  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.
