Detecting neural changes during stress and fatigue effectively: A comparison of spectral analysis and sample entropy

Tran, Y; Thuraisingham, RA; Wijesuriya, N; Nguyen, HT; Craig, A

Detecting neural changes during stress and fatigue effectively: A comparison of spectral analysis and sample entropy

Tran, Y

Thuraisingham, RA Wijesuriya, N Nguyen, HT

Craig, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, 2007, pp. 350 - 353
Issue Date:: 2007-09-25

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Field	Value	Language
dc.contributor.author	Tran, Y https://orcid.org/0000-0002-1741-4205	en_US
dc.contributor.author	Thuraisingham, RA	en_US
dc.contributor.author	Wijesuriya, N	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Craig, A	en_US
dc.date.issued	2007-09-25	en_US
dc.identifier.citation	Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, 2007, pp. 350 - 353	en_US
dc.identifier.isbn	1424407923	en_US
dc.identifier.isbn	9781424407927	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2820
dc.description.abstract	Brain computer interface (BCI) technology as Its name implies, relies upon decoding brain signals into operational commands. Aside from needing effective means of control, successful BCIs need to remain stable in varying physiological conditions. BCIs need to be developed with mechanisms to recognise and respond to physiological states (such as stress and fatigue) that can disrupt user capability. This paper compares a spectral analysis of EEG signals technique with a nonlinear method of sample entropy to detect changes In brain dynamics during moments of stress and fatigue. The results demonstrated few changes In the spectral frequency bands of the EEG during fatigue and stress conditions. However, when the EEG signals were analysed with the nonlinear technique of sample entropy the results indicated a reduction of complexity during moments of fatigue and stress and an increase In complexity during moments of engagement to the task. © 2007 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0666942
dc.relation.ispartof	Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering	en_US
dc.relation.isbasedon	10.1109/CNE.2007.369682	en_US
dc.title	Detecting neural changes during stress and fatigue effectively: A comparison of spectral analysis and sample entropy	en_US
dc.type	Conference Proceeding
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	Kohala Coast, Hawaii, USA	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 Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Brain computer interface (BCI) technology as Its name implies, relies upon decoding brain signals into operational commands. Aside from needing effective means of control, successful BCIs need to remain stable in varying physiological conditions. BCIs need to be developed with mechanisms to recognise and respond to physiological states (such as stress and fatigue) that can disrupt user capability. This paper compares a spectral analysis of EEG signals technique with a nonlinear method of sample entropy to detect changes In brain dynamics during moments of stress and fatigue. The results demonstrated few changes In the spectral frequency bands of the EEG during fatigue and stress conditions. However, when the EEG signals were analysed with the nonlinear technique of sample entropy the results indicated a reduction of complexity during moments of fatigue and stress and an increase In complexity during moments of engagement to the task. © 2007 IEEE.

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http://hdl.handle.net/10453/2820