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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 drivingsafety. However, individual differences among different drivers make this taskvery challenging. A calibration session is usually required to collect somesubject-specific data and tune the model parameters before applying it to a newsubject, which is very inconvenient and not user-friendly. Many approaches havebeen proposed to reduce the calibration effort, but few can completelyeliminate it. This paper proposes a novel approach, feature weighted episodictraining (FWET), to completely eliminate the calibration requirement. Itintegrates two techniques: feature weighting to learn the importance ofdifferent features, and episodic training for domain generalization.Experiments on EEG-based driver drowsiness estimation demonstrated that bothfeature weighting and episodic training are effective, and their integrationcan further improve the generalization performance. FWET does not need anylabelled or unlabelled calibration data from the new subject, and hence couldbe very useful in plug-and-play brain-computer interfaces.

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training