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Stress is known as one of the major factors threatening human health. A largenumber of studies have been performed in order to either assess or relievestress by analyzing the brain and heart-related signals. In this study, signalsproduced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recordedfrom 10 healthy volunteers are employed to assess the stress induced by theMontreal Imaging Stress Task by means of a deep learning system. The proposeddeep learning system consists of two main parts: First, the one-dimensionalconvolutional neural network is employed to build informative feature maps.Then, a stack of deep fully connected layers is used to predict the stressexistence probability. Experiment results showed that the trained fNIRS modelperforms stress classification by achieving 88.52 -+ 0.77% accuracy. Employmentof the proposed deep learning system trained on the fNIRS measurements leads tohigher stress classification accuracy than the existing methods proposed infNIRS studies in which the same experimental procedure has been employed. Theproposed method suggests better stability with lower variation in prediction.Furthermore, its low computational cost opens up the possibility to be appliedin real-time stress assessment.

Accurate Stress Assessment based on functional Near Infrared Spectroscopy using Deep Learning Approach