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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title Page
Copyright Page
Preface to the Second Edition
Preface to the First Edition
List of Abbreviations
1 Introduction to Electroencephalography
1.1 Introduction
1.2 History
1.3 Neural Activities
1.4 Action Potentials
1.5 EEG Generation
1.6 The Brain as a Network
1.7 Summary
References
2 EEG Waveforms
2.1 Brain Rhythms
2.2 EEG Recording and Measurement
2.3 Sleep
2.4 Mental Fatigue
2.5 Emotions
2.6 Neurodevelopmental Disorders
2.7 Abnormal EEG Patterns
2.8 Ageing
2.9 Mental Disorders
2.10 Summary
References
3 EEG Signal Modelling
3.1 Introduction
3.2 Physiological Modelling of EEG Generation
3.3 Generating EEG Signals Based on Modelling the Neuronal Activities
3.4 Mathematical Models Derived Directly from the EEG Signals
3.5 Electronic Models
3.6 Dynamic Modelling of Neuron Action Potential Threshold
3.7 Summary
References
4 Fundamentals of EEG Signal Processing
4.1 Introduction
4.2 Nonlinearity of the Medium
4.3 Nonstationarity
4.4 Signal Segmentation
4.5 Signal Transforms and Joint Time–Frequency Analysis
4.6 Empirical Mode Decomposition
4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function
4.8 Filtering and Denoising
4.9 Principal Component Analysis
4.10 Summary
References
5 EEG Signal Decomposition
5.1 Introduction
5.2 Singular Spectrum Analysis
5.3 Multichannel EEG Decomposition
5.4 Sparse Component Analysis
5.5 Nonlinear BSS
5.6 Constrained BSS
5.7 Application of Constrained BSS; Example
5.8 Multiway EEG Decompositions
5.9 Tensor Factorization for Underdetermined Source Separation
5.10 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain
5.11 Separation of Correlated Sources via Tensor Factorization
5.12 Common Component Analysis
5.13 Canonical Correlation Analysis
5.14 Summary
References
6 Chaos and Dynamical Analysis
6.1 Introduction to Chaos and Dynamical Systems
6.2 Entropy
6.3 Kolmogorov Entropy
6.4 Multiscale Fluctuation‐Based Dispersion Entropy
6.5 Lyapunov Exponents
6.6 Plotting the Attractor Dimensions from Time Series
6.7 Estimation of Lyapunov Exponents from Time Series
6.8 Approximate Entropy
6.9 Using Prediction Order
6.10 Summary
References
7 Machine Learning for EEG Analysis
7.1 Introduction
7.2 Clustering Approaches
7.3 Classification Algorithms
7.4 Common Spatial Patterns
7.5 Summary
References
8 Brain Connectivity and Its Applications
8.1 Introduction
8.2 Connectivity through Coherency
8.3 Phase‐Slope Index
8.4 Multivariate Directionality Estimation
8.5 Modelling the Connectivity by Structural Equation Modelling
8.6 Stockwell Time–Frequency Transform for Connectivity Estimation
8.7 Inter‐Subject EEG Connectivity
8.8 State‐Space Model for Estimation of Cortical Interactions
8.9 Application of Cooperative Adaptive Filters
8.10 Graph Representation of Brain Connectivity
8.11 Tensor Factorization Approach
8.12 Summary
References
9 Event‐Related Brain Responses
9.1 Introduction
9.2 ERP Generation and Types
9.3 Detection, Separation, and Classification of P300 Signals
9.4 Brain Activity Assessment Using ERP
9.5 Application of P300 to BCI
9.6 Summary
References
10 Localization of Brain Sources
10.1 Introduction
10.2 General Approaches to Source Localization
10.3 Head Model
10.4 Most Popular Brain Source Localization Approaches
10.5 Forward Solutions to the Localization Problem
10.6 The Methods Based on Source Tracking
10.7 Determination of the Number of Sources from the EEG/MEG Signals
10.8 Other Hybrid Methods
10.9 Application of Machine Learning for EEG/MEG Source Localization
10.10 Summary
References
11 Epileptic Seizure Prediction, Detection, and Localization
11.1 Introduction
11.2 Seizure Detection
11.3 Chaotic Behaviour of Seizure EEG
11.4 Seizure Detection from Brain Connectivity
11.5 Prediction of Seizure Onset from EEG
11.6 Intracranial and Joint Scalp–Intracranial Recordings for IED Detection
11.7 Fusion of EEG–fMRI Data for Seizure Prediction
11.8 Summary
References
12 Sleep Recognition, Scoring, and Abnormalities
12.1 Introduction
12.2 Stages of Sleep
12.3 The Influence of Circadian Rhythms
12.4 Sleep Deprivation
12.5 Psychological Effects
12.6 EEG Sleep Analysis and Scoring
12.7 Detection and Monitoring of Brain Abnormalities during Sleep by EEG and Multimodal PSG Analysis
12.8 Dreams and Nightmares
12.9 EEG and Consciousness
12.10 Functional Brain Connectivity for Sleep Analysis
12.11 Summary
References
13 EEG‐Based Mental Fatigue Monitoring
13.1 Introduction
13.2 Feature‐Based Machine Learning Approaches
13.3 Measurement of Brain Synchronization and Coherency
13.4 Evaluation of ERP for Mental Fatigue
13.5 Separation of P3a and P3b
13.6 A Hybrid EEG–ERP‐Based Method for Fatigue Analysis Using an Auditory Paradigm
13.7 Assessing Mental Fatigue by Measuring Functional Connectivity
13.8 Deep Learning Approaches for Fatigue Evaluation
13.9 Summary
References
14 EEG‐Based Emotion Recognition and Classification
14.1 Introduction
14.2 Effect of Emotion on the Brain
14.3 Emotion‐Related Brain Signal Processing and Machine Learning
14.4 Other Physiological Measurement Modalities Used for Emotion Study
14.5 Applications
14.6 Pain Assessment Using EEG
14.7 Emotion Elicitation and Induction through Virtual Reality
14.8 Summary
References
15 EEG Analysis of Neurodegenerative Diseases
15.1 Introduction
15.2 Alzheimer's Disease
15.3 Motor Neuron Disease
15.4 Parkinson's Disease
15.5 Huntington's Disease
15.6 Prion Disease
15.7 Behaviour Variant Frontotemporal Dementia
15.8 Lewy Body Dementia
15.9 Summary
References
16 EEG As A Biomarker for Psychiatric and Neurodevelopmental Disorders
16.1 Introduction
16.2 EEG Analysis for Different NDDs
16.3 Summary
References
17 Brain–Computer Interfacing Using EEG
17.1 Introduction
17.2 BCI‐Related EEG Components
17.3 Major Problems in BCI
17.4 Multidimensional EEG Decomposition
17.5 Detection and Separation of ERP Signals
17.6 Estimation of Cortical Connectivity
17.7 Application of Common Spatial Patterns
17.8 Multiclass Brain–Computer Interfacing
17.9 Cell‐Cultured BCI
17.10 Recent BCI Applications
17.11 Neurotechnology for BCI
17.12 Joint EEG and Other Brain‐Scanning Modalities for BCI
17.13 Performance Measures for BCI Systems
17.14 Summary
References
18 Joint Analysis of EEG and Other Simultaneously Recorded Brain Functional Neuroimaging Modalities
18.1 Introduction
18.2 Fundamental Concepts
18.3 Joint EEG–fMRI
18.4 EEG–NIRS Joint Recording and Fusion
18.5 MEG–EEG Fusion
18.6 Summary
References
Index
End User License Agreement
List of Tables
Chapter 5
Table 5.1 Maximum number of separable sources using different tensor‐based ...
