Deep Subdomain Adaptation Network Improves Cross-Subject Mental Workload Classification

Cross-subject classification is of great practical value in the mental monitoring. The trained model on a person can be transferred to another person without retraining. To date, it is achieved by global domain adaptation without considering differences in subdomain distributions. In this case, there is a lack of sensitivity to specific information associated with each category. To solve this problem, we proposed a deep subdomain adaptation network (DSAN) to estimate mental workload levels across different persons. In the proposed DSAN, the first temporal and spatial layers were designed as a feature extractor. The features extracted by the feature extractor were aligned between the source samples and target samples in each subdomain separately. The alignment loss calculated by local maximum mean difference (LMMD) was back-propagated to update the weights of the feature extractor to enhance the feature extraction performance. Subdomain adaptation was achieved over iterations during the model training. The proposed subdomain adaptation is not specialized for a particular feature extractor, as shown in this paper. It is universal and can be applied after any feature extractors. Two datasets (Dataset MATB and Dataset SFE) were used to evaluate the proposed DSAN. The results showed that the proposed DSAN outperformed the compared methods in terms of classification accuracy, showing an elevation of 3%~7%. This study provides an effective solution for the cross-subject mental workload classification and will promote practical applications of mental workload monitoring.