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dc.contributor.authorWu, Dongruien_US
dc.contributor.authorLawhern, Vernon J.en_US
dc.contributor.authorGordon, Stephenen_US
dc.contributor.authorLance, Brent J.en_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2018-08-21T05:56:45Z-
dc.date.available2018-08-21T05:56:45Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn1062-922Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/146598-
dc.description.abstractThere 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.language.isoen_USen_US
dc.subjectActive learningen_US
dc.subjectbrain-computer interface (BCI)en_US
dc.subjectdrowsy drivingen_US
dc.subjectEEGen_US
dc.subjectlinear regressionen_US
dc.titleOffline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regressionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)en_US
dc.citation.spage730en_US
dc.citation.epage736en_US
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000402634700112en_US
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