slope Index 240 Multivariate Directionality Estimation 240 Directed Transfer Function 241 Direct DTF 242 Partial Directed Coherence 243 Modelling the Connectivity by Structural Equation Modelling 243 Stockwell Time-Frequency Transform for Connectivity Estimation 246 Inter-Subject EEG Connectivity 247 Objectives 247 Technological Relevance 247 State-Space Model for Estimation of Cortical Interactions 249 Application of Cooperative Adaptive Filters 251 Use of Cooperative Kalman Filter 253 Task-Related Adaptive Connectivity 254 Diffusion Adaptation 255 Brain Connectivity for Cooperative Adaptation 256 Other Applications of Cooperative Learning and Brain Connectivity Estimation 257 Graph Representation of Brain Connectivity 258 Tensor Factorization Approach 259 Summary 262 References 263 8.1 8.2 8.3 8.4 8.4.1 8.4.2 8.4.3 8.5 8.6 8.7 8.7.1 8.7.2 8.8 8.9 8.9.1 8.9.2 8.9.3 8.9.4 8.9.5 8.10 8.11 8.12 269 9 Event-Related Brain Responses 9.1 9.2 9.2.1 9.3 9.3.1 9.3.2 Introduction 269 ERP Generation and Types 269 P300 and its Subcomponents 273 Detection, Separation, and Classification of P300 Signals 274 Using ICA 275 Estimation of Single-Trial Brain Responses by Modelling the ERP Waveforms 277 ERP Source Tracking in Time 278 Time-Frequency Domain Analysis 280 Application of Kalman Filter 284
9.3.3 9.3.4 9.3.5 ix
Contents 9.3.6 9.3.7 9.3.8 9.3.9 9.4 9.5 9.6 Partide Filtering and its Application to ERP Tracking 286 Variational Bayes Method 291 Prony’s Approach for Detection of P300 Signals 293 Adaptive Time-Frequency Methods 297 Brain Activity Assessment Using ERP 298 Application of P300 to ВСІ 299 Summary 300 References 301 10 Localization of Brain Sources 10.1 10.2 10.2.1 10.3 10.4 10.4.1 10.4.2 10.4.3 10.4.4 10.4.5 10.4.6 10.4.7 10.4.8 10.5 10.5.1 10.5.2 10.5.3 10.6 10.6.1 10.6.2 10.7 10.8 10.9 10.10 307 Introduction 307 General Approaches to Source Localization 308 Dipole Assumption 309 Head Model 311 Most Popular Brain Source Localization Approaches 313 EEG Source Localization Using Independent Component Analysis 313 MUSIC Algorithm 313 LORETA Algorithm 317 FOCUSS Algorithm 318 Standardized LORETA 319 Other Weighted Minimum Norm Solutions 320 Evaluation Indices 323 Joint ICA-LORETA Approach 323 Forward Solutions to the Localization Problem 325 Partially Constrained BSS Method 325 Constrained Least-Squares Method for Localization of P3a and P3b 326 Spatial Notch Filtering Approach 328 The Methods Based on Source Tracking 333 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization 333 Hybrid Beamforming - Particle Filtering 336 Determination of the Number of Sources from the EEG/MEG Signals 337 Other Hybrid Methods 340 Application of Machine Learning for EEG/MEG Source Localization 340 Summary 342 References 343 11 Epileptic Seizure Prediction, Detection, and Localization 11.1 11.2 11.2.1 11.2.2 11.3 11.4 11.5 Introduction 351 Seizure Detection 357 Adult Seizure
Detection from EEGs 357 Detection of Neonatal Seizure 363 Chaotic Behaviour of Seizure EEG 366 Seizure Detection from Brain Connectivity 369 Prediction of Seizure Onset from EEG 369 351
Contents 11.6 11.6.1 11.6.2 11.6.3 11.6.4 11.6.5 11.7 11.8 Intracranial and Joint Scalp-Intracrania! Recordings for IED Detection Introduction to IED 384 iEED-Times IED Detection from Scalp EEG 386 A Multiview Approach to IED Detection 391 Coupled Dictionary Learning for IED Detection 391 A Deep Learning Approach to IED Detection 392 Fusion of EEG-fMRI Data for Seizure Prediction 396 Summary 398 References 399 12 407 Introduction 407 Definition of Sleep 407 Sleep Disorder 408 Stages of Sleep 409 NREM Sleep 409 REM Sleep 411 The Influence of Circadian Rhythms 414 Sleep Deprivation 415 Psychological Effects 416 EEG Sleep Analysis and Scoring 416 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation 416 Time-Frequency Analysis of Sleep EEG Using Matching Pursuit 417 Detection of Normal Rhythms and Spindles Using Higher-Order Statistics 421 Sleep Scoring Using Tensor Factorization 423 Application of Neural Networks 425 Model-Based Analysis 426 Detection and Monitoring of Brain Abnormalities during Sleep by EEG and Multimodal PSG Analysis 428 Analysis of Sleep Apnoea 428 EEG and Fibromyalgia Syndrome 431 Sleep Disorders of Neonates 431 Dreams and Nightmares 432 EEG and Consciousness 433 Functional Brain Connectivity for Sleep Analysis 433 Summary 434 References 435 12.1 12.1.1 12.1.2 12.2 12.2.1 12.2.2 12.3 12.4 12.5 12.6 12.6.1 12.6.2 12.6.3 12.6.4 12.6.5 12.6.6 12.7 12.7.1 12.7.2 12.7.3 12.8 12.9 12.10 12.11 13 13.1 13.2 13.2.1 13.2.2 13.2.3 Steep Recognition, Scoring, and Abnormalities EEG-Based Mental Fatigue Monitoring 441 Introduction 441
