Extracting patterns of single-trial EEG using an adaptive learning algorithm.

Lin, C-T; Wang, Y-K; Fang, C-N; Yu, Y-H; King, J-T

Extracting patterns of single-trial EEG using an adaptive learning algorithm.

Lin, C-T Wang, Y-K Fang, C-N Yu, Y-H King, J-T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015, 2015, pp. 6642-6645
Issue Date:: 2015-01

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Field	Value	Language
dc.contributor.author	Lin, C-T
dc.contributor.author	Wang, Y-K
dc.contributor.author	Fang, C-N
dc.contributor.author	Yu, Y-H
dc.contributor.author	King, J-T
dc.date	2015-08-25
dc.date.accessioned	2024-03-31T15:37:15Z
dc.date.available	2024-03-31T15:37:15Z
dc.date.issued	2015-01
dc.identifier.citation	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015, 2015, pp. 6642-6645
dc.identifier.isbn	9781424492718
dc.identifier.issn	1557-170X
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/177376
dc.description.abstract	The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.ispartofseries	IEEE Engineering in Medicine and Biology Society Conference Proceedings
dc.relation.isbasedon	10.1109/embc.2015.7319916
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.mesh	Algorithms
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Evoked Potentials
dc.subject.mesh	Humans
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Evoked Potentials
dc.subject.mesh	Algorithms
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Algorithms
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Evoked Potentials
dc.subject.mesh	Humans
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.title	Extracting patterns of single-trial EEG using an adaptive learning algorithm.
dc.type	Conference Proceeding
utslib.citation.volume	2015
utslib.location.activity	Milan, ITALY
utslib.for	0903 Biomedical Engineering
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 Software
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	in_progress	*
pubs.consider-herdc	false
dc.date.updated	2024-03-31T15:37:14Z
pubs.finish-date	2015-08-29
pubs.place-of-publication	Milan, Italy
pubs.publication-status	Published
pubs.start-date	2015-08-25
pubs.volume	2015
dc.location	Milan, Italy

Abstract:

The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.

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http://hdl.handle.net/10453/177376