Clinical neurophysiology [[electronic resource] /] / edited by Jasper R. Daube |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, 2002 |
Descrizione fisica | 1 online resource (676 p.) |
Disciplina |
612.8
616.8047547 |
Altri autori (Persone) | DaubeJasper R |
Collana | Contemporary neurology series |
Soggetto topico |
Electroencephalography
Electromyography Evoked potentials (Electrophysiology) Nervous system - Diseases - Diagnosis Neurophysiology |
ISBN |
1-280-83494-3
9786610834945 0-19-803228-5 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
CONTENTS; CONTRIBUTORS; SECTION 1. ANALYSIS OF ELECTROPHYSIOLOGIC WAVEFORMS; 1. ELECTRICITY AND ELECTRONICS FOR CLINICAL NEUROPHYSIOLOGY; BASIC PRINCIPLES AND DEFINITIONS IN ELECTRICITY; CIRCUIT ANALYSIS; RESISTIVE-CAPACITIVE AND RESISTIVE-INDUCTIVE CIRCUITS; CIRCUITS CONTAINING INDUCTORS AND CAPACITORS; FILTER CIRCUITS; TRANSISTORS AND AMPLIFIERS; 2. ELECTRIC SAFETY IN THE LABORATORY AND HOSPITAL; ELECTRIC POWER DISTRIBUTION SYSTEMS; ELECTRIC SHOCK; LEAKAGE CURRENT; ELECTRIC SAFETY PRINCIPLES AND IMPLEMENTATION; 3. VOLUME CONDUCTION; PRINCIPLES; ELECTRIC PROPERTIES OF VOLUME CONDUCTORS
CALCULATING POTENTIALS IN INFINITE HOMOGENEOUS MEDIAPOTENTIALS IN NONHOMOGENEOUS MEDIA; APPLICATIONS OF VOLUME CONDUCTION PRINCIPLES; 4. DIGITAL SIGNAL PROCESSING; DIGITAL COMPUTERS IN CLINICAL NEUROPHYSIOLOGY; DIGITIZATION; COMMON USES OF DIGITAL PROCESSING; AVERAGING; DIGITAL FILTERING; TIME AND FREQUENCY DOMAIN ANALYSIS; 5. ELECTROPHYSIOLOGIC GENERATORS IN CLINICAL NEUROPHYSIOLOGY; PHYSIOLOGIC GENERATORS; STRUCTURAL GENERATORS; 6. CLASSIFICATION OF WAVEFORM CHARACTERISTICS; CONTINUOUS WAVEFORMS; EVENT RECORDING; 7. ALTERATION OF WAVEFORMS AND ARTIFACTS; PHYSIOLOGIC ALTERATION OF WAVEFORMS ARTIFACTUAL WAVEFORMSSECTION 2. ELECTROPHYSIOLOGIC ASSESSMENT OF NEURAL FUNCTION; Part A. Cortical Function; 8. ELECTROENCEPHALOGRAPHY: GENERAL PRINCIPLES AND ADULT ELECTROENCEPHALOGRAMS; 9. ELECTROENCEPHALOGRAPHY: ELECTROENCEPHALOGRAMS OF NEONATES, INFANTS, AND CHILDREN; 10. AMBULATORY ELECTROENCEPHALOGRAPHY; 11. PROLONGED VIDEO ELECTROENCEPHALOGRAPHY; 12. ELECTROENCEPHALOGRAPHIC SPECIAL STUDIES; 13. ELECTROENCEPHALOGRAPHIC RECORDINGS FOR EPILEPSY SURGERY; 14. MOVEMENT-RELATED POTENTIALS AND EVENT-RELATED POTENTIALS; Part B. Sensory Pathways; 15. NERVE ACTION POTENTIALS 16. SOMATOSENSORY EVOKED POTENTIALS17. BRAIN STEM AUDITORY EVOKED POTENTIALS IN CENTRAL DISORDERS; 18. AUDIOGRAM, ACOUSTIC REFLEXES, AND EVOKED OTOACOUSTIC EMISSIONS; 19. BRAIN STEM AUDITORY EVOKED POTENTIALS IN PERIPHERAL ACOUSTIC DISORDERS; 20. VISUAL EVOKED POTENTIALS; Part C. Motor Pathways; 21. COMPOUND MUSCLE ACTION POTENTIALS; 22. ASSESSING THE NEUROMUSCULAR JUNCTION WITH REPETITIVE STIMULATION STUDIES; 23. MOTOR EVOKED POTENTIALS; Part D. Assessing the Motor Unit; 24. ASSESSING THE MOTOR UNIT WITH NEEDLE ELECTROMYOGRAPHY; 25. QUANTITATIVE ELECTROMYOGRAPHY 26. SINGLE FIBER ELECTROMYOGRAPHY27. ESTIMATING THE NUMBER OF MOTOR UNITS IN A MUSCLE; Part E. Reflexes and Central Motor Control; 28. H REFLEXES; 29. CRANIAL REFLEXES; 30. LONG LATENCY REFLEXES AND THE SILENT PERIOD; 31. SURFACE ELECTROMYOGRAPHIC STUDIES OF MOVEMENT DISORDERS; 32. VERTIGO AND BALANCE; Part F. Autonomic Function; 33. CLINICAL PHYSIOLOGY OF THE AUTONOMIC NERVOUS SYSTEM; 34. QUANTITATIVE SUDOMOTOR AXON REFLEX TEST AND RELATED TESTS; 35. ADRENERGIC FUNCTION; 36. THERMOREGULATORY SWEAT TEST; 37. CARDIOVAGAL AND OTHER REFLEXES; 38. ELECTROPHYSIOLOGY OF PAIN Part G. Sleep and Consciousness |
