The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and application bottleneck. To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG samples in a group for generating augmented groups by pairing, cross exchanging, and separating. And the model adopts a group projector to extract group-level feature representations from group EEG samples triggered by the same emotion video stimuli. Then contrastive learning is employed to maximize the similarity of group-level representations of augmented groups with the same stimuli. The SGMC achieves the state-of-the-art emotion recognition results on the publicly available DEAP dataset with an accuracy of 94.72% and 95.68% in valence and arousal dimensions, and also reaches competitive performance on the public SEED dataset with an accuracy of 94.04%. It is worthy of noting that the SGMC shows significant performance even when using limited labels. Moreover, the results of feature visualization suggest that the model might have learned video-level emotion-related feature representations to improve emotion recognition. And the effects of group size are further evaluated in the hyper parametric analysis. Finally, a control experiment and ablation study are carried out to examine the rationality of architecture. The code is provided publicly online.
翻译:近些年来,基于EEG的情感认知的进展在人体机器互动和认知科学领域得到了广泛的关注。然而,如何识别带有有限标签的情绪已成为一个新的研究和应用程序瓶颈。为了解决这个问题,本文件建议基于人性刺激性一致的EEG信号,建立自我监督的集团Meisdo对比学习框架(SGMC SGMC )。在SGMC中,开发了一种创新的基因激励数据增强方法,名为Meisisis。它利用了将EEEG样本在一组中进行比对以配对、交叉交换和分离的方式生成扩大组群的比喻的比喻的比喻,从而形成了一个新的。模型利用了94.72%和95.68%的直观效果生成组。模型采用了一组投影仪,从EEEG样本中提取了组的群级特征展示。随后,对比性学习用的是尽量扩大群体在群体中的比喻。SGMC在公开评估时,其直观度和直观度的比值分析中,它达到了94.72%和95.68%的比值的比值。SEA的比值。Salalalalalal的比值分析还展示了一种显著的比值。C的比值。SL的比值的比值的比值分析还显示的比值。SL的比值的比值的比值的比值的比值,还显示的比值的比值。C的比值的比值的比值的比值的比值的比值的比值的比值的比值的比值比值是展示了比值的比值的比值的比值的比值的比值的比值的比值的比值的比值的比值的比值。