EEG Signal Processing and Machine Learning
Description
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.
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Persons
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.
Content
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
List of Abbreviations
- 3D
- Three-dimensional
- AASM
- American Academic of Sleep Medicine
- ACC
- Anterior cingulate cortex
- ACE
- Addenbrooke's cognitive examination
- ACR
- Accuracy of responses
- ACT
- Adaptive chirplet transform
- AD
- Alzheimer's disease
- ADC
- Analogue-to-digital converter
- ADD
- AD patients with mild dementia
- ADD
- Attention-deficit disorder
- ADHD
- Attention-deficit hyperactivity disorder
- AE
- Approximate entropy
- AE
- Autoencoder
- AEP
- Audio evoked potentials
- AfC
- Affective computing
- Ag-AgCl
- Silver-silver chloride
- AI
- Artificial intelligence
- AIC
- Akaike information criterion
- ALE
- Adaptive line enhancer
- ALF
- Adaptive standardized LORETA/FOCUSS
- ALM
- Augmented Lagrange multipliers method
- ALS
- Alternating least squares
- ALS
- Amyotrophic lateral sclerosis
- AMDF
- Average magnitude difference function
- AMI
- Average mutual information
- AMM
- Augmented mixing matrix
- ANN
- Artificial neural network
- AOD
- Auditory oddball
- AP
- Action potential
- ApEn
- Approximate entropy
- APGARCH
- Asymmetric power GARCH
- AR
- Autoregressive modelling
- ARMA
- Autoregressive moving average
- ASCOT
- Adaptive slope of wavelet coefficient counts over various thresholds
- ASD
- Autism spectrum disorder
- ASDA
- American Sleep Disorders Association
- AsI
- Asymmetry index
- ASR
- Automatic speaker recognition
- ASS
- Average artefact subtraction
- AUC
- Area under the curve
- BAS
- Behavioural activation system
- BBCI
- Berlin BCI
- BBI
- Brain-to-brain interface
- BCG
- Ballistocardiogram
- BCI
- Brain-computer interfacing
- BDS
- Brock, Dechert, and Scheinkman
- BEM
- Boundary-element method
- BF
- Beamformer
- BGD
- Bootstrapped geometric difference
- BIC
- Bayesian information criterion
- BIS
- Behavioural inhibition system
- BIS
- Bispectral index
- BMI
- Brain-machine interfacing
- BOLD
- Blood oxygenation level dependent
- BP
- Bereitschaftspotential
- BP
- Bipolar disorder
- Brain/MINDS
- Brain Mapping by Integrated Neurotechnologies for Disease Studies
- BSE
- Blind source extraction
- BSR
- Burst-suppression ratio
- BSS
- Blind source separation
- bvFTD
- Behaviour variant frontotemporal dementia
- Ca
- Calcium
- CAE
- Contractive autoencoder
- CANDECOMP
- Canonical decomposition
- CBD
- Corticobasal degeneration
- CBF
- Cerebral blood flow
- CCA
- Canonical correlation analysis
- CEEMDAN
- Complete ensemble EMD with adaptive noise
- CF
- Characteristic function
- CF
- Cognitive fluctuation
- CFS
- Chronic fatigue syndrome
- Cl
- Chloride
- CDLSA
- Coupled dictionary learning with sparse approximation
- CDR
- Current distributed-source reconstruction
- CI
- Covariance intersection
- cICA
- Constrained ICA
- CIT
- Concealed information test
- CJD
- Creutzfeldt-Jakob disease
- CMA
- Circumplex model of affects
- CMTF
- Coupled matrix and tensor factorizations
- CMOS
- Complementary metal oxide semiconductor
- CNN
- Convolutional neural network
- CNS
- Central nervous system
- CORCONDIA
- Core consistency diagnostic
- CoSAMP
- Compressive sampling matching pursuit
- CPS
- Cyber-physical systems
- CRBPF
- Constrained Rao-Blackwellised particle filter
- CSA
- Central sleep apnoea
