Neural Engineering
Editat de Bin Heen Limba Engleză Paperback – 23 aug 2016
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Specificații
ISBN-13: 9781489978875
ISBN-10: 1489978879
Pagini: 812
Ilustrații: X, 800 p.
Dimensiuni: 155 x 235 x 41 mm
Greutate: 12.039999999999999 kg
Ediția:Softcover reprint of the original 2nd ed. 2013
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States
ISBN-10: 1489978879
Pagini: 812
Ilustrații: X, 800 p.
Dimensiuni: 155 x 235 x 41 mm
Greutate: 12.039999999999999 kg
Ediția:Softcover reprint of the original 2nd ed. 2013
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States
Notă biografică
Bin He, PhD., is a leading figure in the field of bioelectric engineering. An internationally recognized scientist with numerous publications, Dr. He has served as the President of the International Society of Bioelectromagnetism and the IEEE Engineering in Medicine & Biology Society (EMBS), and as an Associate or Guest Editor for multiple journals in the field of biomedical engineering. Dr. He is currently Professor of Biomedical Engineering at the University of Minnesota, where he serves as Associate Director for Research, Institute for Engineering in Medicine, Director, Center for Neuroengineering, and Director, Biomedical Functional Imaging and Neuroengineering Laboratory.
Textul de pe ultima copertă
Neural Engineering, 2nd Edition, contains reviews and discussions of contemporary and relevant topics by leading investigators in the field. It is intended to serve as a textbook at the graduate and advanced undergraduate level in a bioengineering curriculum. This principles and applications approach to neural engineering is essential reading for all academics, biomedical engineers, neuroscientists, neurophysiologists, and industry professionals wishing to take advantage of the latest and greatest in this emerging field.
Caracteristici
Second Edition of bestselling book
Chapters written by top researchers in the field
Suitable for graduate and upper undergraduate use
Includes supplementary material: sn.pub/extras
Chapters written by top researchers in the field
Suitable for graduate and upper undergraduate use
Includes supplementary material: sn.pub/extras
Cuprins
Chapter 1: Introduction to Neurophysiology
Chapter 2: Biopotential Measurements and Electrodes
Chapter 3: EEG Signal Processing: Theory and Applications
Chapter 4: Brain-Computer Interfaces
Chapter 5: Intracortical Brain Machine Interfaces
Chapter 6: Deep Brain Stimulation: Emerging Technologies and Applications
Chapter 7: Transcranial Magnetic Stimulation: Principles and Applications
Chapter 8: Transcranial Electrical Stimulation
Chapter 9: Optogenetics: Novel Brain Interface Technology that Originates in Bioprospecting
Chapter 10: Selective Chronic Recording in the Peripheral Nervous System
Chapter 11: Functional Magnetic Resonance Imaging
Chapter 12: Photoacoustic Tomography of Neural Systems
Chapter 13: Electrophysiological Mapping and Source Imaging
Chapter 14: Exploring Functional and Causal Connectivity in the Brain
Chapter 15: Deep Learning Models with Applications to Brain Image Analysis
Chapter 16: Neural Modeling
Chapter 17: Linear Dynamics & Control of Brain Networks
Chapter 18: Deciphering the Neuronal Population Code
Chapter 19: Machine Intelligence Based Epileptic Seizure Forecasting
Chapter 20: Retinal Prosthesis
Chapter 21: Retinal Bioengineering
Chapter 22: Neural Tissue Engineering
Index
Chapter 2: Biopotential Measurements and Electrodes
Chapter 3: EEG Signal Processing: Theory and Applications
Chapter 4: Brain-Computer Interfaces
Chapter 5: Intracortical Brain Machine Interfaces
Chapter 6: Deep Brain Stimulation: Emerging Technologies and Applications
Chapter 7: Transcranial Magnetic Stimulation: Principles and Applications
Chapter 8: Transcranial Electrical Stimulation
Chapter 9: Optogenetics: Novel Brain Interface Technology that Originates in Bioprospecting
Chapter 10: Selective Chronic Recording in the Peripheral Nervous System
Chapter 11: Functional Magnetic Resonance Imaging
Chapter 12: Photoacoustic Tomography of Neural Systems
Chapter 13: Electrophysiological Mapping and Source Imaging
Chapter 14: Exploring Functional and Causal Connectivity in the Brain
Chapter 15: Deep Learning Models with Applications to Brain Image Analysis
Chapter 16: Neural Modeling
Chapter 17: Linear Dynamics & Control of Brain Networks
Chapter 18: Deciphering the Neuronal Population Code
Chapter 19: Machine Intelligence Based Epileptic Seizure Forecasting
Chapter 20: Retinal Prosthesis
Chapter 21: Retinal Bioengineering
Chapter 22: Neural Tissue Engineering
Index