SUBJECT-INVARIANT EEG REPRESENTATION LEARNING FOR EMOTION RECOGNITION

The discrepancies between the distributions of the train and test data, a.k.a., domain shift, result in lower generalization for emotion recognition methods. One of the main factors contributing to these discrepancies is human variability. Domain adaptation methods are developed to alleviate the problem of domain shift, however, these techniques while reducing between database variations fail to reduce between-subject variability. In this paper, we propose an adversarial deep domain adaptation approach for emotion recognition from electroencephalogram (EEG) signals. The method jointly learns a new representation that minimizes emotion recognition loss and maximizes subject confusion loss. We demonstrate that the proposed representation can improve emotion recognition performance within and across databases.