Self-training maximum classifier discrepancy for EEG emotion recognition

Abstract

Even with an unprecedented breakthrough of deep learning in electroencephalography (EEG), collecting adequate labelled samples is a critical problem due to laborious and time-consuming labelling. Recent study proposed to solve the limited label problem via domain adaptation methods. However, they mainly focus on reducing domain discrepancy without considering task-specific decision boundaries, which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely. A novel self-training maximum classifier discrepancy method for EEG classification is proposed in this study. The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers' outputs. Besides, a self-training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed. Finally, a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network (CNN) is constructed. Extensive experiments on SEED and SEED-IV are conducted. The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.