Feature-Based Machine Learning Approaches 443 Hidden Markov Model Application 443 Kernel Principal Component Analysis and Hidden Markov Model Regression-Based Fatigue Estimation 444 444 384 xi
xii Contents 13.2.4 13.2.5 13.3 13.3.1 13.3.2 13.4 13.5 13.6 13.7 13.8 13.9 Regularized Regression 445 Other Feature-Based Approaches 445 Measurement of Brain Synchronization and Coherency 446 Linear Measure of Synchronization 446 Nonlinear Measure of Synchronization 448 Evaluation of ERP for Mental Fatigue 451 Separation of P3a and P3b 457 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm 463 Assessing Mental Fatigue by Measuring Functional Connectivity 465 Deep Learning Approaches for Fatigue Evaluation 472 Summary 474 References 474 479 14 EEG-Based Emotion Recognition and Classification 14.1 14.1.1 14.1.2 14.1.3 14.1.4 14.1.4.1 14.1.4.2 14.1.4.3 14.1.4.4 14.1.4.5 14.1.4.6 14.1.4.7 14.1.4.8 14.1.4.9 14.1.4.10 14.1.4.11 14.1.5 14.2 14.2.1 14.2.2 14.2.3 14.2.4 14.3 14.3.1 14.3.2 14.3.3 14.3.4 14.4 14.5 14.6 Introduction 479 Theories and Emotion Classification 480 The Physiological Effects of Emotions 482 Psychology and Psychophysiology of Emotion 485 Emotion Regulation 487 Agency and Intentionality 490 Norm Violation 490 Guilt 491 Shame 491 Embarrassment 491 Pride 491 Indignation and Anger 491 Contempt 491 Pity and Compassion 492 Awe and Elevation 492 Gratitude 492 Emotion-Provoking Stimuli 492 Effect of Emotion on the Brain 494 ERP Change Due to Emotion 494 Changes of Normal Brain Rhythms with Emotion 497 Emotion and Lateral Brain Engagement 498 Perception of Odours and Emotion: Why Are They Related? 498 Emotion-Related Brain Signal Processing and Machine Learning 499 Evaluation of Emotion Based on the Changes in Brain Rhythms 500 Brain
Asymmetricity and Connectivity for Emotion Evaluation 501 Changes in ERPs for Emotion Recognition 504 Combined Features for Emotion Analysis 504 Other Physiological Measurement Modalities Used for Emotion Study 507 Applications 510 Pain Assessment Using EEG 510
Contents 14.7 14.8 Emotion Elicitation and Induction through Virtual Reality 512 Summary 513 References 514 15 EEG Analysis of Neurodegenerative Diseases 15.1 15.2 15.2.1 15.2.2 15.2.3 15.3 15.4 15.5 15.6 15.7 15.8 15.9 Introduction 525 Alzheimer’s Disease 527 Application of Brain Connectivity Estimation to AD and MCI ERP-Based AD Monitoring 532 Other Approaches to EEG-Based AD Monitoring 532 Motor Neuron Disease 537 Parkinson’s Disease 537 Huntington’s Disease 541 Prion Disease 542 Behaviour Variant Frontotemporal Dementia 544 Lewy Body Dementia 545 Summary 545 References 546 16 EEG As A Biomarker for Psychiatric and Neurodevelopmental Disorders 16.1 16.1.1 16.1.1.1 16.1.1.2 16.2 16.2.1 16.2.1.1 16.2.1.2 16.2.2 16.2.2.1 16.2.2.2 16.2.3 16.2.3.1 16.2.3.2 16.2.4 16.2.4.1 16.2.4.2 16.2.5 16.2.5.1 16.2.5.2 16.2.6 16.2.6.1 16.2.6.2 16.2.7 16.2.7.1 Introduction 551 History 551 Different Psychiatric and Neurodevelopmental Disorders NDD Diagnosis 554 EEG Analysis for Different NDDs 554 ADHD 554 ADHD Symptoms and Possible Treatment 554 EEG-Based Diagnosis of ADHD 555 ASO 559 ASD Symptoms and Possible Treatment 559 EEG-Based Diagnosis of ASD 560 Mood Disorder 561 EEG for Monitoring Depression 562 EEG for Monitoring Bipolar Disorder 564 Schizophrenia 565 Schizophrenia Symptoms and Management 565 EEG as the Biomarker for Schizophrenia 566 Anxiety (and Panic) Disorder 568 Definition and Symptoms 568 EEG for Assessing Anxiety 569 Insomnia 571 Symptoms of Insomnia 571 EEG for Insomnia Analysis 572 Schizotypal Personality Disorder 572 What Is Schizotypal Disorder? 572 525 553 528 551 xiii
