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Electroencephalogram (EEG) emotion recognition has become a research focus in the field of human-computer interaction (HCI). However, the process of EEG signal collection requires lots of expertise, which makes the amount of labeled EEG data very limited. It constrains the performance of supervised methods which require large amounts of annotated data in some sense. Self-supervised learning paradigm, which aims to train models that do not require any labeled samples can make full use of a large amount of unlabeled EEG samples. But a drawback is that they fall short of learning class discriminative sample representations since no labeled information is utilized during training. To solve the above problem, we propose a semi-supervised model, named consistency regularization enhanced graph attention network (CR-GAT) for EEG emotion recognition. The CR-GAT mainly consists of three modules, namely the feature extraction and fusion (FEF) module, the feature graph building and augment (GBA) module as well as the consistency regularization (CR) module. Specifically, t he F EFm odule is to extract task-specific EEG features and highlight the most valuable features from the EEG signals. The GBA module is to build a sample-related graph representation of the EEG feature set. The CR module, which draws support samples from labeled samples and anchor samples from the entire sample set, intends to minimize the difference between the predicted class distributions from different graphs constructed by multi-views of the sample set to push samples that belong to the same class to be grouped together. We conduct our experiment on three real-world datasets, the experimental results show the method surpasses most of competitive models.
Liu et al. (Tue,) studied this question.