  Electroencephalography (EEG) is an objective tool for emotion recognition and
shows promising performance. However, the label scarcity problem is a main
challenge in this field, which limits the wide application of EEG-based emotion
recognition. In this paper, we propose a novel semi-supervised learning
framework (EEGMatch) to leverage both labeled and unlabeled EEG data. First, an
EEG-Mixup based data augmentation method is developed to generate more valid
samples for model learning. Second, a semi-supervised two-step pairwise
learning method is proposed to bridge prototype-wise and instance-wise pairwise
learning, where the prototype-wise pairwise learning measures the global
relationship between EEG data and the prototypical representation of each
emotion class and the instance-wise pairwise learning captures the local
intrinsic relationship among EEG data. Third, a semi-supervised multi-domain
adaptation is introduced to align the data representation among multiple
domains (labeled source domain, unlabeled source domain, and target domain),
where the distribution mismatch is alleviated. Extensive experiments are
conducted on two benchmark databases (SEED and SEED-IV) under a cross-subject
leave-one-subject-out cross-validation evaluation protocol. The results show
the proposed EEGmatch performs better than the state-of-the-art methods under
different incomplete label conditions (with 6.89% improvement on SEED and 1.44%
improvement on SEED-IV), which demonstrates the effectiveness of the proposed
EEGMatch in dealing with the label scarcity problem in emotion recognition
using EEG signals. The source code is available at
https://github.com/KAZABANA/EEGMatch.
