Estimation of high-dimensional connectivity in FMRI data via subspace autoregressive models
We consider the challenge in estimating effective connectivity of brain networks with a large number of nodes from fMRI data. The classical vector autoregressive (VAR) modeling tends to produce unreliable estimates for large dimensions due to the huge number of parameters. We propose a subspace esti...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
IEEE Computer Society
2016
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73097/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987892034&doi=10.1109%2fSSP.2016.7551799&partnerID=40&md5=32b51c62976b05b5c2a2865f1f2a45e3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|