Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression

Dongrui Wu, Lawhern Vernon J., Stephen Gordon, Lance Brent J, Chin-Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

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.
Original languageEnglish
Title of host publication2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
PublisherIEEE
Pages730-736
Number of pages7
ISBN (Print)978-1-5090-1897-0
DOIs
StatePublished - 2016

Publication series

NameIEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN (Print)1062-922X

Keywords

  • Active learning
  • brain-computer interface (BCI)
  • EEG
  • drowsy driving
  • linear regression

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