Name
Title | ||
|---|---|---|
Predicting subject-driven actions and sensory experience in a virtual world with Relevance Vector Machine Regression of fMRI data. |
Abstract | ||
|---|---|---|
In this work we illustrate the approach of the Maastricht Brain Imaging Center to the PBAIC 2007 competition, where participants had to predict, based on fMRI measurements of brain activity, subject driven actions and sensory experience in a virtual world. After standard pre-processing (slice scan time correction, motion correction), we generated rating predictions based on linear Relevance Vector Machine (RVM) learning from all brain voxels. Spatial and temporal filtering of the time series was optimized rating by rating. For some of the ratings (e.g. Instructions, Hits, Faces, Velocity), linear RVM regression was accurate and very consistent within and between subjects. For other ratings (e.g. Arousal, Valence) results were less satisfactory. Our approach ranked overall second. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neuroimage.2010.09.062 | NeuroImage |
Keywords | Field | DocType |
relevance vector machine,time series,multivariate regression,brain imaging,virtual worlds | Voxel,Bayesian inference,Regression,Multivariate statistics,Psychology,Brain activity and meditation,Artificial intelligence,Relevance vector machine,Neuroimaging,Perception,Machine learning | Journal |
Volume | Issue | ISSN |
56 | 2 | 1053-8119 |
Citations | PageRank | References |
11 | 0.60 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giancarlo Valente | 1 | 127 | 10.62 |
| Federico De Martino | 2 | 325 | 20.34 |
| Fabrizio Esposito | 3 | 421 | 36.61 |
| Rainer Goebel | 4 | 670 | 56.00 |
| Elia Formisano | 5 | 778 | 58.91 |