- CSD
- Current source density
- CSF
- Cerebrospinal fluid
- CSP
- Common spatial patterns
- CT
- Computerized tomography
- DAE
- Denoising autoencoder
- DARPA
- Defence Advanced Research Projects Agency
- DASM
- Differential asymmetry
- DBS
- Deep brain stimulation
- DC
- Direct current
- DCAU
- Differential Causality
- DCM
- Dynamic causal modelling
- DCT
- Discrete cosine transform
- dDTF
- Direct directed transfer function
- DE
- Differential entropy
- DeconvNet
- Deconvolutional ANN
- DFT
- Discrete Fourier transform
- DFV
- Dominant frequency variability
- DHT
- Discrete Hermite transform
- DL
- Diagonal loading
- DLE
- Digitally linked ears
- DM
- Default mode
- DMN
- Default mode network
- DNN
- Deep neural network
- DPF
- Differential pathlength factor
- DSM
- Diagnostic and Statistical Manual
- DSTCLN
- Deep spatio-temporal convolutional bidirectional long short-term memory network
- DT
- Decision tree
- DTF
- Directed transfer function
- DTI
- Diffusion tensor imaging
- DUET
- Degenerate unmixing estimation technique
- DWT
- Discrete wavelet transform
- ECD
- Electric current dipole
- ECD
- Equivalent current dipole
- ECG
- Electrocardiogram
- ECG
- Electrocardiography
- ECoG
- Electrocorticogram
- ECT
- Electroconvulsive therapy
- ED
- Error distance
- EEG
- Electroencephalogram
- EEG
- Electroencephalography
- EEMD
- Ensemble empirical mode decomposition
- EGARCH
- Exponential GARCH
- EGG
- Electrogastrography
- EKG
- Electrocardiogram
- EKG
- Electrocardiography
- EM
- Expectation maximization
- EMD
- Empirical mode decomposition
- EMG
- Electromyogram
- EMG
- Electromyography
- ENet
- Efficient neural network
- EOG
- Electro-oculogram
- EP
- Evoked potential
- EPN
- Early posterior negativity
- EPSP
- Excitatory post-synaptic potential
- ERBM
- Entropy rate bound minimization
- ERD
- Event-related desynchronization
- ERN
- Error-related negativity
- ERP
- Event-related potential
- ERS
- Event-related synchronization
- FA
- Factor analysis
- FC
- Functional connectivity
- FCM
- Fuzzy c-means
- FD
- Fractal dimension
- FDA
- Food and Drug Administration
- FDispEn
- Fluctuation-based dispersion entropy
- FDR
- False detection rate
- FEM
- Finite element model
- FFNN
- Feed forward neural network
- FET
- Field-effect transistor
- fICA
- Fast independent component analysis
- FIR
- Finite impulse response
- fMRI
- Functional magnetic resonance imaging
- FMS
- Fibromyalgia syndrome
- FN
- False negative
- fNIRS
- Functional near-infrared spectroscopy
- FO
- Foramen ovale
- FOBSS
- First order blind source separation
- FOCUSS
- Focal underdetermined system solver
- FOOBI
- Fourth order cumulant based blind identification
- FP
- False positive
- FRDA
- Frontal rhythmic delta activity
- FRN
- Feedback related negativity
- FSOR
- Feature selection with orthogonal regression
- FSP
- Falsely detected source number (position)
- FTD
- Frontotemporal dementia
- FuzEn
- Fuzzy entropy
- GA
- Genetic algorithm
- GAD
- General anxiety disorder
- GAN
- Generative adversarial network
- GARCH
- Generalized autoregressive conditional heteroskedasticity
- GARCH-M
- GARCH-in-mean
- GC
- Granger causality
- GCN
- Graph convolutional network
- GFNN
- Global false nearest neighbours
- GJR-GARCH
- Glosten, Jagannathan, & Runkle GARCH
- GLM
- General linear model
- GMM
- Gaussian mixture model
- GP
- Gaussian process
- GP-LR
- Gaussian process logistic regression
- GSCCA
- Group sparse canonical correlation...
