Domain adversarial learning with multiple adversarial tasks for EEG emotion recognition

Domain adaptation methods using electroencephalography (EEG) play an important role in cross-subject emotion recognition. However, enhancing the generalizability of such models remains challenging. This paper proposes a domain adversarial neural network using multiple adversarial tasks (DANN-MAT). Multiple emotion-unrelated classification tasks are designed to adversarially challenge an emotion classifier, thereby preserving emotion-related features and eliminating irrelevant information. The results demonstrate that employing multiple adversarial tasks improves the generalizability of the developed model, and utilizing more adversarial tasks leads to higher accuracy in cross-subject emotion classification. Our method achieves state-of-the-art results on the SEED and SEED-IV datasets, and the superior stability of the algorithm is proven via parameter comparison experiments. • A domain adversarial neural network using multiple adversarial tasks is proposed. • Multiple adversarial tasks could improve the generation of emotion classifier. • The method improves the performance in cross-subject EEG emotion recognition.