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Electromagnetic Brain Imaging = A Ba...
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Nagarajan, Srikantan S.
Electromagnetic Brain Imaging = A Bayesian Perspective /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Electromagnetic Brain Imaging/ by Kensuke Sekihara, Srikantan S. Nagarajan.
其他題名:
A Bayesian Perspective /
作者:
Sekihara, Kensuke.
其他作者:
Nagarajan, Srikantan S.
面頁冊數:
XIV, 270 p. 32 illus., 27 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Neurosciences. -
電子資源:
https://doi.org/10.1007/978-3-319-14947-9
ISBN:
9783319149479
Electromagnetic Brain Imaging = A Bayesian Perspective /
Sekihara, Kensuke.
Electromagnetic Brain Imaging
A Bayesian Perspective /[electronic resource] :by Kensuke Sekihara, Srikantan S. Nagarajan. - 1st ed. 2015. - XIV, 270 p. 32 illus., 27 illus. in color.online resource.
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.
ISBN: 9783319149479
Standard No.: 10.1007/978-3-319-14947-9doiSubjects--Topical Terms:
593561
Neurosciences.
LC Class. No.: RC321-580
Dewey Class. No.: 612.8
Electromagnetic Brain Imaging = A Bayesian Perspective /
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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.
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