Chapter 9
Table 9.1
K
n
and
P
n
for different recursive algorithms.
Chapter 10
Table 10.1 Most popular academic and commercial software packages that can ...
Chapter 12
Table 12.1 The time, frequency, and amplitudes of both SWA and sleep spindle...
Chapter 18
Table 18.1 Normalized correlation between the extracted BOLD time course and...
List of Illustrations
Chapter 1
Figure 1.1 Hieroglyphic symbol for the ancient Egyptian word for ‘brain’.
Figure 1.2 Physiologists Adolf Beck (Polish, 1863–1942) on the left and Vlad...
Figure 1.3 The neuron membrane potential changes and current flow during syn...
Figure 1.4 An example of an AP.
Figure 1.5 Changing the membrane potential for a giant squid by closing the ...
Figure 1.6 Structure of a neuron.
Figure 1.7 The head layers from brain to scalp.
Figure 1.8 Diagrammatic representation of the major parts of the brain.
Chapter 2
Figure 2.1 Five (can be categorized as four) typical dominant brain normal r...
Figure 2.2 Different waveforms that may appear in the EEG while awake or dur...
Figure 2.3 Conventional 10–20 EEG electrode positions for the placement of 2...
Figure 2.4 A diagrammatic representation of 10–20 electrode settings for 75 ...
Figure 2.5 A typical set of EEG signals during approximately seven seconds o...
Figure 2.6 Wearable and tattooed EEG systems/electrodes.
Figure 2.7 Electrocorticography.
Figure 2.8 Foramen ovale holes within facial skeleton.
Figure 2.9 Foramen ovale electrodes.
Figure 2.10 A 4‐mm diameter Stentrode with electrode contacts within the ste...
Figure 2.11 A set of normal EEG signals affected by an eye‐blinking artefact...
Figure 2.12 A multichannel EEG set with the clear appearance of ECG signals ...
Figure 2.13 A set of multichannel EEG signals from a patient suffering from ...
Figure 2.14 Bursts of 3–7 Hz seizure activity in a set of adult EEG signals....
Figure 2.15 Generalized tonic–clonic (grand mal) seizure. The seizure appear...
Chapter 3
Figure 3.1 (a) A network of three neurons that exchange electric signals, na...
Figure 3.2 A pair of oscillators weakly coupled via the perturbation functio...
Figure 3.3 The Hodgkin–Huxley excitation model.
Figure 3.4 A single AP in response to a transient stimulation based on the H...
Figure 3.5 The AP from a Hodgkin–Huxley oscillatory model with reduced maxim...
Figure 3.6 Simulation of an AP within the Morris–Lecar model. The model para...
Figure 3.7 An illustration of the bursting behaviour that can be generated b...
Figure 3.8 A nonlinear lumped model for generating the rhythmic activity of ...
Figure 3.9 The local EEG model (LEM). The thalamocortical relay neurons are ...
Figure 3.10 Simplified model for brain cortical alpha generation. The input ...
Figure 3.11 A two‐column model for generation of VEP. Two connectivity const...
Figure 3.12 A linear model for the generation of EEG signals.
Figure 3.13 Mixture of Gaussian (dotted curves) models of a multimodal unkno...
Figure 3.14 The Lewis membrane model [57].
Figure 3.15 Circuits simulating (a) potassium and (b) sodium conductances in...
Figure 3.16 The Lewis neuron model from 1968 [57].
Figure 3.17 The Harmon neuron model [60].
Figure 3.18 The Lewis model for simulation of the propagation of the action ...
Chapter 4
Figure 4.1 An EEG set of tonic–clonic seizure signals including three segmen...
Figure 4.2 (a) An EEG seizure signal including preictal, ictal, and posticta...
Figure 4.3 Single‐channel EEG spectrum. (a) A segment of an EEG signal with ...
Figure 4.4 TF representation of an epileptic waveform in (a) for different t...
Figure 4.5 Morlet's wavelet: real and imaginary parts shown respectively in ...
Figure 4.6 Mexican hat wavelet.
Figure 4.7 The filter bank associated with the multiresolution analysis.
Figure 4.8 (a) A segment of a signal consisting of two modulated components,...
Figure 4.9 Illustration of
for the Choi–Williams distribution.
