Conference paper
Probabilistic M/EEG source imaging from sparse spatio-temporal event structure
While MEG and EEG source imaging methods have to tackle a severely ill-posed problem their success can be stated as their ability to constrain the solutions using appropriate priors. In this paper we propose a hierarchical Bayesian model facilitating spatio-temporal patterns through the use of both spatial and temporal basis functions.
We demonstrate the efficacy of the model on both artificial data and real EEG data.
Language: | English |
---|---|
Year: | 2012 |
Proceedings: | 2nd NIPS Workshop on Machine Learning and Interpretation in NeuroImaging (MLINI 2012) |
Types: | Conference paper |
ORCIDs: | Hansen, Lars Kai |