WGAN Domain Adaptation for EEG-Based Emotion Recognition

Abstract

In this paper, we propose a novel Wasserstein generative adversarial network domain adaptation (WGANDA) framework for building cross-subject electroencephalography (EEG)-based emotion recognition models. The proposed framework consists of GANs-like components and a two-step training procedure with pre-training and adversarial training. Pre-training is to map source domain and target domain to a common feature space, and adversarial-training is to narrow down the gap between the mappings of the source and target domains on the common feature space. A Wasserstein GAN gradient penalty loss is applied to adversarial-training to guarantee the stability and convergence of the framework. We evaluate the framework on two public EEG datasets for emotion recognition, SEED and DEAP. The experimental results demonstrate that our WGANDA framework successfully handles the domain shift problem in cross-subject EEG-based emotion recognition and significantly outperforms the state-of-the-art domain adaptation methods.