Figure 4.10 Cross‐spectral coherence for a set of three electrode EEGs, one ...
Figure 4.11 An adaptive noise canceller.
Figure 4.12 The general application of PCA.
Figure 4.13 Adaptive estimation of the weight vector
w
(
n
).
Chapter 5
Figure 5.1 Separation of EMG and ECG using the SSA technique; top signal is ...
Figure 5.2 BSS concept; mixing and blind separation of the EEG signals.
Figure 5.3 A sample of an EEG signal simultaneously recorded with fMRI.
Figure 5.4 The EEG signals after removal of the scanner artefact.
Figure 5.5 Estimated independent components of a set of EEG signals, acquire...
Figure 5.6 Topographic maps, each illustrating an IC. It is clear that the s...
Figure 5.7 Tensor and its various modes.
Figure 5.8 Tensor factorization using: (a) Tucker and (b) PARAFAC models.
Figure 5.9 A tensor representation of a set of multichannel EEG signals. (a)...
Figure 5.10 The extracted factors using STF–TS modelling. (a) and (b) illust...
Figure 5.11 Restoration of the EEG signals in (a) from multiple eye blinks a...
Figure 5.12 Representation of the first two components (a, b) in the time–sp...
Figure 5.13 The results of application of the FOBSS algorithm to a set of sc...
Figure 5.14 The intracranial records from three electrodes. These signals we...
Chapter 6
Figure 6.1 Generated chaotic signal using the model
x
(
n
) →
αx
(
n
)(1 −
x
(
Figure 6.2 The attractors for (a) a sinusoid and (b) the above chaotic time ...
Figure 6.3 The reference and the model trajectories, evolution of the error,...
Figure 6.4 (a) The signal and (b) prediction order measured for overlapping ...
Chapter 7
Figure 7.1 A two‐dimensional feature space with three clusters, each with me...
Figure 7.2 Schematic diagram of deep clustering [16]. The deep features are ...
Figure 7.3 An example of a decision tree to show the humidity level at 9:00 ...
Figure 7.4 The SVM separating hyperplane and support vectors for a separable...
Figure 7.5 Soft margin and the concept of a slack parameter.
Figure 7.6 Nonlinear discriminant hyperplane (separation margin) for SVM.
Figure 7.7 Output class distributions for (a) close to zero and (b) non‐zero...
Figure 7.8 A biological neuron that expresses the fundamental elements of a ...
Figure 7.9 A simple three‐layer NN for node localization in sensor networks ...
Figure 7.10 Sigmoid (a) and ReLU (b) activation functions.
Figure 7.11 An example of a CNN and the operations in different layers.
Figure 7.12 Structure of an autoencoder NN.
Figure 7.13 The schematic of the VAE structure.
refers to Kullback–Leibler...
Figure 7.14 (a) The association of biological neuron activity and an artific...
Figure 7.15 Rate‐based encoding (on the left) and temporal encoding (on the ...
Figure 7.16 A synthetic ECG segment of a healthy individual and its correspo...
Figure 7.17 An HMM for detection of a healthy heart from an ECG sequence.
Figure 7.18 CSP patterns related to right‐hand movement (a) and left‐hand mo...
Chapter 8
Figure 8.1 Cross‐spectral coherence for a set of three electrode EEGs, one s...
Figure 8.2 Connectivity pattern imposed in the generation of simulated signa...
Figure 8.3 The result of application of S‐transform to a set of simulated so...
Figure 8.4 Representation of node
k
and its neighbouring nodes for diffusion...
Figure 8.5 The use of brain connectivity for diffusion adaptation filtering....
Figure 8.6 An illustration of brain connectivity pattern. EEG signals collec...
Figure 8.7 Variation of combination weights (brain connectivity parameters) ...
Figure 8.8 Example of modelling the multirelational social network as a tens...
Figure 8.9 Conceptual model of tensors decomposition for linked multiway BSS...
Chapter 9
Figure 9.1 Four P100 components: (a) two normal P100 and (b) two abnormal P1...
Figure 9.2 The average ERP signals for normal and alcoholic subjects. The cu...
Figure 9.3 Typical P3a and P3b subcomponents of a P300 ERP signal viewed at ...
Figure 9.4 Block diagram of the ICA‐based algorithm proposed in [43]. Three ...
Figure 9.5 Synthetic ERP templates including a number of delayed Gaussian an...
Figure 9.6 The results of the ERP detection algorithm [47]. The scatter plot...
Figure 9.7 The average P3a and P3b for a schizophrenic patient (a) and (b) r...
Figure 9.8 Construction of an ERP signal using a WN. The nodes in the hidden...
Figure 9.9 Dynamic variations of ERP signals. (a) First stimulus and (b) twe...
Figure 9.10 Estimated ERPs by applying KF and PF, (a) and (b) ERP latency ov...
Figure 9.11 Estimated (a) amplitude and (b) latency of P3a (bold line) and P...
Figure 9.12 The chirplets extracted from a simulated EEG‐type waveform [75]....
Chapter 10
Figure 10.1 The magnetic field B at each electrode is calculated with respec...
Figure 10.2 The magnetic field B at each electrode is calculated with respec...
Figure 10.3 The steps in using MRI data to build up a head model. (a) Origin...
Figure 10.4 Localization results for (a) the schizophrenic patients and (b) ...
Figure 10.5 Flow diagram of inverse methods used for EEG source localization...
Figure 10.6 The locations of the P3a and P3b sources for five patients in a ...
Figure 10.7 The locations of the P3a and P3b sources for five healthy indivi...
Figure 10.8 Localization plot for one source uncorrelated with other sources...
Figure 10.9 Percentage of successful localizations for various SNRs for the ...
Figure 10.10 Percentage of successful localizations for various SNRs for the...
Figure 10.11 Localization plot for P3a, circles, o, and P3b, squares, □, for...
Figure 10.12 Localization plot for the P3a, circles, o, and P3b, squares, □,...
Figure 10.13 Topographies or power profiles of real MEG data obtained using ...