Record Nr. | UNINA-9910782320203321 |
Oxford ; ; New York, : Oxford University Press, 2002 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Clinical neurophysiology / / edited by Jasper R. Daube |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, 2002 |
Descrizione fisica | 1 online resource (676 p.) |
Disciplina |
612.8
616.8047547 |
Altri autori (Persone) | DaubeJasper R |
Collana | Contemporary neurology series |
Soggetto topico |
Electroencephalography
Electromyography Evoked potentials (Electrophysiology) Nervous system - Diseases - Diagnosis Neurophysiology |
ISBN |
1-280-83494-3
9786610834945 0-19-803228-5 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
CONTENTS; CONTRIBUTORS; SECTION 1. ANALYSIS OF ELECTROPHYSIOLOGIC WAVEFORMS; 1. ELECTRICITY AND ELECTRONICS FOR CLINICAL NEUROPHYSIOLOGY; BASIC PRINCIPLES AND DEFINITIONS IN ELECTRICITY; CIRCUIT ANALYSIS; RESISTIVE-CAPACITIVE AND RESISTIVE-INDUCTIVE CIRCUITS; CIRCUITS CONTAINING INDUCTORS AND CAPACITORS; FILTER CIRCUITS; TRANSISTORS AND AMPLIFIERS; 2. ELECTRIC SAFETY IN THE LABORATORY AND HOSPITAL; ELECTRIC POWER DISTRIBUTION SYSTEMS; ELECTRIC SHOCK; LEAKAGE CURRENT; ELECTRIC SAFETY PRINCIPLES AND IMPLEMENTATION; 3. VOLUME CONDUCTION; PRINCIPLES; ELECTRIC PROPERTIES OF VOLUME CONDUCTORS
CALCULATING POTENTIALS IN INFINITE HOMOGENEOUS MEDIAPOTENTIALS IN NONHOMOGENEOUS MEDIA; APPLICATIONS OF VOLUME CONDUCTION PRINCIPLES; 4. DIGITAL SIGNAL PROCESSING; DIGITAL COMPUTERS IN CLINICAL NEUROPHYSIOLOGY; DIGITIZATION; COMMON USES OF DIGITAL PROCESSING; AVERAGING; DIGITAL FILTERING; TIME AND FREQUENCY DOMAIN ANALYSIS; 5. ELECTROPHYSIOLOGIC GENERATORS IN CLINICAL NEUROPHYSIOLOGY; PHYSIOLOGIC GENERATORS; STRUCTURAL GENERATORS; 6. CLASSIFICATION OF WAVEFORM CHARACTERISTICS; CONTINUOUS WAVEFORMS; EVENT RECORDING; 7. ALTERATION OF WAVEFORMS AND ARTIFACTS; PHYSIOLOGIC ALTERATION OF WAVEFORMS ARTIFACTUAL WAVEFORMSSECTION 2. ELECTROPHYSIOLOGIC ASSESSMENT OF NEURAL FUNCTION; Part A. Cortical Function; 8. ELECTROENCEPHALOGRAPHY: GENERAL PRINCIPLES AND ADULT ELECTROENCEPHALOGRAMS; 9. ELECTROENCEPHALOGRAPHY: ELECTROENCEPHALOGRAMS OF NEONATES, INFANTS, AND CHILDREN; 10. AMBULATORY ELECTROENCEPHALOGRAPHY; 11. PROLONGED VIDEO ELECTROENCEPHALOGRAPHY; 12. ELECTROENCEPHALOGRAPHIC SPECIAL STUDIES; 13. ELECTROENCEPHALOGRAPHIC RECORDINGS FOR EPILEPSY SURGERY; 14. MOVEMENT-RELATED POTENTIALS AND EVENT-RELATED POTENTIALS; Part B. Sensory Pathways; 15. NERVE ACTION POTENTIALS 16. SOMATOSENSORY EVOKED POTENTIALS17. BRAIN STEM AUDITORY EVOKED POTENTIALS IN CENTRAL DISORDERS; 18. AUDIOGRAM, ACOUSTIC REFLEXES, AND EVOKED OTOACOUSTIC EMISSIONS; 19. BRAIN STEM AUDITORY EVOKED POTENTIALS IN PERIPHERAL ACOUSTIC DISORDERS; 20. VISUAL EVOKED POTENTIALS; Part C. Motor Pathways; 21. COMPOUND MUSCLE ACTION POTENTIALS; 22. ASSESSING THE NEUROMUSCULAR JUNCTION WITH REPETITIVE STIMULATION STUDIES; 23. MOTOR EVOKED POTENTIALS; Part D. Assessing the Motor Unit; 24. ASSESSING THE MOTOR UNIT WITH NEEDLE ELECTROMYOGRAPHY; 25. QUANTITATIVE ELECTROMYOGRAPHY 26. SINGLE FIBER ELECTROMYOGRAPHY27. ESTIMATING