Name
Abstract | ||
|---|---|---|
In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6092003 | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
kalman filters,dynamic system,bayesian methods,hemodynamics,haemodynamics,neurophysiology,kalman filter,mathematical model,noise measurement,bayesian method,deconvolution,estimation,hemodynamic response,noise | Noise measurement,Computer science,Deconvolution,Artificial intelligence,Cubature kalman filter,Covariance,Computer vision,Extended Kalman filter,Neurophysiology,Pattern recognition,Kalman filter,Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
2011 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Havlicek | 1 | 108 | 5.95 |
| Jirí Jan | 2 | 79 | 9.18 |
| Milan Brázdil | 3 | 23 | 4.78 |
| Vince D Calhoun | 4 | 2769 | 268.91 |