Electromagnetic Brain Imaging : A Bayesian Perspective

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. ℗ℓIt 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. ℗ℓThis textbook will not only appeal to graduate students but all scientists and engineers engaged in research on electromagnetic brain imaging Front Matter....Pages i-xiv Introduction to Electromagnetic Brain Imaging....Pages 1-8 Minimum-Norm-Based Source Imaging Algorithms....Pages 9-28 Adaptive Beamformers....Pages 29-50 Sparse Bayesian (Champagne) Algorithm....Pages 51-74 Bayesian Factor Analysis: A Versatile Framework for Denoising, Interference Suppression, and Source Localization....Pages 75-117 A Unified Bayesian Framework for MEG/EEG Source Imaging....Pages 119-137 Source-Space Connectivity Analysis Using Imaginary Coherence....Pages 139-169 Estimation of Causal Networks: Source-Space Causality Analysis....Pages 171-198 Detection of Phase–Amplitude Coupling in MEG Source Space: An Empirical Study....Pages 199-213 Back Matter....Pages 215-270 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. ℗lIt 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. ℗lThis textbook will not only appeal to graduate students but all scientists and engineers engaged in research on electromagnetic brain imaging