Name
Abstract | ||
|---|---|---|
We propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being invariant with respect to phase angles and rotations of the coordinate system. We demonstrate the method's ability to reconstruct simulated sources of random shape and show that the accuracy of the recovered sources can be increased, when interrelated field patterns are co-localized. Technically, this leads to large-scale mathematical problems, which are solved using recent advances in convex optimization. We apply our method for localizing brain areas involved in different types of motor imagery using real data from Brain–Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neuroimage.2010.09.003 | NeuroImage |
Keywords | Field | DocType |
EEG,MEG,Inverse problem,Basis field,Large-scale optimization,Motor imagery,Brain–computer interfaces | Coordinate system,Computer science,Brain–computer interface,Cognitive psychology,Inverse problem,Artificial intelligence,Electroencephalography,Computer vision,Pattern recognition,Fourier series,Invariant (mathematics),Convex optimization,Motor imagery | Journal |
Volume | Issue | ISSN |
54 | 2 | 1053-8119 |
Citations | PageRank | References |
30 | 1.83 | 21 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Haufe | 1 | 645 | 36.63 |
| Ryota Tomioka | 2 | 1367 | 91.68 |
| Thorsten Dickhaus | 3 | 237 | 14.52 |
| Claudia Sannelli | 4 | 180 | 13.18 |
| B. Blankertz | 5 | 2918 | 334.21 |
| G Nolte | 6 | 535 | 50.42 |
| Klaus-Robert Müller | 7 | 12756 | 1615.17 |