EEG Signal Processing and Machine Learning (eBook)
Wiley (Verlag)
978-1-119-38693-3 (ISBN)
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:
Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, and 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 Biomedical Engineering and Neuroscience, including Epileptology, students.
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.
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.
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.
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...
| Erscheint lt. Verlag | 23.9.2021 |
|---|---|
| Sprache | englisch |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Schlagworte | automatic sleep scoring • BCI • biosignal AI • biosignal artificial intelligence • biosignal machine learning • Biosignal processing • brain computer interfacing • EEG AI • EEG artificial intelligence • EEG-fMRI • EEG machine learning • EEG machine learning applications • EEG machine learning techniques • EEG-NIRS • EEG pattern recognition • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • magnetoencephalography • Maschinelles Lernen • MEG • mental fatigue analysis • Neurodevelopmental diseases • Numerical Methods & Algorithms • Numerische Methoden u. Algorithmen • psychiatric diseases • Signal Processing • Signalverarbeitung • sleep abnormalities |
| ISBN-10 | 1-119-38693-4 / 1119386934 |
| ISBN-13 | 978-1-119-38693-3 / 9781119386933 |
| Haben Sie eine Frage zum Produkt? |
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