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

Signal Processing;Machine Learning