THE NUMBER OF MOTOR UNITS IN A MUSCLE; Part E. Reflexes and Central Motor Control; 28. H REFLEXES; 29. CRANIAL REFLEXES; 30. LONG LATENCY REFLEXES AND THE SILENT PERIOD; 31. SURFACE ELECTROMYOGRAPHIC STUDIES OF MOVEMENT DISORDERS; 32. VERTIGO AND BALANCE; Part F. Autonomic Function; 33. CLINICAL PHYSIOLOGY OF THE AUTONOMIC NERVOUS SYSTEM; 34. QUANTITATIVE SUDOMOTOR AXON REFLEX TEST AND RELATED TESTS; 35. ADRENERGIC FUNCTION; 36. THERMOREGULATORY SWEAT TEST; 37. CARDIOVAGAL AND OTHER REFLEXES; 38. ELECTROPHYSIOLOGY OF PAIN Part G. Sleep and Consciousness |
Record Nr. | UNINA-9910828572303321 |
Oxford ; ; New York, : Oxford University Press, 2002 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Clinical neurophysiology |
Pubbl/distr/stampa | Shannon, : Elsevier |
Disciplina | 616.8047547 |
ISSN | 1388-2457 |
Formato | Materiale a stampa |
Livello bibliografico | Periodico |
Lingua di pubblicazione | eng |
Note periodicità | Altro |
Record Nr. | UNINA-990008941620403321 |
Shannon, : Elsevier | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Designing eeg experiments for studying the brain : design code and example datasets / / Aamir Saeed Malik, Hafeez Ullah Amin |
Autore | Malik Aamir Saeed |
Pubbl/distr/stampa | London, England : , : Academic Press, , 2017 |
Descrizione fisica | 1 online resource (280 pages) |
Disciplina | 616.8047547 |
Soggetto topico | Electroencephalography |
ISBN | 0-12-811141-0 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910583063203321 |
Malik Aamir Saeed | ||
London, England : , : Academic Press, , 2017 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
EEG Signal Processing and Feature Extraction / / edited by Li Hu, Zhiguo Zhang |
Edizione | [1st ed. 2019.] |
Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
Descrizione fisica | 1 online resource (435 pages) |
Disciplina | 616.8047547 |
Soggetto topico |
Biomedical engineering
Neurosciences Experiential research Biomedical Engineering/Biotechnology Psychology Research |
ISBN | 981-13-9113-0 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction -- EEG: origin and measurement -- Electroencephalography, Evoked potentials and event-related potentials -- ERP Experimental design -- EEG Preprocessing and denoising -- Spectral and time-frequency analysis -- Blind source separation -- Microstate analysis -- Source analysis -- Single-trial analysis -- Nonlinear neural dynamics -- Connectivity analysis -- Spatial complex brain network -- Temporal complex network analysis -- Machine learning -- Deep learning -- Statistical analysis -- Simultaneous EEG-fMRI -- EEG/ERP data analysis toolboxes -- Summary and conclusions. |
Record Nr. | UNINA-9910350349403321 |
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
EEG signal processing and machine learning / / Saeid Sanei, Jonathon A. Chambers |
Autore | Sanei Saeid |
Edizione | [Second edition.] |
Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2022] |
Descrizione fisica | 1 online resource (751 pages) |
Disciplina | 616.8047547 |
Soggetto topico | Electroencephalography |
Soggetto genere / forma | Electronic books. |
ISBN |
1-119-38693-4
