  Drowsy driving is pervasive, and also a major cause of traffic accidents.
Estimating a driver's drowsiness level by monitoring the electroencephalogram
(EEG) signal and taking preventative actions accordingly may improve driving
safety. However, individual differences among different drivers make this task
very challenging. A calibration session is usually required to collect some
subject-specific data and tune the model parameters before applying it to a new
subject, which is very inconvenient and not user-friendly. Many approaches have
been proposed to reduce the calibration effort, but few can completely
eliminate it. This paper proposes a novel approach, feature weighted episodic
training (FWET), to completely eliminate the calibration requirement. It
integrates two techniques: feature weighting to learn the importance of
different features, and episodic training for domain generalization.
Experiments on EEG-based driver drowsiness estimation demonstrated that both
feature weighting and episodic training are effective, and their integration
can further improve the generalization performance. FWET does not need any
labelled or unlabelled calibration data from the new subject, and hence could
be very useful in plug-and-play brain-computer interfaces.
