Name
Abstract | ||
|---|---|---|
Functional magnetic resonance imaging (fMRI) time series is nonlinear and composed of components at multiple temporal scales, which presents significant challenges to its analysis. In the literature, significant effort has been devoted into model-based fMRI signal analysis, while much less attention has been directed to data-driven fMRI signal analysis. In this paper, we present a novel data-drive... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TBME.2012.2221125 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Time series analysis,Correlation,Analytical models,Noise,Signal resolution,Educational institutions,Wavelet transforms | Brain mapping,Signal processing,Computer vision,Nonlinear system,Functional magnetic resonance imaging,Sample mean and sample covariance,Neurophysiology,Computer science,Speech recognition,Artificial intelligence | Journal |
Volume | Issue | ISSN |
60 | 1 | 0018-9294 |
Citations | PageRank | References |
5 | 0.47 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Deng | 1 | 96 | 7.56 |
| Dajiang Zhu | 2 | 320 | 36.72 |
| Jinglei Lv | 3 | 205 | 26.70 |
| Lei Guo | 4 | 1661 | 142.63 |
| Tianming Liu | 5 | 1033 | 112.95 |