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

Content Provider	IEEE Xplore Digital Library
Author	Toppi, J. Babiloni, F. Vecchiato, G. De Vico Fallani, F. Mattia, D. Salinari, S. Milde, T. Leistritz, L. Witte, H. Astolfi, L.
Copyright Year	2012
Description	Author affiliation: Bernstein Group Comput. Neurosci., Friedrich Schiller Univ., Jena, Germany (Milde, T.; Leistritz, L.; Witte, H.) \|\| Dept. of Comput., Control, & Manage. Eng., Univ. of Rome Sapienza, Rome, Italy (Toppi, J.; Salinari, S.; Astolfi, L.) \|\| Fondazione Santa Lucia, Rome, Italy (Mattia, D.) \|\| Dept. of Physiol. & Pharmacology, Univ. of Rome Sapienza, Rome, Italy (Babiloni, F.; Vecchiato, G.; De Vico Fallani, F.)
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.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	6192
Ending Page	6195
File Size	858904
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6347408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal to noise ratio Brain modeling Estimation Transient analysis Kalman filters Adaptation models Electroencephalography
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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