Sleep Quality Estimation with Adversarial Domain Adaptation: From Laboratory to Real Scenario

Abstract

Previous studies on EEG-based last-night sleep quality estimation mainly focus on evaluation with data from laboratory experiments. However, due to the reality gap con- stituted with device performance, subject groups, experiment settings and controlled conditions, the models trained solely on laboratory data cannot generalize well to real scenarios. In this work, we investigate the sleep quality estimation for high-speed train drivers as an instance of real-scenario application. Domain adaptation models are adopted to deal with the individual differences across subjects when modeling and testing with the real-scenario data. As it is usually difficult and costly to acquire data and annotate them in real scenarios, the high-quality data in laboratory conditions are used for model trainings. Knowledge from simulation is transferred to reality with domain adaptation methods. A novel approach called Domain Adversarial Neural Network (DANN) is adopted. DANN learns domain independent features through deep networks with an adversarial architecture. The experimental results indicate that DANN outperforms other state-of-the-art methods and achieves 19.55% and 23.50% im- provements in terms of accuracy on the cross-subject and cross- scenario tasks, respectively, in comparison with the baseline SVM model.