New Methods of Source Reconstruction for Magnetoencephalography

Evaluating the dynamic activities of human brain is a challenge for academia. This book provides a novel way of MEG source reconstruction from the computer science perspective (specified on pattern recognition, graph theory and the signal processing). On one hand, pattern recognition is widely and successfully used for the different (and mainly unrelated) applications on classification', diagnosis' or identification . On the other hand, since MEG source reconstruction is fundamentally an ill-posed inverse problem, the conventional approaches by no means may represent the true image at all times. The proposed methods of MEG source reconstruction in this book sufficiently introduce the thoughts, process and solutions from pattern recognition into solving the problem of MEG source reconstruction. The combination of these two fields not only opens an innovational angle, also inspires methodological research on other medical imaging technologies, such as fMRI, EEG. The target readers of this monograph are: university/college students of computer science, mathematics and physics; technicians in imaging algorithm; relevant scholars and colleagues.

Autorentext

Dr. Jing Kan obtained PH.D. in computer science at the University of York, UK, in 2011. Her research interests are in a wide range of topics related to 3D surface reconstruction, spectral methods for graphs, mathematics of non-linear/linear dynamic systems and biomedical application, non-invasive imaging method and machine learning.