Transfer EEG Emotion Recognition by Combining Semi-Supervised Regression with Bipartite Graph Label Propagation

Abstract

Individual differences often appear in electroencephalography (EEG) data collected from different subjects due to its weak, nonstationary and low signal-to-noise ratio properties. This causes many machine learning methods to have poor generalization performance because the independent identically distributed assumption is no longer valid in cross-subject EEG data. To this end, transfer learning has been introduced to alleviate the data distribution difference between subjects. However, most of the existing methods have focused only on domain adaptation and failed to achieve effective collaboration with label estimation. In this paper, an EEG feature transfer method combined with semi-supervised regression and bipartite graph label propagation (TSRBG) is proposed to realize the unified joint optimization of EEG feature distribution alignment and semi-supervised joint label estimation. Through the cross-subject emotion recognition experiments on the SEED-IV data set, the results show that (1) TSRBG has significantly better recognition performance in comparison with the state-of-the-art models; (2) the EEG feature distribution differences between subjects are significantly minimized in the learned shared subspace, indicating the effectiveness of domain adaptation; (3) the key EEG frequency bands and channels for cross-subject EEG emotion recognition are achieved by investigating the learned subspace, which provides more insights into the study of EEG emotion activation patterns.