基于学习时空模式的成对脑电信号人际关系分析
Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns
AI总结：
本文提出一种基于成对脑电信号时空模式的社交关系分析框架，构建了72对参与者的数据集，结合动态图卷积网络、一维卷积、深度典型相关分析与三元组损失训练深度神经网络，有效识别了个体间陌生人或朋友的关系类型。
摘要：
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，客观量化仍具挑战性。本文提出一种利用成对脑电信号时空模式的新型社交关系分析框架，可定量测量企业团队建设中的团队合作，并评估精神治疗中治疗师与患者间的人际互动。首先，我们构建了一个成对脑电数据集，包含72对参与者在同时观看情感视频时的两种关系（陌生人或朋友）。随后，我们提出一种处理成对受试者脑电信号的深度神经网络，结合动态图卷积神经网络表征脑电通道间的人际关系，以及一维卷积提取时间序列信息。为从两份脑电记录中获得良好表征两名受试者关系的特征向量，我们整合深度典型相关分析和三元组损失来训练网络。实验结果表明，两名个体间的社交关系类型（陌生人或朋友）可通过其脑电数据有效识别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
