Name
Abstract | ||
|---|---|---|
With the advent of sophisticated acquisition and analysis techniques, decoding the contents of someone's experience has become a reality. We propose a straightforward linear Gaussian approach, where decoding relies on the inversion of properly regularized encoding models, which can still be solved analytically. In order to test our approach we acquired functional magnetic resonance imaging data under a rapid event-related design in which subjects were presented with handwritten characters. Our approach is shown to yield state-of-the-art reconstructions of perceived characters as estimated from BOLD responses. This even holds for previously unseen characters. We propose that this framework serves as a baseline with which to compare more sophisticated models for which analytical inversion is infeasible. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neuroimage.2013.07.043 | NeuroImage |
Keywords | Field | DocType |
fMRI analysis,Image reconstruction,Linear regression,Regularization | Computer science,Inversion (meteorology),Cognitive psychology,Regularization (mathematics),Artificial intelligence,Linear regression,Iterative reconstruction,Functional magnetic resonance imaging,Pattern recognition,Speech recognition,Gaussian,Decoding methods,Encoding (memory) | Journal |
Volume | ISSN | Citations |
83 | 1053-8119 | 21 |
PageRank | References | Authors |
1.00 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanne Schoenmakers | 1 | 36 | 2.70 |
| Markus Barth | 2 | 53 | 4.01 |
| Tom Heskes | 3 | 1519 | 198.44 |
| Marcel Van Gerven | 4 | 321 | 39.35 |