Electroencephalography (EEG) is an objective tool for emotion recognition
with promising applications. However, the scarcity of labeled data remains a
major challenge in this field, limiting the widespread use of EEG-based emotion
recognition. In this paper, a semi-supervised Dual-stream Self-Attentive
Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed
to tackle the challenge of limited labeled data in cross-subject EEG-based
emotion recognition. The DS-AGC framework includes two parallel streams for
extracting non-structural and structural EEG features. The non-structural
stream incorporates a semi-supervised multi-domain adaptation method to
alleviate distribution discrepancy among labeled source domain, unlabeled
source domain, and unknown target domain. The structural stream develops a
graph contrastive learning method to extract effective graph-based feature
representation from multiple EEG channels in a semi-supervised manner. Further,
a self-attentive fusion module is developed for feature fusion, sample
selection, and emotion recognition, which highlights EEG features more relevant
to emotions and data samples in the labeled source domain that are closer to
the target domain. Extensive experiments conducted on two benchmark databases
(SEED and SEED-IV) using a semi-supervised cross-subject leave-one-subject-out
cross-validation evaluation scheme show that the proposed model outperforms
existing methods under different incomplete label conditions (with an average
improvement of 5.83% on SEED and 6.99% on SEED-IV), demonstrating its
effectiveness in addressing the label scarcity problem in cross-subject
EEG-based emotion recognition.

本文提出了一种半监督的双流自我注意对抗性图对比学习框架 (DS-AGC)，用于解决跨受试者基于脑电图的情绪识别中标记数据不足的挑战，并通过多个实验表明该模型在不完整标签条件下优于现有方法，具有有效解决跨受试者基于脑电图的情绪识别中标签稀缺问题的效果。