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Electromagnetic Brain Imaging electronic resource A Bayesian Perspective / by Kensuke Sekihara, Srikantan S. Nagarajan.

By: Sekihara, Kensuke [author.]Contributor(s): Nagarajan, Srikantan S [author.] | SpringerLink (Online service)Material type: TextTextPublication details: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XIV, 270 p. 32 illus., 27 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319149479Subject(s): medicine | Neurosciences | Neurobiology | Biomedical engineering | Biomedicine | Neurosciences | Biomedical Engineering | NeurobiologyDDC classification: 612.8 LOC classification: RC321-580Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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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