Name
Title | ||
|---|---|---|
Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework. |
Abstract | ||
|---|---|---|
In this work, we propose a symmetrical multimodal EEG/fMRI information fusion approach dedicated to the identification of event-related bioelectric and hemodynamic responses. Unlike existing, asymmetrical EEG/fMRI data fusion algorithms, we build a joint EEG/fMRI generative model that explicitly accounts for local coupling/uncoupling of bioelectric and hemodynamic activities, which are supposed to share a common substrate. Under a dedicated assumption of spatio-temporal separability, the spatial profile of the common EEG/fMRI sources is introduced as an unknown hierarchical prior on both markers of cerebral activity. Thereby, a devoted Variational Bayesian (VB) learning scheme is derived to infer common EEG/fMRI sources from a joint EEG/fMRI dataset. This yields an estimate of the common spatial profile, which is built as a trade-off between information extracted from EEG and fMRI datasets. Furthermore, the spatial structure of the EEG/fMRI coupling/uncoupling is learned exclusively from the data. The proposed data generative model and devoted VBEM learning scheme thus provide an un-supervised well-balanced approach for the fusion of EEG/fMRI information. We first demonstrate our approach on synthetic data. Results show that, in contrast to classical EEG/fMRI fusion approach, the method proved efficient and robust regardless of the EEG/fMRI discordance level. We apply the method on EEG/fMRI recordings from a patient with epilepsy, in order to identify brain areas involved during the generation of epileptic spikes. The results are validated using intracranial EEG measurements. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.neuroimage.2007.01.044 | NeuroImage |
Keywords | Field | DocType |
information extraction,synthetic data,hemodynamic response,data fusion | Computer science,Cognitive psychology,Synthetic data,Data fusion algorithms,Artificial intelligence,Electroencephalography,Pattern recognition,Spatial structure,Information fusion,Machine learning,EEG-fMRI,Generative model,Bayesian probability | Journal |
Volume | Issue | ISSN |
36 | 1 | 1053-8119 |
Citations | PageRank | References |
41 | 1.94 | 14 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean Daunizeau | 1 | 1406 | 71.53 |
| Christophe Grova | 2 | 175 | 15.99 |
| Guillaume Marrelec | 3 | 426 | 29.12 |
| Jérémie Mattout | 4 | 784 | 48.61 |
| Saad Jbabdi | 5 | 1236 | 55.22 |
| Mélanie Pélégrini-Issac | 6 | 275 | 21.68 |
| Jean-Marc Lina | 7 | 157 | 17.41 |
| Habib Benali | 8 | 837 | 68.94 |