AdaptEEG: A Deep Subdomain Adaptation Network With Class Confusion Loss for Cross-Subject Mental Workload Classification

EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation techniques have shown effectiveness in enhancing the accuracy of cross-subject classification. However, current state-of-the-art methods for cross-subject mental workload classification primarily focus on global domain adaptation, which may lack fine-grained information and result in ambiguous classification boundaries. We proposed a novel approach called deep subdomain adaptation network with class confusion loss (DSAN-CCL) to enhance the performance of cross-subject mental workload classification. DSAN-CCL utilizes the local maximum mean discrepancy to align the feature distributions between the source domain and the target domain for each mental workload category. Moreover, the class confusion matrix was constructed by the product of the weighted class probabilities (class probabilities predicted by the label classifier) and the transpose of the class probabilities. The loss for maximizing diagonal elements and minimizing non-diagonal elements of the class confusion matrix was added to increase the credibility of pseudo-labels, thus improving the transfer performance. The proposed DSAN-CCL method was validated on two datasets, and the results indicate a significant improvement of 3 similar to 10 percentage points compared to state-of-the-art domain adaptation methods. In addition, our proposed method is not dependent on a specific feature extractor. It can be replaced by any other feature extractor to fit new applications. This makes our approach universal to cross-domain classification problems.