EEG emotion Enhancement using Task-specific Domain Adversarial Neural Network

Electroencephalogram (EEG) signal has been widely applied in detecting human emotion. Individual differences limit the generalization in cross-subject classification since the release of emotion will be definitely different across persons even with the same emotion stimuli. Previous research utilizes domain adaptation to solve this problem in a leave-one-subject-out training that is learning common emotion-related features from many subjects and testing on a new subject, which requires abundant labeled data. This paper proposed a novel one-to-one domain adaptation method, the Task-specific Domain Adversarial Neural Network (T-DANN) which transfers knowledge from either one subject to predict on another subject or knowledge from one phase to predict on another phase within the same subject. Therefore, T-DANN is more flexible and requires much less data during training. T-DANN is an adversarial training method which adapts the conditional distribution between domains and adapts classification boundaries between classes simultaneously. Compared with data from different subjects, phase data from the same subject has much deeper correlation thus enhances the prediction of emotion in new phase. We evaluated our method on EEG emotion benchmark dataset SEED. The experiments showed that our proposed method outperformed other baseline methods in cross-subject adaptation. By cross-phase adaptation, our method achieved accuracy that approximately 3% lower than the state-of-the-art method but only used 1/14 labeled data, indicating the priority of our proposed method in real-time application.