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

研究成果: Conference contribution同行評審

21 引文 斯高帕斯(Scopus)

摘要

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.
原文English
主出版物標題2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
發行者IEEE
頁面730-736
頁數7
ISBN(列印)978-1-5090-1897-0
DOIs
出版狀態Published - 2016

出版系列

名字IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN(列印)1062-922X

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