One of the main limitations of the brain functional connectivity estimation methods based on Autoregressive Modeling, like the Granger Causality family of estimators, is the hypothesis that only stationary signals can be included in the estimation process. This hypothesis precludes the analysis of transients which often contain important information about the neural processes of interest. On the other hand, previous techniques developed for overcoming this limitation are affected by problems linked to the dimension of the multivariate autoregressive model (MVAR), which prevents from analysing complex networks like those at the basis of most cognitive functions in the brain. The General Linear Kalman Filter (GLKF) approach to the estimation of adaptive MVARs was recently introduced to deal with a high number of time series (up to 60) in a full multivariate analysis. In this work we evaluated the performances of this new method in terms of estimation quality and adaptation speed, by means of a simulation study in which specific factors of interest were systematically varied in the signal generation to investigate their effect on the method performances. The method was then applied to high density EEG data related to an imaginative task. The results confirmed the possibility to use this approach to study complex connectivity networks in a full multivariate and adaptive fashion, thus opening the way to an effective estimation of complex brain connectivity networks.

Towards the Time Varying Estimation of Complex Brain Connectivity Networks by means of a General Linear Kalman Filter Approach / Toppi, Jlenia; Babiloni, Fabio; Vecchiato, Giovanni; DE VICO FALLANI, Fabrizio; D., Mattia; Salinari, Serenella; T., Milde; L., Leistritz; H., Witte; Astolfi, Laura. - (2012), pp. 6192-6195. (Intervento presentato al convegno 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society tenutosi a San Diego, CA; USA nel 28 August - 01 September 2012) [10.1109/EMBC.2012.6347408].

Towards the Time Varying Estimation of Complex Brain Connectivity Networks by means of a General Linear Kalman Filter Approach

TOPPI, JLENIA;BABILONI, Fabio;VECCHIATO, GIOVANNI;DE VICO FALLANI, FABRIZIO;SALINARI, Serenella;ASTOLFI, LAURA
2012

Abstract

One of the main limitations of the brain functional connectivity estimation methods based on Autoregressive Modeling, like the Granger Causality family of estimators, is the hypothesis that only stationary signals can be included in the estimation process. This hypothesis precludes the analysis of transients which often contain important information about the neural processes of interest. On the other hand, previous techniques developed for overcoming this limitation are affected by problems linked to the dimension of the multivariate autoregressive model (MVAR), which prevents from analysing complex networks like those at the basis of most cognitive functions in the brain. The General Linear Kalman Filter (GLKF) approach to the estimation of adaptive MVARs was recently introduced to deal with a high number of time series (up to 60) in a full multivariate analysis. In this work we evaluated the performances of this new method in terms of estimation quality and adaptation speed, by means of a simulation study in which specific factors of interest were systematically varied in the signal generation to investigate their effect on the method performances. The method was then applied to high density EEG data related to an imaginative task. The results confirmed the possibility to use this approach to study complex connectivity networks in a full multivariate and adaptive fashion, thus opening the way to an effective estimation of complex brain connectivity networks.
2012
34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Autoregressive modeling; Brain connectivity; Cognitive functions; Complex networks; Estimation methods; Estimation process; Estimation quality; Functional connectivity; Granger Causality; High density; Linear Kalman filters; Multi variate analysis; Multivariate autoregressive models; Neural process; Signal generation; Simulation studies; Stationary signal; Time varying
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Towards the Time Varying Estimation of Complex Brain Connectivity Networks by means of a General Linear Kalman Filter Approach / Toppi, Jlenia; Babiloni, Fabio; Vecchiato, Giovanni; DE VICO FALLANI, Fabrizio; D., Mattia; Salinari, Serenella; T., Milde; L., Leistritz; H., Witte; Astolfi, Laura. - (2012), pp. 6192-6195. (Intervento presentato al convegno 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society tenutosi a San Diego, CA; USA nel 28 August - 01 September 2012) [10.1109/EMBC.2012.6347408].
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