Name
Abstract | ||
|---|---|---|
This work considers the problem of brain imaging using simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). To this end, we introduce a linear coupling model that links the electrical LEG signal to the hemodynamic response from the blood oxygen level dependent (BOLD) signal. Both modalities are then symmetrically integrated, to achieve a high resolution in time and space while allowing some robustness against potential decoupling of the BOLD effect. The novelty of the approach consists in expressing the joint imaging problem as a linear inverse problem, which is addressed using sparse regularization. We consider several sparsity-enforcing penalties, which naturally reflect the fact that only few areas of the brain are activated at a certain time, and allow for a fast optimization through proximal algorithms. The significance of the method and the effectiveness of the algorithms are demonstrated through numerical investigations on a spherical head model. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | EEG-fMRI,multimodal imaging,structured sparsity,EEG inverse problem |
Field | DocType | ISSN |
Computer vision,Functional magnetic resonance imaging,Pattern recognition,Noise measurement,Computer science,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Inverse problem,Neuroimaging,Electroencephalography,EEG-fMRI | Conference | 2076-1465 |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Oberlin | 1 | 128 | 14.57 |
| Christian Barillot | 2 | 1290 | 133.50 |
| Rémi Gribonval | 3 | 1207 | 83.59 |
| Pierre Maurel | 4 | 42 | 5.80 |