Name
Abstract | ||
|---|---|---|
The use of multivariate pattern recognition for the analysis of neural representations encoded in fMRI data has become a significant research topic, with wide applications in neuroscience and psychology. A popular approach is to learn a mapping from the data to the observed behavior. However, identifying the instantaneous cognitive state without reference to external conditions is a relatively unexplored problem and could provide important insights into mental processes. In this paper, we present preliminary but promising results from the application of an unsupervised learning technique to identify distinct brain states. The temporal ordering of the states were seen to be synchronized with the experimental conditions, while the spatial distribution of activity in a state conformed with the expected functional recruitment. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15745-5_25 | MICCAI (2) |
Keywords | Field | DocType |
expected functional recruitment,fmri data,instantaneous cognitive state,distinct brain state,neural representation,important insight,multivariate pattern recognition,external condition,mental process,unsupervised learning,experimental condition,pattern recognition | Computer vision,Pattern recognition,Computer science,Multivariate statistics,Unsupervised learning,Artificial intelligence,Cognition,Machine learning | Conference |
Volume | Issue | ISSN |
13 | Pt 2 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-15744-0 | 2 | 0.43 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F Janoos | 1 | 2 | 0.43 |
| R. Machiraju | 2 | 3 | 0.80 |
| S. Sammet | 3 | 5 | 0.89 |
| M V Knopp | 4 | 4 | 1.06 |
| Istvan A Mórocz | 5 | 4 | 0.84 |