Improving Generalization of Drowsiness State Classification by
  Domain-Specific Normalization
Dong-Young Kim Dong-Kyun Han Seo-Hyeon Park Geun-Deok Jang Seong-Whan Lee
Abnormal driver states, particularly have been major concerns for road
safety, emphasizing the importance of accurate drowsiness detection to prevent
accidents. Electroencephalogram (EEG) signals are recognized for their
effectiveness in monitoring a driver's mental state by monitoring brain
activities. However, the challenge lies in the requirement for prior
calibration due to the variation of EEG signals among and within individuals.
The necessity of calibration has made the brain-computer interface (BCI) less
accessible. We propose a practical generalized framework for classifying driver
drowsiness states to improve accessibility and convenience. We separate the
normalization process for each driver, treating them as individual domains. The
goal of developing a general model is similar to that of domain generalization.
The framework considers the statistics of each domain separately since they
vary among domains. We experimented with various normalization methods to
enhance the ability to generalize across subjects, i.e. the model's
generalization performance of unseen domains. The experiments showed that
applying individual domain-specific normalization yielded an outstanding
improvement in generalizability. Furthermore, our framework demonstrates the
potential and accessibility by removing the need for calibration in BCI
applications.