xiv Contents 16.2.7.2 16.3 EEG Manifestation of Schizotypal Summaiy 573 References 574 17 17.1 17.1.1 17.1.2 17.1.3 17.1.4 17.1.5 17.2 17.2.1 17.2.2 17.2.3 17.2.4 17.2.5 17.2.6 17.3 17.3.1 17.4 17.4.1 17.4.2 17.5 17.6 17.7 17.8 17.9 17.10 17.11 17.12 17.12.1 17.12.2 17.13 17.14 581 Introduction 581 State of the Art in ВСІ 584 ВСІ Terms and Definitions 585 Popular ВСІ Directions 585 Virtual Environment for ВСІ 586 Evolution of ВСІ Design 587 BCI-Related EEG Components 588 Readiness Potential and Its Detection 588 ERD and ERS 588 Transient Beta Activity after the Movement 593 Gamma Band Oscillations 593 Long Delta Activity 593 ERPs 594 Major Problems in ВСІ 594 Preprocessing of the EEGs 595 Multidimensional EEG Decomposition 597 Space-Time-Frequency Method 599 Parallel Factor Analysis 599 Detection and Separation of ERP Signals 601 Estimation of Cortical Connectivity 603 Application of Common Spatial Patterns 606 Multiclass Brain-Computer Interfacing 609 Cell-Cultured ВСІ 610 Recent ВСІ Applications 610 Neuro technology for ВСІ 614 Joint EEG and Other Brain-Scanning Modalities for ВСІ Joint EEG-fNIRS for ВСІ 617 Joint EEG-MEG for ВСІ 618 Performance Measures for ВСІ Systems 618 Summary 619 References 620 18 Joint Analysis of EEG and Other Simultaneously Recorded Brain Functional 18.1 18.2 18.2.1 18.2.1.1 18.2.1.2 18.2.1.3 Neuroimaging Modalities 631 Introduction 631 Fundamental Concepts 631 Functional Magnetic Resonance Imaging 631 Blood Oxygenation Level Dependence 633 Popular fMRI Data Formats 635 Preprocessing of fMRI Data 635 573 Brain-Computer Interfacing Using EEG 617
Contents 18.2.2 18.2.3 18.3 18.3.1 18.3.2 18.3.3 18.3.3.1 18.3.3.2 18.3.4 18.3.4.1 18.3.4.2 18.3.5 18.3.5.1 18.3.5.2 18.3.5.3 18.3.6 18.3.7 18.3.8 18.3.9 18.4 18.5 18.6 Functional Near-Infrared Spectroscopy 636 Magnetoencephalography 640 Joint EEG-fMRI 640 Relation Between EEG and fMRI 640 Model-Based Method for BOLD Detection 642 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG 644 Gradient Artefact Removal from EEG 644 Ballistocardiogram Artefact Removal from EEG 645 BOLD Detection in fMRI 652 Implementation of Different NMF Algorithms for BOLD Detection 653 BOLD Detection Experiments 654 Fusion of EEG and fMRI 659 Extraction of fMRI Time Course from EEG 659 Fusion of EEG and fMRI; Blind Approach 659 Fusion of EEG and fMRI; Model-Based Approach 664 Application to Seizure Detection 664 Investigation of Decision Making in the Brain 666 Application to Schizophrenia 666 Other Applications 667 EEG-NIRS Joint Recording and Fusion 668 MEG-EEG Fusion 672 Summary 672 References 673 Index 681 w
|
| adam_txt |
Contents Preface to the Second Edition xvii Preface to the First Edition xxi List of Abbreviations xxiii 1 Introduction to Electroencephalography 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Introduction 1 History 2 Neural Activities 5 Action Potentials 6 EEG Generation 8 The Brain as a Network Summary 12 References 13 2 EEG Waveforms 2.1 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.9.1 2.9.2 2.9.3 1 12 15 Brain Rhythms 15 EEG Recording and Measurement 18 Conventional Electrode Positioning 21 Unconventional and Special Purpose EEG Recording Systems 24 Invasive Recording of Brain Potentials 26 Conditioning the Signals 27 Sleep 28 Mental Fatigue 30 Emotions 30 Neurodevelopmental Disorders 31 Abnormal EEG Patterns 32 Ageing 33 Mental Disorders 34 Dementia 34 Epileptic Seizure and Nonepileptic Attacks 35 Psychiatric Disorders 39
vi Contents 2.9.4 2.10 External Effects Summary 41 References 42 3 EEG Signal Modelling 3.1 3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.3 3.4 3.4.1 3.4.1.1 3.4.1.2 3.4.2 3.4.3 3.5 3.5.1 3.5.1.1 3.5.1.2 3.5.2 3.5.2.1 3.5.2.2 3.5.3 3.5.4 3.6 3.7 Introduction 47 Physiological Modelling of EEG Generation 47 Integrate-and-Fire Models 49 Phase-Coupled Models 49 Hodgkin-Huxley Model 51 Morris-Lecar Model 54 Generating EEG Signals Based on Modelling the Neuronal Activities 57 Mathematical Models Derived Directly from the EEG Signals 61 Linear Models 61 Prediction Method 61 Prony’s Method 62 Nonlinear Modelling 64 Gaussian Mixture Model 66 Electronic Models 67 Models Describing the Function of the Membrane 67 Lewis Membrane Model 68 Roy Membrane Model 68 Models Describing the Function of a Neuron 68 Lewis Neuron Model 68 The Harmon Neuron Model 71 A Model Describing the Propagation of the Action Pulse in an Axon 72 Integrated Circuit Realizations 72 Dynamic Modelling of Neuron Action Potential Threshold 72 Summary 73 References 73 4 77 Introduction 77 Nonlinearity of the Medium 78 Nonstationarity 79 Signal Segmentation 80 Signal Transforms and Joint Time-Frequency Analysis Wavelet Transform 87 Continuous Wavelet Transform 87 Examples of Continuous Wavelets 89 Discrete-Time Wavelet Transform 89 Multiresolution Analysis 90 Wavelet Transform Using Fourier Transform 93 Reconstruction 94 Synchro-Squeezed Wavelet Transform 95 4.1 4.2 4.3 4.4 4.5 4.5.1 4.5.1.1 4.5.1.2 4.5.1.3 4.5.1.4 4.5.1.5 4.5.1.6 4.5.2 40 47 Fundamentals of EEG Signal Processing 83
Contents 4.5.3 4.6 4.7 4.8 4.9 4.9.1 4.10 5 5.1 5.2 5.2.1 5.2.2 5.3 5.3.1 5.3.2 5.3.3 5.3.3.1 5.3.3.2 5.4 5.4.1 5.4.1.1 5.4.1.2 5.4.2 5.5 5.6 5.7 5.8 5.8.1 5.8.2 5.9 5.10 5.11 5.12 5.13 5.14 6 6.1 6.2 6.3 Ambiguity Function and the Wigner-Ville Distribution 96 Empirical Mode Decomposition 100 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function 101 Filtering and Denoising 104 Principal Component Analysis 107 Singular Value Decomposition 108 Summary 110 References 110 EEG Signal Decomposition 115 Introduction 115 Singular Spectrum Analysis 115 Decomposition 116 Reconstruction 117 Multichannel EEG Decomposition 118 Independent Component Analysis 118 Instantaneous BSS 122 Convolutive BSS 126 General Applications 127 Application of Convolutive BSS to EEG 128 Sparse Component Analysis 129 Standard Algorithms for Sparse Source Recovery 130 Greedy-Based Solution 130 Relaxation-Based Solution 131 k-Sparse Mixtures 131 Nonlinear BSS 133 Constrained BSS 134 Application of Constrained BSS; Example 135 Multiway EEG Decompositions 136 Tensor Factorization for BSS 139 Solving BSS of Nonstationary Sources Using Tensor Factorization 143 Tensor Factorization for Underdetermined Source Separation 149 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 153 Separation of Correlated Sources via Tensor Factorization 153 Common Component Analysis 154 Canonical Correlation Analysis 154 Summary 155 References 155 165 Introduction to Chaos and Dynamical Systems Entropy 166 Kolmogorov Entropy 166 Chaos and Dynamical Analysis 165 vii
viii Contents 6.4 6.5 6.6 6.7 6.7.1 6.7.2 6.8 6.9 6.10 7 7.1 7.2 7.2.1 7.2.2 7.2.3 7.2.4 7.2.5 7.2.6 7.2.7 7.2.7.1 7.2.7.2 7.2.8 7.3 7.3.1 7.3.2 7.3.3 7.3.4 7.3.5 7.3.6 7.3.7 7.3.8 7.3.9 7.3.9.1 7.3.9.2 7.3.9.3 7.3.9.4 7.3.9.5 7.3.9.6 7.3.9.7 7.3.10 7.3.11 7.3.12 7.3.13 Multiscale Fluctuation-Based Dispersion Entropy 167 Lyapunov Exponents 167 Plotting the Attractor Dimensions from Time Series 169 Estimation of Lyapunov Exponents from Time Series 169 Optimum Time Delay 172 Optimum Embedding Dimension 172 Approximate Entropy 173 Using Prediction Order 174 Summary 175 References 175 Machine Learning for EEG Analysis 177 Introduction 177 Clustering Approaches 181 k-Means Clustering Algorithm 181 Iterative Self-Organizing Data Analysis Technique Gap Statistics 183 Density-Based Clustering 184 Affinity-Based Clustering 184 Deep Clustering 184 Semi-Supervised Clustering 185 Basic Semi-Supervised Techniques 185 Deep Semi-Supervised Techniques 186 Fuzzy Clustering 186 Classification Algorithms 187 Decision Trees 188 Random Forest 189 Linear Discriminant Analysis 190 Support Vector Machines 191 k-Nearest Neighbour 199 Gaussian Mixture Model 200 Logistic Regression 200 Reinforcement Learning 201 Artificial Neural Networks 201 Deep Neural Networks 203 Convolutional Neural Networks 205 Autoencoders 207 Variational Autoencoder 208 Recent DNN Approaches 209 Spike Neural Networks 210 Applications of DNNs to EEG 212 Gaussian Processes 212 Neural Processes 213 Graph Convolutional Networks 213 Naïve Bayes Classifier 213 183
