NDLI: Features Extraction from Time-Varying Cortical Networks Adopting a Theoretical Graph Approach

Content Provider	IEEE Xplore Digital Library
Author	De Vico, F.F. Astolfi, L. Cincotti, F. Mattia, D. Tocci, A. Capitanio, S. Marciani, M.G. Salinari, H. Hesse, W. Witte, H. Gao, S. Colosimo, A. Babiloni, F.
Copyright Year	2007
Description	Author affiliation: Univ. ";Sapienza";, Rome (De Vico, F.F.)
Abstract	In this work, a novel approach is proposed in order to capture relevant features related to the structure and organization of the functional brain networks estimated in the time-frequency domain. To achieve this, we used a cascade of computational tools able to estimate first the electrical activity of the cortical surface by using high resolution EEG techniques. Then, on the cortical signals from different regions of interests we estimated the time-varying functional connectivity patterns by means of the adaptive partial directed coherence. Such time-varying connectivity estimation returns a series of causality patterns evolving during the examined task which can be summarized and interpreted with the aid of mathematical indexes based on the graph theory. The combination of all these methods is demonstrated on a set of high resolution EEG data recorded from a healthy subject performing a simple foot movement.
Starting Page	5198
Ending Page	5201
File Size	447898
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4353513
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Electroencephalography Brain modeling Neuroscience Biomedical computing Complex networks Frequency estimation Frequency domain analysis Spatial resolution Magnetic heads
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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