Chapter 11
Figure 11.1 Two segments of EEG signals each from a patient suffering: (a) g...
Figure 11.2 The CNN architecture proposed in [48]. The first layer (Conv2D) ...
Figure 11.3 (a) A sample of IED recorded using intracranial foramen ovale el...
Figure 11.4 The main three different neonate seizure patterns. (a) Low ampli...
Figure 11.5 Eight seconds of EEG signals from eight out of 16 scalp electrod...
Figure 11.6 The four independent components obtained by applying BSS to the ...
Figure 11.7 The smoothed λ
1
evolution over time for two intracranial electro...
Figure 11.8 Smoothed λ
1
evolution over time for two independent components I...
Figure 11.9 (a) A segment of eight seconds of EEG signals (with zero mean) f...
Figure 11.10 (a) Intracranial EEG analysis: three‐point smoothed λ
1
evolutio...
Figure 11.11 Smoothed λ
1
evolution for a focal seizure estimated from the in...
Figure 11.12 (a) Smoothed λ
1
evolution of four intracranial electrodes for a...
Figure 11.13 The schematic for the proposed LRCN seizure prediction algorith...
Figure 11.14 Foramen ovale holes where the subdural electrodes are inserted ...
Figure 11.15 Basal (left) and lateral (right) X‐ray images showing the inser...
Figure 11.16 A segment of concurrent multichannel data. The first 22 signals...
Figure 11.17 The scoring histogram provided by an expert in epilepsy.
Figure 11.18 Classification accuracy of the ensemble classifier with respect...
Figure 11.19 The ratio of detected IEDs (from the scalp EEG), to the total n...
Figure 11.20 Detected scalp‐invisible IEDs with true positive on the top and...
Figure 11.21 Examples of single‐channel reconstructed intracranial signals f...
Figure 11.22 Topology of the DNN for mapping scalp to iEEGs.
X
is the scalp ...
Figure 11.23 A schematic comparison between the proposed method (left) and a...
Figure 11.24 Estimation of iEEG for two IED segments and a non‐IED segment (...
Figure 11.25 A segment of EEG signals affected by the scanner ballistocardio...
Figure 11.26 Schematic diagram of the topographic map correlation procedure....
Chapter 12
Figure 12.1 Exemplar EEG signals recorded during drowsiness.
Figure 12.2 During Stage III sleep, 26 s of brain waves were recorded.
Figure 12.3 Twenty‐six seconds of brain waves recorded during the REM state....
Figure 12.4 A typical concentration of melatonin in a healthy adult man (ext...
Figure 12.5 Typical waveforms for (a) spindles and (b) K‐complexes, adopted ...
Figure 12.6 Time–frequency energy map of 20 seconds epochs of sleep EEG in d...
Figure 12.7 Block diagram of the sleep scoring system proposed in [34].
Figure 12.8 The scoring result of the proposed system in [34] (bottom) compa...
Figure 12.9
I
×
K
matrix
X
is converted to tensor
where
J
is the number of...
Figure 12.10 Block diagram of the single‐channel source separation system us...
Figure 12.11 A model for neuronal slow‐wave generation;
is the derivative ...
Figure 12.12 A sample PSG record of multichannel five seconds long data. The...
Chapter 13
Figure 13.1 Inter‐hemisphere coherency of beta, alpha, and theta rhythms; to...
Figure 13.2 Inter‐hemisphere phase synchronization of beta, alpha, and theta...
Figure 13.3 Tracking variability of P3a and P3b before and during fatigue; t...
Figure 13.4 Comparison of three methods (spatial PCA, exact match and mismat...
Figure 13.5 (a) Single‐trial ERPs (40 trials related to the infrequent tones...
Figure 13.6 Selection of reference signals for P3a and P3b. In each row, the...
Figure 13.7 Scalp projections of P3a (top row) and P3b (bottom row) in four ...
Figure 13.8 Forty single‐trial ERPs and their average from the Cz channel be...
Figure 13.9 Forty single‐trial ERPs and their average from the Cz channel du...
Figure 13.10 The ERP achieved by averaging 40 EEG trials before and during t...
Figure 13.11 The estimated scalp projections of P3a (top row) and P3b (botto...
Figure 13.12 The estimated scalp projections of P3a (top row) and P3b (botto...
Figure 13.13 Theta phase synchronization of F3–F4: (a) before stimulus and (...
Figure 13.14 Alpha phase synchronization of C3–C4: (a) before stimulus and (...
Figure 13.15 Beta phase synchronization of F3–F4: (a) before stimulus and (b...
Figure 13.16 Two‐stage PCANet block diagram proposed in [55].
Chapter 14
Figure 14.1 The limbic system and the location of the amygdala.
Figure 14.2 The generators of respiration‐related anxiety potentials are in ...
Figure 14.3 Valence–arousal space showing high and low positive and negative...
Figure 14.4 Emotion neural circuitry regions involved in emotion regulation....
Figure 14.5 Direct and indirect pathways to the amygdala.
Figure 14.6 Time course of an EPN and its corresponding topography images [9...
Figure 14.7 Time course of the late positive potential; grand‐averaged ERP w...
Figure 14.8 Olfactory bulb.
Figure 14.9 Group comparison between anterior and posterior P300 amplitudes ...
Figure 14.10 Negative and positive magnitudes of activity for the main regio...
Chapter 15
Figure 15.1 Distribution of the MEG sensors into left central (LC), anterior...
Figure 15.2 Block diagram of the spectral coherency,
c
(
f
), measure.
Figure 15.3 (a) A 10 second segment of one EMG channel and (b) its correspon...
Figure 15.4 Reference selection using the
k
‐means algorithm.
Figure 15.5 Spectral coherency levels without (left) and with (right) region...
Figure 15.6 ERPs recorded using the Cz electrode for (a) healthy and (b) AD ...
Figure 15.7 Mean GC magnitudes across subjects for all links in control, AD–...