Contents 7.3.14 7.3.14.1 7.3.14.2 7.3.14.3 7.4 7.5 Hidden Markov Model 214 Forward Algorithm 216 Backward Algorithm 216 HMM Design 216 Common Spatial Patterns 218 Summary 222 References 223 8 Brain Connectivity and Its Applications 235 Introduction 235 Connectivity through Coherency 238 Phase-Slope Index 240 Multivariate Directionality Estimation 240 Directed Transfer Function 241 Direct DTF 242 Partial Directed Coherence 243 Modelling the Connectivity by Structural Equation Modelling 243 Stockwell Time-Frequency Transform for Connectivity Estimation 246 Inter-Subject EEG Connectivity 247 Objectives 247 Technological Relevance 247 State-Space Model for Estimation of Cortical Interactions 249 Application of Cooperative Adaptive Filters 251 Use of Cooperative Kalman Filter 253 Task-Related Adaptive Connectivity 254 Diffusion Adaptation 255 Brain Connectivity for Cooperative Adaptation 256 Other Applications of Cooperative Learning and Brain Connectivity Estimation 257 Graph Representation of Brain Connectivity 258 Tensor Factorization Approach 259 Summary 262 References 263 8.1 8.2 8.3 8.4 8.4.1 8.4.2 8.4.3 8.5 8.6 8.7 8.7.1 8.7.2 8.8 8.9 8.9.1 8.9.2 8.9.3 8.9.4 8.9.5 8.10 8.11 8.12 269 9 Event-Related Brain Responses 9.1 9.2 9.2.1 9.3 9.3.1 9.3.2 Introduction 269 ERP Generation and Types 269 P300 and its Subcomponents 273 Detection, Separation, and Classification of P300 Signals 274 Using ICA 275 Estimation of Single-Trial Brain Responses by Modelling the ERP Waveforms 277 ERP Source Tracking in Time 278 Time-Frequency Domain Analysis 280 Application of Kalman Filter 284
9.3.3 9.3.4 9.3.5 ix
Contents 9.3.6 9.3.7 9.3.8 9.3.9 9.4 9.5 9.6 Partide Filtering and its Application to ERP Tracking 286 Variational Bayes Method 291 Prony’s Approach for Detection of P300 Signals 293 Adaptive Time-Frequency Methods 297 Brain Activity Assessment Using ERP 298 Application of P300 to ВСІ 299 Summary 300 References 301 10 Localization of Brain Sources 10.1 10.2 10.2.1 10.3 10.4 10.4.1 10.4.2 10.4.3 10.4.4 10.4.5 10.4.6 10.4.7 10.4.8 10.5 10.5.1 10.5.2 10.5.3 10.6 10.6.1 10.6.2 10.7 10.8 10.9 10.10 307 Introduction 307 General Approaches to Source Localization 308 Dipole Assumption 309 Head Model 311 Most Popular Brain Source Localization Approaches 313 EEG Source Localization Using Independent Component Analysis 313 MUSIC Algorithm 313 LORETA Algorithm 317 FOCUSS Algorithm 318 Standardized LORETA 319 Other Weighted Minimum Norm Solutions 320 Evaluation Indices 323 Joint ICA-LORETA Approach 323 Forward Solutions to the Localization Problem 325 Partially Constrained BSS Method 325 Constrained Least-Squares Method for Localization of P3a and P3b 326 Spatial Notch Filtering Approach 328 The Methods Based on Source Tracking 333 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization 333 Hybrid Beamforming - Particle Filtering 336 Determination of the Number of Sources from the EEG/MEG Signals 337 Other Hybrid Methods 340 Application of Machine Learning for EEG/MEG Source Localization 340 Summary 342 References 343 11 Epileptic Seizure Prediction, Detection, and Localization 11.1 11.2 11.2.1 11.2.2 11.3 11.4 11.5 Introduction 351 Seizure Detection 357 Adult Seizure
Detection from EEGs 357 Detection of Neonatal Seizure 363 Chaotic Behaviour of Seizure EEG 366 Seizure Detection from Brain Connectivity 369 Prediction of Seizure Onset from EEG 369 351
Contents 11.6 11.6.1 11.6.2 11.6.3 11.6.4 11.6.5 11.7 11.8 Intracranial and Joint Scalp-Intracrania! Recordings for IED Detection Introduction to IED 384 iEED-Times IED Detection from Scalp EEG 386 A Multiview Approach to IED Detection 391 Coupled Dictionary Learning for IED Detection 391 A Deep Learning Approach to IED Detection 392 Fusion of EEG-fMRI Data for Seizure Prediction 396 Summary 398 References 399 12 407 Introduction 407 Definition of Sleep 407 Sleep Disorder 408 Stages of Sleep 409 NREM Sleep 409 REM Sleep 411 The Influence of Circadian Rhythms 414 Sleep Deprivation 415 Psychological Effects 416 EEG Sleep Analysis and Scoring 416 Detection of the Rhythmic Waveforms and Spindles Employing Blind Source Separation 416 Time-Frequency Analysis of Sleep EEG Using Matching Pursuit 417 Detection of Normal Rhythms and Spindles Using Higher-Order Statistics 421 Sleep Scoring Using Tensor Factorization 423 Application of Neural Networks 425 Model-Based Analysis 426 Detection and Monitoring of Brain Abnormalities during Sleep by EEG and Multimodal PSG Analysis 428 Analysis of Sleep Apnoea 428 EEG and Fibromyalgia Syndrome 431 Sleep Disorders of Neonates 431 Dreams and Nightmares 432 EEG and Consciousness 433 Functional Brain Connectivity for Sleep Analysis 433 Summary 434 References 435 12.1 12.1.1 12.1.2 12.2 12.2.1 12.2.2 12.3 12.4 12.5 12.6 12.6.1 12.6.2 12.6.3 12.6.4 12.6.5 12.6.6 12.7 12.7.1 12.7.2 12.7.3 12.8 12.9 12.10 12.11 13 13.1 13.2 13.2.1 13.2.2 13.2.3 Steep Recognition, Scoring, and Abnormalities EEG-Based Mental Fatigue Monitoring 441 Introduction 441
