Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
This work addresses the challenge of efficient labeling in offline regression for brain-computer interface applications like driver drowsiness monitoring, representing an incremental improvement over existing active learning methods.
The paper tackled the problem of selecting a small number of unlabeled EEG epochs for labeling to build an accurate regression model for driver drowsiness estimation, proposing an enhanced batch-mode active learning approach that improved regression performance, though specific numerical gains were not detailed in the 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.