Name
Title | ||
|---|---|---|
Advanced methods for time-varying effective connectivity estimation in memory processes. |
Abstract | ||
|---|---|---|
Memory processes are based on large cortical networks characterized by non-stationary properties and time scales which represent a limitation to the traditional connectivity estimation methods. The recent development of connectivity approaches able to consistently describe the temporal evolution of large dimension connectivity networks, in a fully multivariate way, represents a tool that can be used to extract novel information about the processes at the basis of memory functions. In this paper, we applied such advanced approach in combination with the use of state-of-the-art graph theory indexes, computed on the connectivity networks estimated from high density electroencephalographic (EEG) data recorded in a group of healthy adults during the Sternberg Task. The results show how this approach is able to return a characterization of the main phases of the investigated memory task which is also sensitive to the increased length of the numerical string to be memorized. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610155 | EMBC |
Keywords | Field | DocType |
numerical string,time-varying effective connectivity estimation,large cortical networks,nonstationary properties,temporal evolution,data recording,high density electroencephalographic data recording,kalman filters,time-varying systems,memory processes,geriatrics,healthy adults,electroencephalography,large dimension connectivity networks,numerical analysis,medical signal processing,connectivity network estimation,memory functions,traditional connectivity estimation methods,memory task,sternberg task,graph theory,multivariable systems,state-of-the-art graph theory indexes,eeg data recording,indexes,electrodes,statistics,estimation,sociology | Graph theory,Data recording,Computer vision,Memory functions,Pattern recognition,Computer science,Multivariate statistics,High density,Kalman filter,Theoretical computer science,Artificial intelligence,Electroencephalography | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 1 |
PageRank | References | Authors |
0.40 | 6 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Astolfi | 1 | 170 | 20.52 |
| Jlenia Toppi | 2 | 136 | 22.74 |
| Guilherme Wood | 3 | 14 | 5.29 |
| S Kober | 4 | 1 | 0.40 |
| M Risetti | 5 | 1 | 0.73 |
| L Macchiusi | 6 | 1 | 0.40 |
| S Salinari | 7 | 1 | 0.40 |
| Fabio Babiloni | 8 | 315 | 40.23 |
| D Mattia | 9 | 37 | 7.96 |