Wasserstein-Distance-Based Multi-Source Adversarial Domain Adaptation for Emotion Recognition and Vigilance Estimation

To build a subject-independent affective model based on electroencephalography (EEG) is a challenging task due to the domain shift problem caused by individual differences in EEG data. In this paper, we prove a new generalization bound based on Wasserstein distance for multi-source classification and regression problems. Based on our bound, we propose two novel Wasserstein-distance-based multi-source adversarial domain adaptation methods (wMADA) for learning domain invariant and task discriminative domain mappings by dynamically aligning different domain mappings. We evaluate our methods on two typical EEG datasets. The experimental results demonstrate that our wMADA methods successfully handle the multi-source domain shift problem in creating subject-independent affective models and outperform the state-of-the-art domain adaptation methods.