1-119-38695-0 1-119-38692-6 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- List of Abbreviations -- Chapter 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 -- Chapter 2 EEG Waveforms -- 2.1 Brain Rhythms -- 2.2 EEG Recording and Measurement -- 2.2.1 Conventional Electrode Positioning -- 2.2.2 Unconventional and Special Purpose EEG Recording Systems -- 2.2.3 Invasive Recording of Brain Potentials -- 2.2.4 Conditioning the Signals -- 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 -- 2.9.1 Dementia -- 2.9.2 Epileptic Seizure and Nonepileptic Attacks -- 2.9.3 Psychiatric Disorders -- 2.9.4 External Effects -- 2.10 Summary -- References -- Chapter 3 EEG Signal Modelling -- 3.1 Introduction -- 3.2 Physiological Modelling of EEG Generation -- 3.2.1 Integrate-and-Fire Models -- 3.2.2 Phase-Coupled Models -- 3.2.3 Hodgkin-Huxley Model -- 3.2.4 Morris-Lecar Model -- 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities -- 3.4 Mathematical Models Derived Directly from the EEG Signals -- 3.4.1 Linear Models -- 3.4.1.1 Prediction Method -- 3.4.1.2 Prony's Method -- 3.4.2 Nonlinear Modelling -- 3.4.3 Gaussian Mixture Model -- 3.5 Electronic Models -- 3.5.1 Models Describing the Function of the Membrane -- 3.5.1.1 Lewis Membrane Model -- 3.5.1.2 Roy Membrane Model -- 3.5.2 Models Describing the Function of a Neuron -- 3.5.2.1 Lewis Neuron Model -- 3.5.2.2 The Harmon Neuron Model -- 3.5.3 A Model Describing the Propagation of the Action Pulse in an Axon -- 3.5.4 Integrated Circuit Realizations.
3.6 Dynamic Modelling of Neuron Action Potential Threshold -- 3.7 Summary -- References -- Chapter 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 -- 4.5.1 Wavelet Transform -- 4.5.1.1 Continuous Wavelet Transform -- 4.5.1.2 Examples of Continuous Wavelets -- 4.5.1.3 Discrete-Time Wavelet Transform -- 4.5.1.4 Multiresolution Analysis -- 4.5.1.5 Wavelet Transform Using Fourier Transform -- 4.5.1.6 Reconstruction -- 4.5.2 Synchro-Squeezed Wavelet Transform -- 4.5.3 Ambiguity Function and the Wigner-Ville Distribution -- 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 -- 4.9.1 Singular Value Decomposition -- 4.10 Summary -- References -- Chapter 5 EEG Signal Decomposition -- 5.1 Introduction -- 5.2 Singular Spectrum Analysis -- 5.2.1 Decomposition -- 5.2.2 Reconstruction -- 5.3 Multichannel EEG Decomposition -- 5.3.1 Independent Component Analysis -- 5.3.2 Instantaneous BSS -- 5.3.3 Convolutive BSS -- 5.3.3.1 General Applications -- 5.3.3.2 Application of Convolutive BSS to EEG -- 5.4 Sparse Component Analysis -- 5.4.1 Standard Algorithms for Sparse Source Recovery -- 5.4.1.1 Greedy-Based Solution -- 5.4.1.2 Relaxation-Based Solution -- 5.4.2 k-Sparse Mixtures -- 5.5 Nonlinear BSS -- 5.6 Constrained BSS -- 5.7 Application of Constrained BSS -- Example -- 5.8 Multiway EEG Decompositions -- 5.8.1 Tensor Factorization for BSS -- 5.8.2 Solving BSS of Nonstationary Sources Using Tensor Factorization -- 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 -- Chapter 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 -- 6.7.1 Optimum Time Delay -- 6.7.2 Optimum Embedding Dimension -- 6.8 Approximate Entropy -- 6.9 Using Prediction Order -- 6.10 Summary -- References -- Chapter 7 Machine Learning for EEG Analysis -- 7.1 Introduction -- 7.2 Clustering Approaches -- 7.2.1 k-Means Clustering Algorithm -- 7.2.2 Iterative Self-Organizing Data Analysis Technique -- 7.2.3 Gap Statistics -- 7.2.4 Density-Based Clustering -- 7.2.5 Affinity-Based Clustering -- 7.2.6 Deep Clustering -- 7.2.7 Semi-Supervised Clustering -- 7.2.7.1 Basic Semi-Supervised Techniques -- 7.2.7.2 Deep Semi-Supervised Techniques -- 7.2.8 Fuzzy Clustering -- 7.3 Classification Algorithms -- 7.3.1 Decision Trees -- 7.3.2 Random Forest -- 7.3.3 Linear Discriminant Analysis -- 7.3.4 Support Vector Machines -- 7.3.5 k-Nearest Neighbour -- 7.3.6 Gaussian Mixture Model -- 7.3.7 Logistic Regression -- 7.3.8 Reinforcement Learning -- 7.3.9 Artificial Neural Networks -- 7.3.9.1 Deep Neural Networks -- 7.3.9.2 Convolutional Neural Networks -- 7.3.9.3 Autoencoders -- 7.3.9.4 Variational Autoencoder -- 7.3.9.5 Recent DNN Approaches -- 7.3.9.6 Spike Neural Networks -- 7.3.9.7 Applications of DNNs to EEG -- 7.3.10 Gaussian Processes -- 7.3.11 Neural Processes -- 7.3.12 Graph Convolutional Networks -- 7.3.13 Naïve Bayes Classifier -- 7.3.14 Hidden Markov Model -- 7.3.14.1 Forward Algorithm -- 7.3.14.2 Backward Algorithm. 7.3.14.3 HMM Design -- 7.4 Common Spatial Patterns -- 7.5 Summary -- References -- Chapter 8 Brain Connectivity and Its Applications -- 8.1 Introduction -- 8.2 Connectivity through Coherency -- 8.3 Phase-Slope Index -- 8.4 Multivariate Directionality Estimation -- 8.4.1 Directed Transfer Function -- 8.4.2 Direct DTF -- 8.4.3 Partial Directed Coherence -- 8.5 Modelling the Connectivity by Structural Equation Modelling -- 8.6 Stockwell Time-Frequency Transform for Connectivity Estimation -- 8.7 Inter-Subject EEG Connectivity -- 8.7.1 Objectives -- 8.7.2 Technological Relevance -- 8.8 State-Space Model for Estimation of Cortical Interactions -- 8.9 Application of Cooperative Adaptive Filters -- 8.9.1 Use of Cooperative Kalman Filter -- 8.9.2 Task-Related Adaptive Connectivity -- 8.9.3 Diffusion Adaptation -- 8.9.4 Brain Connectivity for Cooperative Adaptation -- 8.9.5 Other Applications of Cooperative Learning and Brain Connectivity Estimation -- 8.10 Graph Representation of Brain Connectivity -- 8.11 Tensor Factorization Approach -- 8.12 Summary -- References -- Chapter 9 Event-Related Brain Responses -- 9.1 Introduction -- 9.2 ERP Generation and Types -- 9.2.1 P300 and its Subcomponents -- 9.3 Detection, Separation, and Classification of P300 Signals -- 9.3.1 Using ICA -- 9.3.2 Estimation of Single-Trial Brain Responses by Modelling the ERP Waveforms -- 9.3.3 ERP Source Tracking in Time -- 9.3.4 Time-Frequency Domain Analysis -- 9.3.5 Application of Kalman Filter -- 9.3.6 Particle Filtering and its Application to ERP Tracking -- 9.3.7 Variational Bayes Method -- 9.3.8 Prony's Approach for Detection of P300 Signals -- 9.3.9 Adaptive Time-Frequency Methods -- 9.4 Brain Activity Assessment Using ERP -- 9.5 Application of P300 to BCI -- 9.6 Summary -- References -- Chapter 10 Localization of Brain Sources -- 10.1 Introduction. 