Name
Abstract | ||
|---|---|---|
We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one-dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate Bayesian analysis to determine automatically the number and appropriate complexity of the underlying dynamics, with a preference for the simplest... |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/LSP.2006.881515 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Bayesian methods,Frequency,Independent component analysis,Kalman filters,Filtering,Smoothing methods,Vectors,Electroencephalography,Brain modeling,Biomedical signal processing | Interpretability,Pattern recognition,Computer science,Kalman filter,Dynamical systems theory,Artificial intelligence,Independent component analysis,Gaussian process,Biosignal,State space,Bayesian probability | Journal |
Volume | Issue | ISSN |
14 | 4 | 1070-9908 |
Citations | PageRank | References |
1 | 0.36 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvia Chiappa | 1 | 74 | 8.56 |
| David Barber | 2 | 404 | 45.57 |