Subject-Independent ERP-Based Brain-Computer Interfaces

Vo, K; Pham, T; Nguyen, DN; Kha, HH; Dutkiewicz, E

Subject-Independent ERP-Based Brain-Computer Interfaces

Vo, K Pham, T

Nguyen, DN

Kha, HH Dutkiewicz, E

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (4), pp. 719 - 728
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Vo, K	en_US
dc.contributor.author	Pham, T https://orcid.org/0000-0002-7153-0135	en_US
dc.contributor.author	Nguyen, DN https://orcid.org/0000-0003-2659-8648	en_US
dc.contributor.author	Kha, HH	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (4), pp. 719 - 728	en_US
dc.identifier.issn	1534-4320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120128
dc.description.abstract	© 2001-2011 IEEE. Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Systems and Rehabilitation Engineering	en_US
dc.relation.isbasedon	10.1109/TNSRE.2018.2810332	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Communication Aids for Disabled	en_US
dc.subject.mesh	Learning	en_US
dc.subject.mesh	Event-Related Potentials, P300	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.subject.mesh	Brain-Computer Interfaces	en_US
dc.title	Subject-Independent ERP-Based Brain-Computer Interfaces	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	26	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2001-2011 IEEE. Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120128