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Electromagnetic Brain Imaging

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Cover of 'Electromagnetic Brain Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Electromagnetic Brain Imaging
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    Chapter 2 Minimum-Norm-Based Source Imaging Algorithms
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    Chapter 3 Adaptive Beamformers
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    Chapter 4 Sparse Bayesian (Champagne) Algorithm
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    Chapter 5 Bayesian Factor Analysis: A Versatile Framework for Denoising, Interference Suppression, and Source Localization
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    Chapter 6 A Unified Bayesian Framework for MEG/EEG Source Imaging
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    Chapter 7 Source-Space Connectivity Analysis Using Imaginary Coherence
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    Chapter 8 Estimation of Causal Networks: Source-Space Causality Analysis
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    Chapter 9 Detection of Phase–Amplitude Coupling in MEG Source Space: An Empirical Study
Attention for Chapter 4: Sparse Bayesian (Champagne) Algorithm
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Chapter title
Sparse Bayesian (Champagne) Algorithm
Chapter number 4
Book title
Electromagnetic Brain Imaging
Published by
Springer, Cham, January 2015
DOI 10.1007/978-3-319-14947-9_4
Book ISBNs
978-3-31-914946-2, 978-3-31-914947-9
Authors

Kensuke Sekihara, Srikantan S. Nagarajan

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