  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.
