Name
Title | ||
|---|---|---|
Parametric recurrence quantification analysis of autoregressive processes for pattern recognition in multichannel electroencephalographic data |
Abstract | ||
|---|---|---|
•Analytic expressions of five RQA measures for autoregressive processes are derived.•Parametric RQA (pRQA) applies to time series modeled by autoregressive processes.•pRQA is computationally fast and accurate.•pRQA can detect spatial patterns in multichannel data, e.g. EEG data. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107572 | Pattern Recognition |
Keywords | DocType | Volume |
Recurrence plots,Recurrence quantification analysis,Autoregressive stochastic processes,Asymptotic recurrence measures,Multichannel data,EEG Data | Journal | 109 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sofiane Ramdani | 1 | 10 | 5.10 |
| Anthony Boyer | 2 | 0 | 0.68 |
| Stéphane Caron | 3 | 78 | 12.87 |
| François Bonnetblanc | 4 | 0 | 0.68 |
| Frédéric Bouchara | 5 | 35 | 10.46 |
| Hugues Duffau | 6 | 154 | 12.16 |
| Annick Lesne | 7 | 41 | 7.12 |