Effects of Task Complexity on Motor Imagery-Based Brain-Computer Interface

Mashat, MEM; Lin, CT; Zhang, D

Effects of Task Complexity on Motor Imagery-Based Brain-Computer Interface

Mashat, MEM Lin, CT

Zhang, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27 (10), pp. 2178 - 2185
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Mashat, MEM	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Zhang, D https://orcid.org/0000-0003-4803-7489	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27 (10), pp. 2178 - 2185	en_US
dc.identifier.issn	1534-4320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139172
dc.description.abstract	© 2001-2011 IEEE. The performance of electroencephalogram (EEG)-based brain-computer interfaces (BCIs) still needs improvements for real world applications. An improvement on BCIs could be achieved by enhancing brain signals from the source via subject intention-based modulation. In this work, we aim to investigate the effects of task complexity on performance of motor imagery (MI) based BCIs. In specific, we studied the effects of motor imagery of a complex task versus a simple task on discriminability of brain activation patterns using EEG. The results show an increase of up to 7.25% in BCI classification accuracy for motor imagery of the complex task in comparison to the simple task. Furthermore, spectral power analysis in low frequency bands, alpha and beta, shows a significant decrease in power value for the complex task. However, high frequency gamma band analysis unveils a significant increase for the complex task. These findings may lead to designing better BCIs with high performance.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Systems and Rehabilitation Engineering	en_US
dc.relation.isbasedon	10.1109/TNSRE.2019.2936987	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Biomedical Engineering	en_US
dc.title	Effects of Task Complexity on Motor Imagery-Based Brain-Computer Interface	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	27	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic 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	closed_access	*
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 2001-2011 IEEE. The performance of electroencephalogram (EEG)-based brain-computer interfaces (BCIs) still needs improvements for real world applications. An improvement on BCIs could be achieved by enhancing brain signals from the source via subject intention-based modulation. In this work, we aim to investigate the effects of task complexity on performance of motor imagery (MI) based BCIs. In specific, we studied the effects of motor imagery of a complex task versus a simple task on discriminability of brain activation patterns using EEG. The results show an increase of up to 7.25% in BCI classification accuracy for motor imagery of the complex task in comparison to the simple task. Furthermore, spectral power analysis in low frequency bands, alpha and beta, shows a significant decrease in power value for the complex task. However, high frequency gamma band analysis unveils a significant increase for the complex task. These findings may lead to designing better BCIs with high performance.

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http://hdl.handle.net/10453/139172