10.2 General Approaches to Source Localization -- 10.2.1 Dipole Assumption -- 10.3 Head Model -- 10.4 Most Popular Brain Source Localization Approaches -- 10.4.1 EEG Source Localization Using Independent Component Analysis -- 10.4.2 MUSIC Algorithm -- 10.4.3 LORETA Algorithm -- 10.4.4 FOCUSS Algorithm -- 10.4.5 Standardized LORETA -- 10.4.6 Other Weighted Minimum Norm Solutions -- 10.4.7 Evaluation Indices -- 10.4.8 Joint ICA-LORETA Approach -- 10.5 Forward Solutions to the Localization Problem -- 10.5.1 Partially Constrained BSS Method -- 10.5.2 Constrained Least-Squares Method for Localization of P3a and P3b -- 10.5.3 Spatial Notch Filtering Approach -- 10.6 The Methods Based on Source Tracking -- 10.6.1 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization -- 10.6.2 Hybrid Beamforming - Particle Filtering -- 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 -- Chapter 11 Epileptic Seizure Prediction, Detection, and Localization -- 11.1 Introduction -- 11.2 Seizure Detection -- 11.2.1 Adult Seizure Detection from EEGs -- 11.2.2 Detection of Neonatal Seizure -- 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 -- 11.6.1 Introduction to IED -- 11.6.2 iEED-Times IED Detection from Scalp EEG -- 11.6.3 A Multiview Approach to IED Detection -- 11.6.4 Coupled Dictionary Learning for IED Detection -- 11.6.5 A Deep Learning Approach to IED Detection -- 11.7 Fusion of EEG-fMRI Data for Seizure Prediction -- 11.8 Summary -- References -- Chapter 12 Sleep Recognition, Scoring, and Abnormalities -- 12.1 Introduction. 12.1.1 Definition of Sleep. |
Altri titoli varianti | Electroencephalogram signal processing and machine learning |
Record Nr. | UNINA-9910555251803321 |
Sanei Saeid | ||
Hoboken, New Jersey : , : Wiley, , [2022] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
EEG signal processing and machine learning / / Saeid Sanei, Jonathon A. Chambers |
Autore | Sanei Saeid |
Edizione | [Second edition.] |
Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2022] |
Descrizione fisica | 1 online resource (751 pages) |
Disciplina | 616.8047547 |
Soggetto topico | Electroencephalography |
ISBN |
1-119-38693-4
1-119-38695-0 1-119-38692-6 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- List of Abbreviations -- Chapter 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 -- Chapter 2 EEG Waveforms -- 2.1 Brain Rhythms -- 2.2 EEG Recording and Measurement -- 2.2.1 Conventional Electrode Positioning -- 2.2.2 Unconventional and Special Purpose EEG Recording Systems -- 2.2.3 Invasive Recording of Brain Potentials -- 2.2.4 Conditioning the Signals -- 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 -- 2.9.1 Dementia -- 2.9.2 Epileptic Seizure and Nonepileptic Attacks -- 2.9.3 Psychiatric Disorders -- 2.9.4 External Effects -- 2.10 Summary -- References -- Chapter 3 EEG Signal Modelling -- 3.1 Introduction -- 3.2 Physiological Modelling of EEG Generation -- 3.2.1 Integrate-and-Fire Models -- 3.2.2 Phase-Coupled Models -- 3.2.3 Hodgkin-Huxley Model -- 3.2.4 Morris-Lecar Model -- 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities -- 3.4 Mathematical Models Derived Directly from the EEG Signals -- 3.4.1 Linear Models -- 3.4.1.1 Prediction Method -- 3.4.1.2 Prony's Method -- 3.4.2 Nonlinear Modelling -- 3.4.3 Gaussian Mixture Model -- 3.5 Electronic Models -- 3.5.1 Models Describing the Function of the Membrane -- 3.5.1.1 Lewis Membrane Model -- 3.5.1.2 Roy Membrane Model -- 3.5.2 Models Describing the Function of a Neuron -- 3.5.2.1 Lewis Neuron Model -- 3.5.2.2 The Harmon Neuron Model -- 3.5.3 A Model Describing the Propagation of the Action Pulse in an Axon -- 3.5.4 Integrated Circuit Realizations.
