NDLI: State-Space Multivariate Autoregressive Models for Estimation of Cortical Connectivity from EEG

Content Provider	IEEE Xplore Digital Library
Author	Cheung, B.L.P. Riedner, B. Tononi, G. Van Veen, B.D.
Copyright Year	2009
Description	Author affiliation: Department of Psychiatry, University of Wisconsin-Madison, WI 53706, USA (Riedner, B.; Tononi, G.) \|\| Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA (Cheung, B.L.P.; Van Veen, B.D.)
Abstract	We propose using a state-space model to estimate cortical connectivity from scalp-based EEG recordings. A state equation describes the dynamics of the cortical signals and an observation equation describes the manner in which the cortical signals contribute to the scalp measurements. The state equation is based on a multivariate autoregressive (MVAR) process model for the cortical signals. The observation equation describes the physics relating the cortical signals to the scalp EEG measurements and spatially correlated observation noise. An expectation-maximization (EM) algorithm is employed to obtain maximum-likelihood estimates of the MVAR model parameters. The strength of influence between cortical regions is then derived from the MVAR model parameters. Simulation results show that this integrated approach performs significantly better than the two-step approach in which the cortical signals are first estimated from the EEG measurements by attempting to solve the EEG inverse problem and second, an MVAR model is fit to the estimated signals. The method is also applied to data from a subject watching a movie, and confirms that feedforward connections between visual and parietal cortex are generally stronger than feedback connections.
Starting Page	61
Ending Page	64
File Size	445393
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5335049
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling State estimation Electroencephalography Equations Scalp Signal processing Physics Noise measurement Maximum likelihood estimation Performance evaluation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm

Estimation of Cortical Connectivity From EEG Using State-Space Models

Shrinkage approach for EEG covariance matrix estimation

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies

Smoothness constraint for cortical dipolar sources estimation

Reference estimation in EEG recordings

Dipolar estimates of the cortical map

A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

EEG source localization based on multivariate autoregressive models using Kalman filtering

State-Space Multivariate Autoregressive Models for Estimation of Cortical Connectivity from EEG

Similar Documents

Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm

Estimation of Cortical Connectivity From EEG Using State-Space Models

Shrinkage approach for EEG covariance matrix estimation

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies

Smoothness constraint for cortical dipolar sources estimation

Reference estimation in EEG recordings

Dipolar estimates of the cortical map

A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

EEG source localization based on multivariate autoregressive models using Kalman filtering

State-Space Multivariate Autoregressive Models for Estimation of Cortical Connectivity from EEG