You are not logged in.

Application of extended multivariate modeling for information flow analysis of event related responses

Hettiachchrai, Imali, Mohamed, Shady, Nahavandi, Saeid and Nahavandi, Sofia 2015, Application of extended multivariate modeling for information flow analysis of event related responses, in SMC 2015 : Proceedings of 2015 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 1845-1851, doi: 10.1109/SMC.2015.323.

Attached Files
Name Description MIMEType Size Downloads

Title Application of extended multivariate modeling for information flow analysis of event related responses
Author(s) Hettiachchrai, ImaliORCID iD for Hettiachchrai, Imali orcid.org/0000-0002-4220-0970
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Nahavandi, Sofia
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China))
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Proceedings of 2015 IEEE International Conference on Systems, Man and Cybernetics
Publication date 2015
Series Systems Man and Cybernetics Conference Proceedings
Start page 1845
End page 1851
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Cybernetics
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Effective Connectivity
Granger Causality
MVAR
ERPs
Adaptive Estimation
INDEPENDENT COMPONENT ANALYSIS
PARTIAL DIRECTED COHERENCE
AUTOREGRESSIVE MODELS/
EIGENMODES
PARAMETERS
DYNAMICS
Summary Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.323
Field of Research 090609 Signal Processing
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082430

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 157 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Wed, 23 Mar 2016, 12:32:57 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.