3.6 Dynamic Modelling of Neuron Action Potential Threshold -- 3.7 Summary -- References -- Chapter 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 -- 4.5.1 Wavelet Transform -- 4.5.1.1 Continuous Wavelet Transform -- 4.5.1.2 Examples of Continuous Wavelets -- 4.5.1.3 Discrete-Time Wavelet Transform -- 4.5.1.4 Multiresolution Analysis -- 4.5.1.5 Wavelet Transform Using Fourier Transform -- 4.5.1.6 Reconstruction -- 4.5.2 Synchro-Squeezed Wavelet Transform -- 4.5.3 Ambiguity Function and the Wigner-Ville Distribution -- 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 -- 4.9.1 Singular Value Decomposition -- 4.10 Summary -- References -- Chapter 5 EEG Signal Decomposition -- 5.1 Introduction -- 5.2 Singular Spectrum Analysis -- 5.2.1 Decomposition -- 5.2.2 Reconstruction -- 5.3 Multichannel EEG Decomposition -- 5.3.1 Independent Component Analysis -- 5.3.2 Instantaneous BSS -- 5.3.3 Convolutive BSS -- 5.3.3.1 General Applications -- 5.3.3.2 Application of Convolutive BSS to EEG -- 5.4 Sparse Component Analysis -- 5.4.1 Standard Algorithms for Sparse Source Recovery -- 5.4.1.1 Greedy-Based Solution -- 5.4.1.2 Relaxation-Based Solution -- 5.4.2 k-Sparse Mixtures -- 5.5 Nonlinear BSS -- 5.6 Constrained BSS -- 5.7 Application of Constrained BSS -- Example -- 5.8 Multiway EEG Decompositions -- 5.8.1 Tensor Factorization for BSS -- 5.8.2 Solving BSS of Nonstationary Sources Using Tensor Factorization -- 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 -- Chapter 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 -- 6.7.1 Optimum Time Delay -- 6.7.2 Optimum Embedding Dimension -- 6.8 Approximate Entropy -- 6.9 Using Prediction Order -- 6.10 Summary -- References -- Chapter 7 Machine Learning for EEG Analysis -- 7.1 Introduction -- 7.2 Clustering Approaches -- 7.2.1 k-Means Clustering Algorithm -- 7.2.2 Iterative Self-Organizing Data Analysis Technique -- 7.2.3 Gap Statistics -- 7.2.4 Density-Based Clustering -- 7.2.5 Affinity-Based Clustering -- 7.2.6 Deep Clustering -- 7.2.7 Semi-Supervised Clustering -- 7.2.7.1 Basic Semi-Supervised Techniques -- 7.2.7.2 Deep Semi-Supervised Techniques -- 7.2.8 Fuzzy Clustering -- 7.3 Classification Algorithms -- 7.3.1 Decision Trees -- 7.3.2 Random Forest -- 7.3.3 Linear Discriminant Analysis -- 7.3.4 Support Vector Machines -- 7.3.5 k-Nearest Neighbour -- 7.3.6 Gaussian Mixture Model -- 7.3.7 Logistic Regression -- 7.3.8 Reinforcement Learning -- 7.3.9 Artificial Neural Networks -- 7.3.9.1 Deep Neural Networks -- 7.3.9.2 Convolutional Neural Networks -- 7.3.9.3 Autoencoders -- 7.3.9.4 Variational Autoencoder -- 7.3.9.5 Recent DNN Approaches -- 7.3.9.6 Spike Neural Networks -- 7.3.9.7 Applications of DNNs to EEG -- 7.3.10 Gaussian Processes -- 7.3.11 Neural Processes -- 7.3.12 Graph Convolutional Networks -- 7.3.13 Naïve Bayes Classifier -- 7.3.14 Hidden Markov Model -- 7.3.14.1 Forward Algorithm -- 7.3.14.2 Backward Algorithm. 7.3.14.3 HMM Design -- 7.4 Common Spatial Patterns -- 7.5 Summary -- References -- Chapter 8 Brain Connectivity and Its Applications -- 8.1 Introduction -- 8.2 Connectivity through Coherency -- 8.3 Phase-Slope Index -- 8.4 Multivariate Directionality Estimation -- 8.4.1 Directed Transfer Function -- 8.4.2 Direct DTF -- 8.4.3 Partial Directed Coherence -- 8.5 Modelling the Connectivity by Structural Equation Modelling -- 8.6 Stockwell Time-Frequency Transform for Connectivity Estimation -- 8.7 Inter-Subject EEG Connectivity -- 8.7.1 Objectives -- 8.7.2 Technological Relevance -- 8.8 State-Space Model for Estimation of Cortical Interactions -- 8.9 Application of Cooperative Adaptive Filters -- 8.9.1 Use of Cooperative Kalman Filter -- 8.9.2 Task-Related Adaptive Connectivity -- 8.9.3 Diffusion Adaptation -- 8.9.4 Brain Connectivity for Cooperative Adaptation -- 8.9.5 Other Applications of Cooperative Learning and Brain Connectivity Estimation -- 8.10 Graph Representation of Brain Connectivity -- 8.11 Tensor Factorization Approach -- 8.12 Summary -- References -- Chapter 9 Event-Related Brain Responses -- 9.1 Introduction -- 9.2 ERP Generation and Types -- 9.2.1 P300 and its Subcomponents -- 9.3 Detection, Separation, and Classification of P300 Signals -- 9.3.1 Using ICA -- 9.3.2 Estimation of Single-Trial Brain Responses by Modelling the ERP Waveforms -- 9.3.3 ERP Source Tracking in Time -- 9.3.4 Time-Frequency Domain Analysis -- 9.3.5 Application of Kalman Filter -- 9.3.6 Particle Filtering and its Application to ERP Tracking -- 9.3.7 Variational Bayes Method -- 9.3.8 Prony's Approach for Detection of P300 Signals -- 9.3.9 Adaptive Time-Frequency Methods -- 9.4 Brain Activity Assessment Using ERP -- 9.5 Application of P300 to BCI -- 9.6 Summary -- References -- Chapter 10 Localization of Brain Sources -- 10.1 Introduction. 