Towards the time varying estimation of complex brain connectivity networks by means of a General Linear Kalman Filter approach

J. Toppi, F. Babiloni, G. Vecchiato, F. De Vico Fallani, D. Mattia, S. Salinari, T. Milde, L. Leistritz, H. Witte, L. Astolfi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages6192-6195
Number of pages4
DOIs
Publication statusPublished - 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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    Toppi, J., Babiloni, F., Vecchiato, G., De Vico Fallani, F., Mattia, D., Salinari, S., Milde, T., Leistritz, L., Witte, H., & Astolfi, L. (2012). Towards the time varying estimation of complex brain connectivity networks by means of a General Linear Kalman Filter approach. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 6192-6195). [6347408] https://doi.org/10.1109/EMBC.2012.6347408