  Fatigue is the most vital factor of road fatalities and one manifestation of
fatigue during driving is drowsiness. In this paper, we propose using deep
Q-learning to analyze an electroencephalogram (EEG) dataset captured during a
simulated endurance driving test. By measuring the correlation between
drowsiness and driving performance, this experiment represents an important
brain-computer interface (BCI) paradigm especially from an application
perspective. We adapt the terminologies in the driving test to fit the
reinforcement learning framework, thus formulate the drowsiness estimation
problem as an optimization of a Q-learning task. By referring to the latest
deep Q-Learning technologies and attending to the characteristics of EEG data,
we tailor a deep Q-network for action proposition that can indirectly estimate
drowsiness. Our results show that the trained model can trace the variations of
mind state in a satisfactory way against the testing EEG data, which
demonstrates the feasibility and practicability of this new computation
paradigm. We also show that our method outperforms the supervised learning
counterpart and is superior for real applications. To the best of our
knowledge, we are the first to introduce the deep reinforcement learning method
to this BCI scenario, and our method can be potentially generalized to other
BCI cases.
