Joint Distribution Adaptation Network for Multi-source Electroencephalogram-based Emotion Recognition

Abstract

Electroencephalogram (EEG)-based emotion recognition is a feasible method to improve human-robot interaction (HRI) systems. However, most existing methods fail to generalize EEG data from existing users or from same user collected on different time, which limits the widespread use of HRI systems. To this end, we propose a joint distribution adaptation network (JDAN) model for multi-source EEG-based emotion recognition. Specifically, A deep neural network is firstly used to extract the deep features of EEG. We then propose two alignment stages: the joint distribution alignment and multi-classifier alignment. The former can reduce the joint feature and label distribution discrepancy between each pair of source and target domains, while the latter reduce the variance in the multi-source distribution. We evaluate our method on the public dataset SEED. Experiments prove that our JDAN outperforms support vector machine and a simple deep neural network on both cross-subject and cross-day settings, demonstrating its effectiveness in tackling multi-source EEG-based emotion recognition.