Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression

Wu, D; Lawhern, VJ; Gordon, S; Lance, BJ; Lin, CT

Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression

Wu, D Lawhern, VJ Gordon, S Lance, BJ Lin, CT

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 730 - 736
Issue Date:: 2017-02-06

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Field	Value	Language
dc.contributor.author	Wu, D	en_US
dc.contributor.author	Lawhern, VJ	en_US
dc.contributor.author	Gordon, S	en_US
dc.contributor.author	Lance, BJ	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2017-02-06	en_US
dc.identifier.citation	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 730 - 736	en_US
dc.identifier.isbn	9781509018970	en_US
dc.identifier.uri	http://hdl.handle.net/10453/113460
dc.description.abstract	© 2016 IEEE. There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.	en_US
dc.relation.ispartof	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/SMC.2016.7844328	en_US
dc.title	Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression	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:

© 2016 IEEE. There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.

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http://hdl.handle.net/10453/113460