Name
Title | ||
|---|---|---|
A hierarchical Bayesian M/EEG imagingmethod correcting for incomplete spatio-temporal priors. |
Abstract | ||
|---|---|---|
In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatio-temporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio-temporal prior belief. Both artificial data and real EEG data is used to demonstrate the efficacy of the model. © 2013 IEEE. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISBI.2013.6556536 | Biomedical Imaging |
Keywords | Field | DocType |
eeg,inverse problem,meg,spatio-temporal prior,variational bayes,inverse problems,data models,computational modeling,magnetoencephalography,electroencephalography,imaging | Computer vision,Bayesian inference,Subspace topology,Pattern recognition,Computer science,Basis function,Artificial intelligence,Inverse problem,Prior probability,Electroencephalography,Bayesian probability,Spatiotemporal pattern | Conference |
Volume | Issue | ISSN |
null | null | 19458452 |
ISBN | Citations | PageRank |
978-1-4673-6456-0 | 3 | 0.44 |
References | Authors | |
7 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carsten Stahlhut | 1 | 55 | 8.47 |
| Hagai Attias | 2 | 856 | 96.36 |
| Kensuke Sekihara | 3 | 274 | 27.27 |
| David P. Wipf | 4 | 584 | 46.31 |
| Lars Kai Hansen | 5 | 2776 | 341.03 |
| Srikantan S. Nagarajan | 6 | 536 | 64.99 |