Objective: Daytime short nap involves individual physiological states including alertness and drowsiness. In order to have a better understanding of the periodical rhymes of physiological states and then promote a good interpretability of alertness, the aim of this study is to detect drowsiness during daytime short nap.
Methods: A method of Bayesian-copula discriminant classifier (BCDC) was introduced to detect individual drowsiness based on the physiological features extracted from electroencephalogram (EEG) signals. As an extension of traditional Bayesian decision theory, the BCDC method tries to construct the class-conditional probability density functions by exploiting the theory of copula and kernel density estimation.
Results: The proposed BCDC method was validated with experimental dataset and compared with other traditional methods for drowsiness detection. The obtained results showed that our method outperformed other methods in terms of three evaluation criteria.
Conclusion: Our proposed method is effective to detect drowsiness with superior performance. Additionally, the BCDC method is relatively robust to different parameter settings on the group-level dataset.
Significance: The proposed method is likely to be a useful tool to improve the correctness of the estimated class-conditional probability density functions. Since features are extracted from spontaneous EEG recordings, the results of this study can be further generalized to other experimental environment to detect vigilance level or driver drowsiness.