Estimation of Cortical Connectivity From EEG Using State-Space Models

A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals...

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Vydané v:	IEEE transactions on biomedical engineering Ročník 57; číslo 9; s. 2122 - 2134
Hlavní autori:	Cheung, Bing Leung Patrick, Riedner, Brady Alexander, Tononi, Giulio, Van Veen, Barry D.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.09.2010 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Analytical models Biological and medical sciences Brain modeling Cerebral Cortex - physiology Computer Simulation Computerized, statistical medical data processing and models in biomedicine Covariance matrix Economic models Effective connectivity Electrodiagnosis. Electric activity recording Electroencephalography Electroencephalography - methods Equations expectation-maximization (EM) algorithm Expectation-maximization algorithms Fundamental and applied biological sciences. Psychology General aspects. Models. Methods Granger causality Humans Integrated approach Investigative techniques, diagnostic techniques (general aspects) Medical management aid. Diagnosis aid Medical sciences Models, Neurological Multivariate Analysis multivariate autoregressive (MVAR) models Nervous system Noise Noise measurement Physics Regression Analysis Signal analysis Signal Processing, Computer-Assisted Spatial distribution State estimation state-space models Studies Vertebrates: nervous system and sense organs Human expectation-maximization (EM) algorithm Multivariate process Cerebral cortex Central nervous system Electrophysiology Electroencephalography Autoregressive model state-space models State space method Modeling Encephalon multivariate autoregressive (MVAR) models Connectedness Causality EM algorithm Granger causality Biomedical engineering Effective connectivity
ISSN:	0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:	A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume that the cortical signals originate from known regions of cortex, but the spatial distribution of activity within each region is unknown. An expectation-maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next, an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	0018-9294 1558-2531 1558-2531
DOI:	10.1109/TBME.2010.2050319