Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns
This provides an objective method for analyzing interpersonal dynamics in contexts like team building and therapy, though it is incremental as it builds on existing EEG and deep learning techniques.
The authors tackled the problem of objectively quantifying interpersonal relationships by proposing a framework that uses dyadic EEG signals to identify social relationship types, achieving effective identification of strangers versus friends.
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.