Spectral meta-learner for regression (SMLR) model aggregation: Towards calibrationless brain-computer interface (BCI)

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

Spectral meta-learner for regression (SMLR) model aggregation: Towards calibrationless brain-computer interface (BCI)

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. 743 - 749
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. 743 - 749	en_US
dc.identifier.isbn	9781509018970	en_US
dc.identifier.uri	http://hdl.handle.net/10453/113457
dc.description.abstract	© 2016 IEEE. To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.	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.2016.7844330	en_US
dc.title	Spectral meta-learner for regression (SMLR) model aggregation: Towards calibrationless brain-computer interface (BCI)	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.

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http://hdl.handle.net/10453/113457