Figure 15.8 Results of the multitask diffusion adaptation method in [5] for ...
Figure 15.9 Ten brain regions used to estimate the functional connectivity u...
Figure 15.10 A sample of 10‐channel EEG of a CJD patient showing clear perio...
Figure 15.11 EEG in the very early stages of CJD, showing right‐lateralised ...
Chapter 16
Figure 16.1 Stimulus‐locked and response‐locked ERPs. Stimulus‐locked grand ...
Figure 16.2 Typical faces and labels for congruent and incongruent stimuli u...
Figure 16.3 Block diagram of the system developed in [55] for classification...
Figure 16.4 Typical ERPs recorded (and averaged over trials) from the Fz ele...
Figure 16.5 The mean asymmetry between the left and right brain hemisphere c...
Figure 16.6 The 13 EEG electrode groups used in [74].
Chapter 17
Figure 17.1 A cross‐section of the motor cortex and the links to different o...
Figure 17.2 Readiness potential elicited around the finger movement time ins...
Figure 17.3 The averaged RPs from
C
3 and
C
4 during left and right finger mov...
Figure 17.4 ERD/ERS patterns over the central region (
C
3 and
C
4) during imag...
Figure 17.5 A typical BCI system using scalp EEGs when visual feedback is us...
Figure 17.6 A hybrid BSS‐SVM system for EEG artefact removal [98].
Figure 17.7 Classification of left/right finger movements using space–time–f...
Figure 17.8 Left finger imagination: two components. The upper figure repres...
Figure 17.9 Right finger imagination: two components. The upper figures repr...
Figure 17.10 The EEG signals before the removal of eye‐blinking artefacts in...
Figure 17.11 Illustration of source propagation from the coherency spectrum ...
Figure 17.12 Illustration of source propagation from the coherency spectrum ...
Figure 17.13 A block diagram of the system proposed in [128] for classificat...
Figure 17.14 The results of applying CSP to classify the cortical activity o...
Figure 17.15 The electrodes highlighted in dark grey are those which are ove...
Figure 17.16 A typical stimulus grid for the speller BCI.
Figure 17.17 (a) The user operates the real‐time feedback loop to freely typ...
Figure 17.18 The locations of the most popular neurotechnology products [157...
Chapter 18
Figure 18.1 Block diagram of an MRI scanner illustrating the components invo...
Figure 18.2 Gamma functions with varying parameters: (a)
c
changes from 0.2 ...
Figure 18.3 The absorption coefficient curves for oxyhaemoglobin and deoxyha...
Figure 18.4 The concept of cortex fNIRS imaging.
Figure 18.5 The mechanism of inherent link between EEG and fMRI. (Top) An in...
Figure 18.6 fMRI time series for a voxel sample.
Figure 18.7 A schematic presentation of a GLM model for a sample fMRI time s...
Figure 18.8 Gradient artefact for a sample segments of EEG signal recorded s...
Figure 18.9 A set of 13‐channel EEG data covering the entire head, after gra...
Figure 18.10 (a) Comparison between ICA and ICA‐DHT for BCG removal from EEG...
Figure 18.11 Results of artefact removal from CZ channel using ICA (second f...
Figure 18.12 A cycle of a BCG artefact in a sample segment of EEG signal.
Figure 18.13 Topographic maps illustrating mu rhythm after the artefact is r...
Figure 18.14 Simulated fMRI including the sources and corresponding time cou...
Figure 18.15 Computed SIR of source of interest for different methods.
Figure 18.16 Auditory data analysis results obtained from KL I‐divergence me...
Figure 18.17 Visual data analysis results obtained from KL I‐divergence meth...
Figure 18.18 Detected BOLD (top) with its corresponding time course (bottom)...
Figure 18.19 Schematic of different steps of model‐based EEG–fMRI analysis....
Figure 18.20 EEG electrode and fNIRS optode positions for imaginary movement...
Figure 18.21 Example of a typical ‘BOLD’ response recorded by fNIRS in a tas...
Figure 18.22 (a) Experimental setup and task procedure. The participant perf...
Guide
Cover Page
Title Page
Copyright Page
Preface to the Second Edition
Preface to the First Edition
List of Abbreviations
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
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EEG Signal Processing and Machine Learning
Second Edition
Saeid Sanei
Imperial College London & Nottingham Trent University, UK
Jonathon A. Chambers
University of Leicester, UK
This second edition first published 2022© 2022 John Wiley & Sons Ltd
Edition HistoryJohn Wiley & Sons, Ltd. (1e, 2007)
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Library of Congress Cataloging‐in‐Publication Data
Names: Sanei, Saeid, author. | Chambers, Jonathon A., author. | John Wiley & Sons, publisherTitle: EEG signal processing and machine learning / Saeid Sanei, Jonathon A. Chambers.Description: Second edition. | Hoboken, NJ : Wiley, 2021. | Includes bibliographical references and index.Identifiers: LCCN 2021003276 (print) | LCCN 2021003277 (ebook) | ISBN 9781119386940 (hardback) | ISBN 9781119386926 (adobe pdf) | ISBN 9781119386933 (epub)Subjects: LCSH: Electroencephalography. | Signal processing. | Machine learning.Classification: LCC RC386.6.E43 S252 2021 (print) | LCC RC386.6.E43 (ebook) | DDC 616.8/047547–dc23
LC record available at https://lccn.loc.gov/2021003276LC ebook record available at https://lccn.loc.gov/2021003277
Cover Design: WileyCover Image: © Andrea Danti/Shutterstock, imaginima/iStock/Getty Images, xijian/iStock/Getty Images, Marmaduke St. John/Alamy Stock Photo
Preface to the Second Edition
Brain research has reached a considerable level of maturity due, for example, to having access to: a wealth of recording and screening resources; availability of substantial data banks; advanced data processing algorithms; and emerging artificial intelligence (AI) for making more accurate clinical diagnosis. Neurotechnology is also now being exploited to design revolutionary interfaces to guide artificial prostheses for human rehabilitation. Moreover, the technology for brain repair, communications between live and AI‐based body parts, mind reading, and intelligent recordings together with the use of virtual and augmented reality domains is advancing remarkably. The advances in brain research will soon make the Internet‐of‐brains feasible and enable fully monitoring the body for personal medicine purposes.
