Electromagnetic Brain Imaging: A Bayesian Perspective
Author:   Kensuke Sekihara ,  Srikantan S. Nagarajan
Publisher:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2015
ISBN:  

9783319356433


Pages:   270
Publication Date:   06 October 2016
Format:   Paperback
Availability:   In Print   Availability explained
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Electromagnetic Brain Imaging: A Bayesian Perspective


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Overview

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.

Full Product Details

Author:   Kensuke Sekihara ,  Srikantan S. Nagarajan
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2015
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   4.336kg
ISBN:  

9783319356433


ISBN 10:   3319356437
Pages:   270
Publication Date:   06 October 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

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.

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