Feature-Based Machine Learning Approaches 443 Hidden Markov Model Application 443 Kernel Principal Component Analysis and Hidden Markov Model Regression-Based Fatigue Estimation 444 444 384 xi
xii Contents 13.2.4 13.2.5 13.3 13.3.1 13.3.2 13.4 13.5 13.6 13.7 13.8 13.9 Regularized Regression 445 Other Feature-Based Approaches 445 Measurement of Brain Synchronization and Coherency 446 Linear Measure of Synchronization 446 Nonlinear Measure of Synchronization 448 Evaluation of ERP for Mental Fatigue 451 Separation of P3a and P3b 457 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm 463 Assessing Mental Fatigue by Measuring Functional Connectivity 465 Deep Learning Approaches for Fatigue Evaluation 472 Summary 474 References 474 479 14 EEG-Based Emotion Recognition and Classification 14.1 14.1.1 14.1.2 14.1.3 14.1.4 14.1.4.1 14.1.4.2 14.1.4.3 14.1.4.4 14.1.4.5 14.1.4.6 14.1.4.7 14.1.4.8 14.1.4.9 14.1.4.10 14.1.4.11 14.1.5 14.2 14.2.1 14.2.2 14.2.3 14.2.4 14.3 14.3.1 14.3.2 14.3.3 14.3.4 14.4 14.5 14.6 Introduction 479 Theories and Emotion Classification 480 The Physiological Effects of Emotions 482 Psychology and Psychophysiology of Emotion 485 Emotion Regulation 487 Agency and Intentionality 490 Norm Violation 490 Guilt 491 Shame 491 Embarrassment 491 Pride 491 Indignation and Anger 491 Contempt 491 Pity and Compassion 492 Awe and Elevation 492 Gratitude 492 Emotion-Provoking Stimuli 492 Effect of Emotion on the Brain 494 ERP Change Due to Emotion 494 Changes of Normal Brain Rhythms with Emotion 497 Emotion and Lateral Brain Engagement 498 Perception of Odours and Emotion: Why Are They Related? 498 Emotion-Related Brain Signal Processing and Machine Learning 499 Evaluation of Emotion Based on the Changes in Brain Rhythms 500 Brain
Asymmetricity and Connectivity for Emotion Evaluation 501 Changes in ERPs for Emotion Recognition 504 Combined Features for Emotion Analysis 504 Other Physiological Measurement Modalities Used for Emotion Study 507 Applications 510 Pain Assessment Using EEG 510
Contents 14.7 14.8 Emotion Elicitation and Induction through Virtual Reality 512 Summary 513 References 514 15 EEG Analysis of Neurodegenerative Diseases 15.1 15.2 15.2.1 15.2.2 15.2.3 15.3 15.4 15.5 15.6 15.7 15.8 15.9 Introduction 525 Alzheimer’s Disease 527 Application of Brain Connectivity Estimation to AD and MCI ERP-Based AD Monitoring 532 Other Approaches to EEG-Based AD Monitoring 532 Motor Neuron Disease 537 Parkinson’s Disease 537 Huntington’s Disease 541 Prion Disease 542 Behaviour Variant Frontotemporal Dementia 544 Lewy Body Dementia 545 Summary 545 References 546 16 EEG As A Biomarker for Psychiatric and Neurodevelopmental Disorders 16.1 16.1.1 16.1.1.1 16.1.1.2 16.2 16.2.1 16.2.1.1 16.2.1.2 16.2.2 16.2.2.1 16.2.2.2 16.2.3 16.2.3.1 16.2.3.2 16.2.4 16.2.4.1 16.2.4.2 16.2.5 16.2.5.1 16.2.5.2 16.2.6 16.2.6.1 16.2.6.2 16.2.7 16.2.7.1 Introduction 551 History 551 Different Psychiatric and Neurodevelopmental Disorders NDD Diagnosis 554 EEG Analysis for Different NDDs 554 ADHD 554 ADHD Symptoms and Possible Treatment 554 EEG-Based Diagnosis of ADHD 555 ASO 559 ASD Symptoms and Possible Treatment 559 EEG-Based Diagnosis of ASD 560 Mood Disorder 561 EEG for Monitoring Depression 562 EEG for Monitoring Bipolar Disorder 564 Schizophrenia 565 Schizophrenia Symptoms and Management 565 EEG as the Biomarker for Schizophrenia 566 Anxiety (and Panic) Disorder 568 Definition and Symptoms 568 EEG for Assessing Anxiety 569 Insomnia 571 Symptoms of Insomnia 571 EEG for Insomnia Analysis 572 Schizotypal Personality Disorder 572 What Is Schizotypal Disorder? 572 525 553 528 551 xiii