To progress this fast‐growing technology, the demand for electroencephalography (EEG) data, as a widely accessible, informative, flexible, and expandable brain screening modality, together with suitable approaches in EEG processing, is rising dramatically.
Automatic clinical diagnosis requires signal processing and machine learning algorithms to bring more insight into interpretation of the data, devising a treatment plan, and defining the path for achieving personalized medicine which is the goal of future healthcare systems. EEG is of particular interest to researchers due to its very rich information content and its relation to the entire body function.
EEG signals represent three fundamental activities in the brain: firstly, they show the normal brain rhythms which exist in the EEGs of healthy subjects and indicate the human states such as awake and sleep; secondly, they demonstrate the brain responses to audio, visual, and somatosensory excitations, whose variations can represent the brain performance in the cases of mental fatigue, learning, and memory load; and thirdly, the communications between various brain zones which can change due to ageing, dementia, and many other factors. The study of these three aspects of EEG is the focus of this book.
Most of the concepts in single channel or multichannel EEG signal processing have their origin in distinct application areas such as communication, seismic, speech and music signal processing. EEG signals are generally slow‐varying waveforms and therefore, similar to many other physiological signals, can be processed online without much computational effort.
This second edition of the book EEG Signal Processing, first published in 2007, highlights the major impact machine learning is now having on EEG analysis. This has been made possible by the recent developments in data analysis: firstly, due to the availability of supercomputers, powerful graphic cards, large volume computer clusters, and memory space within the public cloud, and secondly, due to introducing powerful classification algorithms such as deep neural networks (DNNs) which are suitable for numerous applications in brain–computer interfacing, mental task evaluation, brain disorder/disease recognition, and many others.
This edition is inclusive and comprehensive, encompassing almost all methodologies in EEG processing and learning together with their diverse applications. It is not only the result of the endeavours of our research teams, but also an encyclopaedia of the most recent works in EEG signal processing, machine learning, and their applications. Hence, this edition covers a wider, deeper, and richer content thereby alleviating the shortcomings in the first edition of this book. As such, this edition can be used as a reference by researchers in bioengineering, neuroscience, psychiatry, neuroimaging, and brain–computer interfacing. It can also be used for teaching bioengineering and neuroengineering at different university levels.
In this second edition, the number of chapters has increased from 7 to 18 by covering and extending the content in each chapter and adding many new topics for analysis of EEG signals including: (i) offering deeper understanding and insight into the generation of EEG signals and modelling the brain EEG generators in Chapters 2 and 3, (ii) being more inclusive in the domains of theoretical and practical aspects in EEG single‐ and multichannel signal processing including static and dynamic systems within multimodal and multiway mathematical models in Chapters 3–6, and (iii) providing a comprehensive and detailed approach to AI, particularly machine learning approaches, starting from traditional crisp classification to advanced deep feature learning approaches in Chapter 7.
Chapter 8 addresses brain coherency, synchrony, and connectivity. This chapter introduces a completely new topic of cooperative learning and adaptive filtering into the domain of brain connectivity and its applications. Chapter 9 introduces the brain response to audio, visual, and tactile events when they are regularly presented or targeted in an odd ball paradigm. The important topic of brain source localization, using both forward and inverse problems, is addressed in Chapter 10. A vast range of applications of these three chapters is given in the ensuing chapters.
From Chapter 11 onwards, more practical and clinically demanding approaches are discussed with the help and direct application of the theoretical developments in the previous chapters. Seizure and epileptic waveforms are studied comprehensively in Chapter 11. This chapter includes a very innovative approach using DNNs to model the pathways between the generators of epileptiform discharges to the scalp electrode recordings.
The fundamental objectives of new and advanced materials included in Chapters 12–14 are to assess the cortical brain waves, the coherency and connectivity within various brain zones, and the brain responses to different stimuli while the subject is in different states of awake, sleep, mentally tired, and under different emotions. Chapters 15 and 16 introduce the state‐of the‐art techniques in signal processing and machine learning for recognition of degenerative diseases and neurodevelopmental disorders respectively.
Brain–computer interfacing benefiting from a wealth of new neurotechnology together with applications of advanced AI systems for rehabilitation, computer gaming, and eventually brain communications purposes is comprehensively covered in Chapter 17 which concludes application of EEG signals and systems. Finally, Chapter 18 shows how EEG can be combined with other simultaneously recorded functional neuroimaging data. It introduces a number of applications where EEG can be combined with functional magnetic resonance images (fMRI) and functional near‐infrared spectroscopy (fNIRS) images, to exploit their high spatial resolutions for enhancing the overall diagnostic performance.
In the treatment of various topics covered within this research monograph it is assumed that the reader has a background in the fundamentals of digital signal processing and machine learning and wishes to focus on EEG analysis. It is hoped that the concepts covered in each chapter provide a solid foundation for future research and development in the field.
As we concluded in the first edition, we do wish to stress that in this book there is no attempt to challenge previous clinical or diagnostic knowledge. Instead, the tools and algorithms described in this book can, we believe, potentially enhance the significant information within EEG signals and thereby aid physicians and ultimately provide more cost effective and efficient diagnostic tools.
Both authors wish to thank most sincerely our Research Associates and PhD students who have contributed so much to the materials in this work.
Saeid Sanei and Jonathon A. Chambers
Preface to the First Edition
There is ever‐increasing global demand for more affordable and effective clinical and healthcare services. New techniques and equipment must therefore be developed to aid in the diagnosis, monitoring, and treatment of abnormalities and diseases of the human body. Biomedical signals (biosignals) in their manifold forms are rich information sources, which when appropriately processed have the potential to facilitate such advancements. In today's technology, such processing is very likely to be digital, as confirmed by the inclusion of digital signal processing concepts as core training in biomedical engineering degrees. Recent advancements in digital signal processing are expected to underpin key aspects of the future progress in biomedical research and technology, and it is the purpose of this research monograph to highlight this trend for the processing of measurements of brain activity, primarily electroencephalograms (EEGs).