10.2 General Approaches to Source Localization -- 10.2.1 Dipole Assumption -- 10.3 Head Model -- 10.4 Most Popular Brain Source Localization Approaches -- 10.4.1 EEG Source Localization Using Independent Component Analysis -- 10.4.2 MUSIC Algorithm -- 10.4.3 LORETA Algorithm -- 10.4.4 FOCUSS Algorithm -- 10.4.5 Standardized LORETA -- 10.4.6 Other Weighted Minimum Norm Solutions -- 10.4.7 Evaluation Indices -- 10.4.8 Joint ICA-LORETA Approach -- 10.5 Forward Solutions to the Localization Problem -- 10.5.1 Partially Constrained BSS Method -- 10.5.2 Constrained Least-Squares Method for Localization of P3a and P3b -- 10.5.3 Spatial Notch Filtering Approach -- 10.6 The Methods Based on Source Tracking -- 10.6.1 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization -- 10.6.2 Hybrid Beamforming - Particle Filtering -- 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 -- Chapter 11 Epileptic Seizure Prediction, Detection, and Localization -- 11.1 Introduction -- 11.2 Seizure Detection -- 11.2.1 Adult Seizure Detection from EEGs -- 11.2.2 Detection of Neonatal Seizure -- 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 -- 11.6.1 Introduction to IED -- 11.6.2 iEED-Times IED Detection from Scalp EEG -- 11.6.3 A Multiview Approach to IED Detection -- 11.6.4 Coupled Dictionary Learning for IED Detection -- 11.6.5 A Deep Learning Approach to IED Detection -- 11.7 Fusion of EEG-fMRI Data for Seizure Prediction -- 11.8 Summary -- References -- Chapter 12 Sleep Recognition, Scoring, and Abnormalities -- 12.1 Introduction. 12.1.1 Definition of Sleep. |
Altri titoli varianti | Electroencephalogram signal processing and machine learning |
Record Nr. | UNINA-9910830667303321 |
Sanei Saeid | ||
Hoboken, New Jersey : , : Wiley, , [2022] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Eeg-based diagnosis of alzheimer disease : a review and novel approaches for feature extraction and classification techniques. / / Nilesh Kulkarni, Vinayak Bairagi |
Autore | Kumar Nilesh |
Edizione | [First edition.] |
Pubbl/distr/stampa | London, England : , : Academic Press, an imprint of Elsevier, , [2018] |
Descrizione fisica | 1 online resource (130 pages) |
Disciplina | 616.8047547 |
Soggetto topico |
Electroencephalography
Alzheimer's disease - Diagnosis |
ISBN |
0-12-815393-8
0-12-815392-X |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910583101003321 |
Kumar Nilesh | ||
London, England : , : Academic Press, an imprint of Elsevier, , [2018] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
EEG/MEG source reconstruction : textbook for electro- and magnetoencephalography / / Thomas R. Knösche, Jens Haueisen |
Autore | Knösche Thomas R. |
Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
Descrizione fisica | 1 online resource (429 pages) |
Disciplina | 616.8047547 |
Soggetto topico | Electroencephalography |
ISBN | 3-030-74918-5 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910616204003321 |
Knösche Thomas R. | ||
Cham, Switzerland : , : Springer, , [2022] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Electrodiagnosis in clinical neurology / [edited by] Michael J. Aminof |
Autore | FINKBEINER, Walter E. |
Edizione | [5. ed.] |
Pubbl/distr/stampa | Philadelphia, : Churchill Livingstone/Elsevier, 2005 |
Descrizione fisica | Testo elettronico (PDF) (XIII, 859 p.) : ill. |
Disciplina | 616.8047547 |
Altri autori (Persone) |
URSELL, Philip C.
DAVIS, Richard L. |
Soggetto topico | Autopsia |
ISBN | 978-0-443-06647-4 |
Formato | Risorse elettroniche |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNISA-990003694980203316 |
FINKBEINER, Walter E. | ||
Philadelphia, : Churchill Livingstone/Elsevier, 2005 | ||
Risorse elettroniche | ||
Lo trovi qui: Univ. di Salerno | ||
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