A unified Bayesian framework for MEG/EEG source imaging
نویسندگان
چکیده
منابع مشابه
A unified Bayesian framework for MEG/EEG source imaging
The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions. However, the means by which these priors are ...
متن کاملUnified Bayesian Framework for MEG / EEG Source Imaging
product of distributed dipole moments and their respective lead field matrix. In a similar way, the measured field can be represented as a product of a multipole lead field matrix and the coefficients of a multipole expansion. In both cases, the lead field matrix does not depend on the data, but rather only on sensor and field generator (i.e. dipoles or multipole expansion point) positions and ...
متن کاملA Unified Bayesian Framework for Face Recognition
This paper introduces a Bayesian framework for face recognition which unifies popular methods such as the eigenfaces and Fisherfaces and can generate two novel probabilistic reasoning models (PRM) with enhanced performance. The Bayesian framework first applies Principal Component Analysis (PCA) for dimensionality reduction with the resulting image representation enjoying noise reduction and enh...
متن کاملA Unified Bayesian Framework for Adaptive Visual Tracking
We propose a novel method for tracking objects in a video scene that undergo drastic changes in their appearance. These changes may arise due to out-of-plane rotation, abrupt or gradual changes in illumination in outdoor scenarios, or changing position with respect to near light-sources indoors. The key problem with most existing models is that they are either non-adaptive (rendering them non-r...
متن کاملBayesian Eigenobjects: A Unified Framework for 3D Robot Perception
We introduce Bayesian Eigenobjects (BEOs), a novel object representation that is the first technique able to perform joint classification, pose estimation, and 3D geometric completion on previously unencountered and partially observed query objects. BEOs employ Variational Bayesian Principal Component Analysis (VBPCA) directly on 3D object representations to create generative and compact probab...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: NeuroImage
سال: 2009
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2008.02.059