Most of the concepts in multichannel EEG digital signal processing have their origin in distinct application areas such as communications engineering, seismics, speech and music signal processing, together with the processing of other physiological signals, such as electrocardiograms (ECGs) The particular topics in digital signal processing first explained in this research monograph include: definitions; illustrations; time domain, frequency domain, and time–frequency domain processing; signal conditioning; signal transforms; linear and nonlinear filtering; chaos definition, evaluation, and measurement; certain classification algorithms; adaptive systems; independent component analysis; and multivariate autoregressive modelling. In addition, motivated by research in the field over the last two decades, techniques specifically related to EEG processing such as brain source localization, detection and classification of event‐related potentials, sleep signal analysis, seizure detection and prediction, together with brain–computer interfacing are comprehensively explained and, with the help of suitable graphs and (topographic) images, simulation results are provided to assess the efficacy of the methods.
Chapter 1 of this research monograph is a comprehensive biography of the history and generation of EEG signals, together with a discussion of their significance and diagnostic capability. Chapter 2 provides an in‐depth introduction to the mathematical algorithms and tools commonly used in the processing of EEG signals. Most of these algorithms have only been recently developed by experts in the signal processing community and then applied to the analysis of EEG signals for various purposes. In Chapter 3, event‐related potentials are explained and the schemes for their detection and classification are explored. Many neurological and psychiatric brain disorders are diagnosed and monitored using these techniques. Chapter 4 complements the previous chapter by specifically looking at the behaviour of EEG signals in patients suffering from epilepsy. Some very recent methods in seizure prediction are demonstrated. This chapter concludes by opening up a new methodology in joint, or bimodal, EEG–fMRI analysis of epileptic seizure signals. Localization of brain source signals is next covered in Chapter 5. Traditional dipole methods are described and some very recent processing techniques such as blind source separation are briefly reviewed. In Chapter 6, the concepts developed for the analysis and description of EEG sleep recordings are summarized and the important parameters and terminologies are explained. Finally, in Chapter 7, one of the most important applications of the developed mathematical tools for processing of EEG signals, namely brain–computer interfacing, is explored and recent advancements are briefly explained. Results of the application of these algorithms are described.
In the treatment of various topics covered within this research monograph it is assumed that the reader has a background in the fundamentals of digital signal processing and wishes to focus on processing of EEGs. It is hoped that the concepts covered in each chapter provide a foundation for future research and development in the field.
In conclusion, we do wish to stress that in this book there is no attempt to challenge previous clinical or diagnostic knowledge. Instead, the tools and algorithms described in this book can, we believe, potentially enhance the significant clinically related information within EEG signals and thereby aid physicians and ultimately provide more cost effective and efficient diagnostic tools.
Both authors wish to thank most sincerely our previous and current PhD students who have contributed so much to the material in this work and our understanding of the field. Special thanks to Min Jing, Tracey Lee, Kianoush Nazarpour, Leor Shoker, Loukianous Spyrou, and Wenwu Wang, who contributed to providing some of the illustrations. Finally, this book became truly possible due to spiritual support and encouragement of Maryam Zahabsaniei, Erfan Sanei, and Ideen Sanei.
Saeid Sanei
Jonathon A. Chambers
January 2007
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 analysis
GSR
Galvanic skin response
GWN
Gaussian white noise
HBO/HbO
Oxyhaemoglobin
HBR/HbR
De‐oxyhaemoglobin
HBT
Total haemoglobin
HCI
Human computer interaction
HD
Huntington’s disease
HEOG
Horizontal electro‐oculograph
HFD
Higuchi's fractal dimension
HHT
Hilbert–Huang transform
HMD
Head‐mounted display
HMM
Hidden Markov model
HOPLS
Higher‐order partial least squares
HOS
Higher‐order statistics
HR
Hemodynamic response
HRF
Haemodynamic response function
HT
Hilbert transform
IAPS
International affective picture system
IBE
International Bureau for Epilepsy
IC
Independent component
ICA
Independent component analysis
iCOH
Imaginary part of coherency
IED
Interictal epileptiform discharge
iEEG
Intracranial electroencephalogram
ICA
Independent component analysis
IIR
Infinite impulse response
ILAE
International League Against Epilepsy
IMF
Intrinsic mode function
ImSCoh
Imaginary part of S‐coherency
INDSCAL
INDividual Differences SCALing
IoB
Internet‐of‐brains
IPL
Inferior parietal lobule
IPSP
Inhibitory post‐synaptic potential
IR
Impulse response
IRLS
Iterative recursive least squares
ISODATA
Iterative self‐organizing data analysis technique algorithm
Isomap
Isometric mapping
ISSWT
Inverse synchro‐squeezing wavelet transform
ITR
Information transfer rate
IVE
Immersive virtual environments
JAD
