Analysing and Inferring of Intimacy Based on fNIRS Signals and Peripheral Physiological Signals

摘要

Intimacy refers to a relatively long-lasting affinity relationship between individuals, which involves complex neuronal activities and physiological changes in the body. Recent advancements in the field of neuroimaging have demonstrated that functional near-infrared spectroscopy (fNIRS) has excellent potential for intimate relationship analysis. Signals such as fNIRS and physiological signals are increasingly utilised in this regard due to their consistency and complementarity. In this paper, first, we apply fNIRS and physiological database collected from 26 subjects when viewing lover, friend and stranger pictures to analyse and infer the intimacy. Then, the time domain information from both the fNIRS and physiological signals are utilised to exploit the representation of intimacy by General Linear Model (GLM) and Complex Brain Network Analysis (CBNA) methods. Based on these two methods, the intimacy can be analysed with different brain activation patterns. Finally, different machine learning techniques are utilised to predict the intimate relationship. The results demonstrate that multi-modal features are more efficient for intimacy research. Moreover, the average classification accuracy of ensemble learning is 98.72% whereas for KNN it is 91.03%.