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

By: Sekihara, Kensuke.
Contributor(s): SpringerLink (Online service) | Nagarajan, Srikantan S.
Material type: materialTypeLabelE-bookPublisher: Cham, Switzerland : Springer, 2015Description: 1 recurso en línea (XIV, 270 p.) : 32 ilustraciones, 27 ilustraciones en color.ISBN: 9783319149479.Subject: Neurobiología | Ingeniería biomédica | Neurobiología | Medicina | Neurociencias | NeurocienciasDDC classification: 612.8 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
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 Phaseâ€{u1B70}litude Coupling in MEG Source Space: An Empirical Study.
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. Â{u9D20} 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. Â{u4A29}s 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 Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias de la Salud RC386.6.M36 S455 2015 EB (Browse shelf(Opens below)) .i11569086 Acceso electrónico eBOOK .i11569086
Total holds: 0

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 Phaseâ€{u1B70}litude 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. Â{u9D20} 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. Â{u4A29}s textbook will not only appeal to graduate students but all scientists and engineers engaged in research on electromagnetic brain imaging.

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