Joint approximate diagonalization
JADE
Joint approximate diagonalization of eigenmatrices
jICA
Joint ICA
K
Potassium
Kc
Kolmogorov complexity
KDT
Karolinska drowsiness test
KL
Kullback–Leibler
KLT
Karhunen–Loéve transform
KMI
Kinaesthetic motor imagery
KNN
k‐nearest neighbour
KPCA
Kernel principal component analysis
KSS
Karolinska sleepiness scale
KT
Kuhn–Tucker
LBD
Lewy body dementia
LCMV
Linearly constrained minimum variance
LD
Linear discriminants
LD
Linearly distributed
LDA
Linear discriminant analysis
LDA
Long delta activity
LE
Lyapunov exponent
LEM
Local EEG model
LLE
Largest Lyapunov exponent
LMS
Least mean square
LORETA
Low‐resolution electromagnetic tomography algorithm
LP
Lowpass
LPM
Letters per minute
LPP
Late positive potential
LRCN
Long‐term recurrent convolutional network
LRT
Low‐resolution tomography
LS
Least squares
LSE
Least‐squares error
LSTM
Long short‐term memory network
LVQ
Learning vector quantization
LWR
Levinson–Wiggins–Robinson
LZC
Lempel–Ziv complexity
M2M
Machine‐to‐machine
MA
Mental arithmetic
MA
Moving average
MAF
Multivariate ambiguity function
MAP
Maximum a posteriori
MCI
Mild cognitive impairment
MCMC
Markov chain Monte Carlo
MDI
Multidimensional directed information
MDP
Moving dipole
MEG
Magnetoencephalogram
MFDE
Multiscale fluctuation‐based dispersion entropy
mHTT
Mutant huntingtin
MI
Mutual information
MIL
Matrix inversion lemma
ML
Maximum likelihood
MLE
Maximum likelihood estimation
MLE
Maximum Lyapunov exponent
MLP
Multilayer perceptron
MMN
Mismatch negativity
MMSE
Minimum mean squared error
MNI
Montreal Neurological Institute and Hospital
MNLS
Minimum norm least squares
MP
Matching pursuits
MRI
Magnetic resonance imaging
MRP
Movement‐related potential
MSE
Multiple system atrophy
MSE
Mean squared error
MSE
Multiscale entropy
MS
Multiple sclerosis
MTLE
Mesial temporal lobe epilepsy
MUSIC
Multichannel signal classification
MVAR
Multivariate autoregressive
Na
Sodium
NC
Normal control
NCDF
Normal cumulative distribution function
NCSP
Nonparametric common spatial patterns
NDD
Neurodevelopmental disorder
NES
Nonepileptic seizure
NIH
National Institute of Health
NIR
Near infrared
NIRS
Near‐infrared spectroscopy
NLMS
Normalized least mean square
NMF
Nonnegative matrix factorization
NMCSP
Nonparametric multiclass common spatial patterns
NMR
Nuclear magnetic resonance
NN
Neural network
NNQP
Nonnegative quadratic program
NP
Neural process
NREM
Non‐rapid eye movement
NSI
Nonstationary index
OA
Ocular artefact
OBS
Optimal basis set
OBS
Organic brain syndrome
OFC
Orbital frontal cortex
OMP
Orthogonal matching pursuit
OP
Oddball paradigm
OSA
Obstructive sleep apnoea
OSAHS
Obstructive sleep apnoea hypopnea syndrome
PARAFAC
Parallel factor analysis
PCA
Principal component analysis
PCANet
Principal component analysis network
PCC
Pearson product correlation coefficient
PCC
Posterior cingulate cortices
PD
Parkinson’s disease
PDC
Partial directed coherence
Probability density function
Pe
Error positivity
PerEn
Permutation entropy
PET
Positron emission tomography
PF
Particle filter
PFC
Prefrontal cortex
PIC
Power iteration clustering
PIF
Phase interaction function
PLED
Periodic literalized epileptiform discharges
PLI
Phase lag index
PLMD
Periodic limb movement disorder
PLS
Partial least squares
PMBR
Post‐movement beta rebound
PMBS
Post‐movement beta synchronization
PNRD
Nonrhythmic delta activity
POST
Positive occipital sharp transients
PPC
Phase–phase coupling
PPG
Photoplethysmography
PPM
Piecewise Prony method
PSD
Power spectrum density
PSDM
Phase‐space dissimilarity measures
PSG
Polysomnography
PSI
Phase‐slope index
PSP
Post‐synaptic potential
PSP
Progressive supranuclear palsy
PSWC
Periodic sharp wave complexes
PTSD
Post‐traumatic stress disorder
PWVD
Pseudo‐Wigner–Ville distribution
QEEG
Quantitative EEG
QGARCH
Quadratic GARCH
QNN
Quantum neural networks
QP
Quadratic programming
R&K
Rechtschtschaffen and Kales
RAP
Recursively applied and projected
RASM
Rational asymmetry
RBD
REM sleep behaviour disorder
RBF
Radial basis function
RBPF
Rao‐Blackwellised particle filter
RBR
Relative beta ratio
RCE
Recursive channel elimination
RE
Regional entropy
ReLU
Rectified linear unit
REM
Rapid eye movement
RF
Radio frequency
RFNN
Recurrent fuzzy neural network
RKHS
Reproducing kernel Hilbert spaces
RLS
Recursive least squares
RMBF
Robust minimum variance beamformer
RMS
Root mean square
RNN
Recurrent neural network
ROC
Receiver operating characteristic
RP
Readiness potential
RR
Respiratory rate
RT
Reaction time
rTMS
repetitive transcranial magnetic stimulation
RV
Residual variance
SAE
Stacked autoencoder
SampEn
Sample entropy
SAS
Sleep apnoea syndrome
SCA
Sparse component analysis
SCD
Sickle cell disease
SCP
Slow cortical potential
SCPS
Slow cortical potential shift
SCR
Skin conductance response
SCV
Spectral coherence value
SCWT
Stroop colour and word test
SDAE
Stacked denoising autoencoder
SDTF
Short‐time DTF
SEM
Structural equation modelling
SFS
SyncFastSlow
SG
Sensory gating
SI
Synchronization index
SICA
Spatial ICA
SL
Synchronization likelihood
sLORETA
Standardized LORETA
SLTP
Short‐ and long‐term prediction
SMI
Sample‐matrix inversion
SMl
Sensorimotor left
SMOTE
Synthetic minority oversampling technique
sMRI
Structural MRI
SN
Salient network
SNN
Spike neural network
SNNAP
Simulator for Neural Networks and Action Potentials
SNR
Signal‐to‐noise ratio
SOBI
Second‐order blind identification
SOBIUM
Second‐order blind identification of underdetermined mixtures
SPET
Single photon emission tomography
SPM
Statistical parametric mapping
SPQ
