Influence of EEG tonic changes on Motor Imagery performance

Nascimben, M; Wang, YK; Singh, AK; King, JT; Lin, CT

Influence of EEG tonic changes on Motor Imagery performance

Nascimben, M Wang, YK

Singh, AK

King, JT Lin, CT

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Publication Type:: Conference Proceeding
Citation:: International IEEE/EMBS Conference on Neural Engineering, NER, 2017, pp. 46 - 49
Issue Date:: 2017-08-10

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Field	Value	Language
dc.contributor.author	Nascimben, M	en_US
dc.contributor.author	Wang, YK https://orcid.org/0000-0001-8390-2664	en_US
dc.contributor.author	Singh, AK https://orcid.org/0000-0002-6539-9695	en_US
dc.contributor.author	King, JT	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.available	2020-05-25T19:10:40Z
dc.date.issued	2017-08-10	en_US
dc.identifier.citation	International IEEE/EMBS Conference on Neural Engineering, NER, 2017, pp. 46 - 49	en_US
dc.identifier.isbn	9781538619162	en_US
dc.identifier.issn	1948-3546	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125683
dc.description.abstract	© 2017 IEEE. In Motor Imagery literature, performance predictors are commonly divided in four categories: personal, psychological, anatomical and neurophysiological. However these predictors are limited to inter-subjects changes. To overcome this limitation and evaluate intra-subjects performance, we tried to combine two groups of these measures: psychological and neurophysiological. As neurophysiological variables tonic changes in resting EEG theta and alpha sub-bands were considered. As psychological parameter we analyzed internalized attention and its correlates in lower alpha. We found that when internalized attention doesn't decrease, Motor Imagery performance outcome can be correctly predicted by resting EEG tonic variations.	en_US
dc.relation.ispartof	International IEEE/EMBS Conference on Neural Engineering, NER	en_US
dc.relation.isbasedon	10.1109/NER.2017.8008288	en_US
dc.title	Influence of EEG tonic changes on Motor Imagery performance	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	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	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. In Motor Imagery literature, performance predictors are commonly divided in four categories: personal, psychological, anatomical and neurophysiological. However these predictors are limited to inter-subjects changes. To overcome this limitation and evaluate intra-subjects performance, we tried to combine two groups of these measures: psychological and neurophysiological. As neurophysiological variables tonic changes in resting EEG theta and alpha sub-bands were considered. As psychological parameter we analyzed internalized attention and its correlates in lower alpha. We found that when internalized attention doesn't decrease, Motor Imagery performance outcome can be correctly predicted by resting EEG tonic variations.

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http://hdl.handle.net/10453/125683