xiv Contents 16.2.7.2 16.3 EEG Manifestation of Schizotypal Summaiy 573 References 574 17 17.1 17.1.1 17.1.2 17.1.3 17.1.4 17.1.5 17.2 17.2.1 17.2.2 17.2.3 17.2.4 17.2.5 17.2.6 17.3 17.3.1 17.4 17.4.1 17.4.2 17.5 17.6 17.7 17.8 17.9 17.10 17.11 17.12 17.12.1 17.12.2 17.13 17.14 581 Introduction 581 State of the Art in ВСІ 584 ВСІ Terms and Definitions 585 Popular ВСІ Directions 585 Virtual Environment for ВСІ 586 Evolution of ВСІ Design 587 BCI-Related EEG Components 588 Readiness Potential and Its Detection 588 ERD and ERS 588 Transient Beta Activity after the Movement 593 Gamma Band Oscillations 593 Long Delta Activity 593 ERPs 594 Major Problems in ВСІ 594 Preprocessing of the EEGs 595 Multidimensional EEG Decomposition 597 Space-Time-Frequency Method 599 Parallel Factor Analysis 599 Detection and Separation of ERP Signals 601 Estimation of Cortical Connectivity 603 Application of Common Spatial Patterns 606 Multiclass Brain-Computer Interfacing 609 Cell-Cultured ВСІ 610 Recent ВСІ Applications 610 Neuro technology for ВСІ 614 Joint EEG and Other Brain-Scanning Modalities for ВСІ Joint EEG-fNIRS for ВСІ 617 Joint EEG-MEG for ВСІ 618 Performance Measures for ВСІ Systems 618 Summary 619 References 620 18 Joint Analysis of EEG and Other Simultaneously Recorded Brain Functional 18.1 18.2 18.2.1 18.2.1.1 18.2.1.2 18.2.1.3 Neuroimaging Modalities 631 Introduction 631 Fundamental Concepts 631 Functional Magnetic Resonance Imaging 631 Blood Oxygenation Level Dependence 633 Popular fMRI Data Formats 635 Preprocessing of fMRI Data 635 573 Brain-Computer Interfacing Using EEG 617
Contents 18.2.2 18.2.3 18.3 18.3.1 18.3.2 18.3.3 18.3.3.1 18.3.3.2 18.3.4 18.3.4.1 18.3.4.2 18.3.5 18.3.5.1 18.3.5.2 18.3.5.3 18.3.6 18.3.7 18.3.8 18.3.9 18.4 18.5 18.6 Functional Near-Infrared Spectroscopy 636 Magnetoencephalography 640 Joint EEG-fMRI 640 Relation Between EEG and fMRI 640 Model-Based Method for BOLD Detection 642 Simultaneous EEG-fMRI Recording: Artefact Removal from EEG 644 Gradient Artefact Removal from EEG 644 Ballistocardiogram Artefact Removal from EEG 645 BOLD Detection in fMRI 652 Implementation of Different NMF Algorithms for BOLD Detection 653 BOLD Detection Experiments 654 Fusion of EEG and fMRI 659 Extraction of fMRI Time Course from EEG 659 Fusion of EEG and fMRI; Blind Approach 659 Fusion of EEG and fMRI; Model-Based Approach 664 Application to Seizure Detection 664 Investigation of Decision Making in the Brain 666 Application to Schizophrenia 666 Other Applications 667 EEG-NIRS Joint Recording and Fusion 668 MEG-EEG Fusion 672 Summary 672 References 673 Index 681 w |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Sanei, Saeid Chambers, Jonathon A. |
| author_GND | (DE-588)104607041X (DE-588)1073169103 |
| author_facet | Sanei, Saeid Chambers, Jonathon A. |
| author_role | aut aut |
| author_sort | Sanei, Saeid |
| author_variant | s s ss j a c ja jac |
| building | Verbundindex |
| bvnumber | BV048867782 |
| classification_rvk | CZ 1300 YG 2400 |
| ctrlnum | (OCoLC)1376409735 (DE-599)BVBBV048867782 |
| dewey-full | 616.8/047547 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 616 - Diseases |
| dewey-raw | 616.8/047547 |
| dewey-search | 616.8/047547 |
| dewey-sort | 3616.8 547547 |
| dewey-tens | 610 - Medicine and health |
| discipline | Psychologie Medizin |
| discipline_str_mv | Psychologie Medizin |
| edition | Second edition |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01973nam a2200457zc 4500</leader><controlfield tag="001">BV048867782</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230428 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">230320s2022 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781119386940</subfield><subfield code="9">978-1-119-38694-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1376409735</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048867782</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-739</subfield><subfield code="a">DE-355</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">616.8/047547</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">CZ 