Mapping information flow of independent source to predict conscious level: A granger causality based brain-computer interface

Chuang, CH; Huang, CS; Lin, CT; Ko, LW; Chang, JY; Yang, JM

Mapping information flow of independent source to predict conscious level: A granger causality based brain-computer interface

Chuang, CH

Huang, CS Lin, CT

Ko, LW Chang, JY Yang, JM

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2012 International Symposium on Computer, Consumer and Control, IS3C 2012, 2012, pp. 813 - 816
Issue Date:: 2012-07-30

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Field	Value	Language
dc.contributor.author	Chuang, CH https://orcid.org/0000-0002-5043-8380	en_US
dc.contributor.author	Huang, CS	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Chang, JY	en_US
dc.contributor.author	Yang, JM	en_US
dc.date.issued	2012-07-30	en_US
dc.identifier.citation	Proceedings - 2012 International Symposium on Computer, Consumer and Control, IS3C 2012, 2012, pp. 813 - 816	en_US
dc.identifier.isbn	9780769546551	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121168
dc.description.abstract	Recent studies have shown that the various brain networks over different cognitive states. In contrast to measure a physiological change over a single region, the information flows between brain regions described by effective connectivity provides an informative dynamic over the whole brain. In this study, we proposed a source information flow network based on the combination of Granger causality and support vector regression to predict driver's conscious level. This work provides the first application of using brain network to develop a brain-computer interface and obtain a sound result of performance. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2012 International Symposium on Computer, Consumer and Control, IS3C 2012	en_US
dc.relation.isbasedon	10.1109/IS3C.2012.209	en_US
dc.title	Mapping information flow of independent source to predict conscious level: A granger causality based brain-computer interface	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recent studies have shown that the various brain networks over different cognitive states. In contrast to measure a physiological change over a single region, the information flows between brain regions described by effective connectivity provides an informative dynamic over the whole brain. In this study, we proposed a source information flow network based on the combination of Granger causality and support vector regression to predict driver's conscious level. This work provides the first application of using brain network to develop a brain-computer interface and obtain a sound result of performance. © 2012 IEEE.

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http://hdl.handle.net/10453/121168