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Electromagnetic Brain Imaging: A Bayesian Perspective

Autor Kensuke Sekihara, Srikantan S. Nagarajan
en Limba Engleză Hardback – 20 mar 2015
This graduate level textbook provides a coherent introduction to the body of main-stream algorithms used in electromagnetic brain imaging, with specific emphasis on novel Bayesian algorithms. It helps readers to more easily understand literature in biomedical engineering and related fields and be ready to pursue research in either the engineering or the neuroscientific aspects of electromagnetic brain imaging. This textbook will not only appeal to graduate students but all scientists and engineers engaged in research on electromagnetic brain imaging.
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Specificații

ISBN-13: 9783319149462
ISBN-10: 3319149466
Pagini: 278
Ilustrații: XIV, 270 p. 32 illus., 27 illus. in color.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.58 kg
Ediția:2015
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Graduate

Cuprins

Introduction to Electromagnetic Brain Imaging.- Minimum-Norm-Based Source Imaging Algorithms.- Adaptive Beamformers.- Sparse Bayesian (Champagne) Algorithm.- Bayesian Factor Analysis: A Versatile Framework.- A Unified Bayesian Framework for MEG/EEG Source.- Source-Space Connectivity Analysis Using Imaginary.- Estimation of Causal Networks: Source-Space Causality Analysis.- Detection of Phase–Amplitude Coupling in MEG Source Space: An Empirical Study.

Textul de pe ultima copertă

This graduate level textbook provides a coherent introduction to the body of main-stream algorithms used in electromagnetic brain imaging, with specific emphasis on novel Bayesian algorithms.  It helps readers to more easily understand literature in biomedical engineering and related fields, and be ready to pursue research in either the engineering or the neuroscientific aspects of electromagnetic brain imaging.  This textbook will not only appeal to graduate students but all scientists and engineers engaged in research on electromagnetic brain imaging.

Caracteristici

Provides a theoretical framework for source imaging methodology Specific focus on Bayesian algorithms Unique approach to the recent advances Includes supplementary material: sn.pub/extras