1300</subfield><subfield code="0">(DE-625)19229:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">YG 2400</subfield><subfield code="0">(DE-625)153482:12905</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sanei, Saeid</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)104607041X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="240" ind1="1" ind2="0"><subfield code="a">EEG signal processing</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">EEG signal processing and machine learning</subfield><subfield code="c">Saeid Sanei ; Jonathon A. Chambers</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xxxiv, 714 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electroencephalography</subfield><subfield code="a">Signal processing</subfield><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chambers, Jonathon A.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1073169103</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, PDF</subfield><subfield code="z">978-1-119-38692-6</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, EPUB</subfield><subfield code="z">978-1-119-38693-3</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Regensburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034132745&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034132745</subfield></datafield></record></collection> |
| id | DE-604.BV048867782 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:43:34Z |
| indexdate | 2024-07-10T09:48:18Z |
| institution | BVB |
| isbn | 9781119386940 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034132745 |
| oclc_num | 1376409735 |
| open_access_boolean | |
| owner | DE-739 DE-355 DE-BY-UBR |
| owner_facet | DE-739 DE-355 DE-BY-UBR |
| physical | xxxiv, 714 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Wiley |
| record_format | marc |
| spelling | Sanei, Saeid Verfasser (DE-588)104607041X aut EEG signal processing EEG signal processing and machine learning Saeid Sanei ; Jonathon A. Chambers Second edition Hoboken, NJ Wiley 2022 xxxiv, 714 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Electroencephalography Signal processing Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Signalverarbeitung (DE-588)4054947-1 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Chambers, Jonathon A. Verfasser (DE-588)1073169103 aut Erscheint auch als Online-Ausgabe, PDF 978-1-119-38692-6 Erscheint auch als Online-Ausgabe, EPUB 978-1-119-38693-3 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034132745&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sanei, Saeid Chambers, Jonathon A. EEG signal processing and machine learning Electroencephalography Signal processing Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Signalverarbeitung (DE-588)4054947-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4054947-1 (DE-588)4033447-8 |
| title | EEG signal processing and machine learning |
| title_alt | EEG signal processing |
| title_auth | EEG signal processing and machine learning |
| title_exact_search | EEG signal processing and machine learning |
| title_exact_search_txtP | EEG signal processing and machine learning |
| title_full | EEG signal processing and machine learning Saeid Sanei ; Jonathon A. Chambers |
| title_fullStr | EEG signal processing and machine learning Saeid Sanei ; Jonathon A. Chambers |
| title_full_unstemmed | EEG signal processing and machine learning Saeid Sanei ; Jonathon A. Chambers |
| title_short | EEG signal processing and machine learning |
| title_sort | eeg signal processing and machine learning |
| topic | Electroencephalography Signal processing Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Signalverarbeitung (DE-588)4054947-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Electroencephalography Signal processing Machine learning Maschinelles Lernen Signalverarbeitung Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034132745&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT saneisaeid eegsignalprocessing AT chambersjonathona eegsignalprocessing AT saneisaeid eegsignalprocessingandmachinelearning AT chambersjonathona eegsignalprocessingandmachinelearning |