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Abnormal driver states, particularly have been major concerns for roadsafety, emphasizing the importance of accurate drowsiness detection to preventaccidents. Electroencephalogram (EEG) signals are recognized for theireffectiveness in monitoring a driver's mental state by monitoring brainactivities. However, the challenge lies in the requirement for priorcalibration due to the variation of EEG signals among and within individuals.The necessity of calibration has made the brain-computer interface (BCI) lessaccessible. We propose a practical generalized framework for classifying driverdrowsiness states to improve accessibility and convenience. We separate thenormalization process for each driver, treating them as individual domains. Thegoal of developing a general model is similar to that of domain generalization.The framework considers the statistics of each domain separately since theyvary among domains. We experimented with various normalization methods toenhance the ability to generalize across subjects, i.e. the model'sgeneralization performance of unseen domains. The experiments showed thatapplying individual domain-specific normalization yielded an outstandingimprovement in generalizability. Furthermore, our framework demonstrates thepotential and accessibility by removing the need for calibration in BCIapplications.

Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization