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Electromagnetic brain imaging : a Bayesian perspective / Kensuke Sekihara and Srikatan S. Nagarajan.

By: Contributor(s): Material type: TextTextPublication details: Cham : Springer, 2015.Description: xiv, 270 p. ; illustrationsISBN:
  • 9783319149462
Subject(s): DDC classification:
  • 616.8047547 23 Se463
Contents:
1. Introduction to Electromagnetic Brain Imaging -- 2. Minimum-Norm-Based Source Imaging Algorithms -- 3. Adaptive Beamformers -- 4. Sparse Bayesian (Champagne) Algorithm -- 5. Bayesian Factor Analysis: A Versatile Framework -- 6. A Unified Bayesian Framework for MEG/EEG Source -- 7. Source-Space Connectivity Analysis Using Imaginary -- 8. Estimation of Causal Networks: Source-Space Causality Analysis -- 9. Detection of Phase{u2013}Amplitude Coupling in MEG Source Space: An Empirical Study -- Appendices.
Summary: 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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 616.8047547 Se463 (Browse shelf(Opens below)) Available 137084
Total holds: 0

Includes bibliographical references and index.

1. Introduction to Electromagnetic Brain Imaging --
2. Minimum-Norm-Based Source Imaging Algorithms --
3. Adaptive Beamformers --
4. Sparse Bayesian (Champagne) Algorithm --
5. Bayesian Factor Analysis: A Versatile Framework --
6. A Unified Bayesian Framework for MEG/EEG Source --
7. Source-Space Connectivity Analysis Using Imaginary --
8. Estimation of Causal Networks: Source-Space Causality Analysis --
9. Detection of Phase{u2013}Amplitude Coupling in MEG Source Space: An Empirical Study --
Appendices.

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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