標題: Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
作者: Wu, Dongrui
Lawhern, Vernon J.
Gordon, Stephen
Lance, Brent J.
Lin, Chin-Teng
腦科學研究中心
Brain Research Center
關鍵字: Active learning;brain-computer interface (BCI);drowsy driving;EEG;linear regression
公開日期: 1-Jan-2016
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.
URI: http://hdl.handle.net/11536/146598
ISSN: 1062-922X
期刊: 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
起始頁: 730
結束頁: 736
Appears in Collections:Conferences Paper