Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains challenging. In this paper, we propose a novel social relationship analysis framework using spatio-temporal patterns derived from dyadic EEG signals, which can be applied to quantitatively measure team cooperation in corporate team building, and evaluate interpersonal dynamics between therapists and patients in psychiatric therapy. First, we constructed a dyadic-EEG dataset from 72 pairs of participants with two relationships (stranger or friend) when watching emotional videos simultaneously. Then we proposed a deep neural network on dyadic-subject EEG signals, in which we combine the dynamic graph convolutional neural network for characterizing the interpersonal relationships among the EEG channels and 1-dimension convolution for extracting the information from the time sequence. To obtain the feature vectors from two EEG recordings that well represent the relationship of two subjects, we integrate deep canonical correlation analysis and triplet loss for training the network. Experimental results show that the social relationship type (stranger or friend) between two individuals can be effectively identified through their EEG data.

翻译：人际关系质量在社交和职业环境中至关重要。现有人际关系分析大多依赖主观自我报告，而客观量化仍具挑战性。本文提出一种基于双人脑电图信号时空模式的新型社会关系分析框架，可应用于企业团队建设中的团队合作量化评估，以及精神治疗中治疗师与患者间人际动态的评估。首先，我们构建了包含72对参与者（陌生人或朋友两种关系）在同步观看情感视频时的双人脑电图数据集。随后提出针对双人脑电图信号的深度神经网络，该网络融合动态图卷积神经网络表征脑电图通道间的人际关系，并采用一维卷积提取时间序列信息。为从两个脑电图记录中获取良好表征被试关系的特征向量，我们整合深度典型相关分析与三元组损失函数进行网络训练。实验结果表明，通过脑电图数据可有效识别两人之间的社会关系类型（陌生或朋友）。