Exploring Spatial-Temporal Representations for fNIRS-based Intimacy Detection via an Attention-enhanced Cascade Convolutional Recurrent Neural Network

The detection of intimacy plays a crucial role in the improvement of intimate relationship, which contributes to promote the family and social harmony. Previous studies have shown that different degrees of intimacy have significant differences in brain imaging. Recently, work has emerged to recognise intimacy automatically by using machine learning techniques. Moreover, considering the temporal dynamic characteristics of intimacy relationship on neural mechanism, how to model spatiotemporal dynamics for intimacy prediction effectively is still a challenge. In this paper, we propose a novel method to explore deep spatial-temporal representations for intimacy prediction by an
Attention-enhanced
Cascade
Convolutional
Recurrent
Neural
Network
(ACCRNN). Given the advantages of time-frequency resolution in complex neuronal activities analysis, this paper utilizes
functional
near-infrared
spectroscopy
(fNIRS) to analyse and infer intimate relationship. We collected fNIRS-based dataset for the analysis of intimate relationship. Forty-two-channel fNIRS signals are recorded from the 44 subjects' prefrontal cortex when they watched a total of 18 photos of lovers, friends and strangers for 30 seconds per photo. The experimental results show that our proposed method outperforms the others in terms of accuracy with the precision of 96.5%. To the best of our knowledge, this is the first time that such a hybrid deep architecture has been employed for fNIRS-based intimacy prediction.