Offline EEGBased Driver Drowsiness Estimation Using Enhanced BatchMode Active Learning (EBMAL) for Regression
Abstract
There are many important regression problems in realworld braincomputer 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 batchmode 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.
 Publication:

arXiv eprints
 Pub Date:
 May 2018
 arXiv:
 arXiv:1805.04737
 Bibcode:
 2018arXiv180504737W
 Keywords:

 Computer Science  Machine Learning;
 Computer Science  HumanComputer Interaction;
 Statistics  Machine Learning