Mental stress is a significant factor in the development of a wide variety of psychological, emotional, behavioral, and physical illnesses. It is critical to accurately quantify mental stress, which needs reliable neuroimaging to monitor stress levels. In this work, we used a modified Stroop Color Word Task (SCWT) with time constraints and negative feedback to elicit two distinct degrees of stress in the workplace. We then used salivary alpha amylase (SAA) concurrently with functional near-infrared spectroscopy (fNIRS) to quantify the level of stress. We propose Long Short-Term Memory (LSTM) to decode the two classes of mental stress based on the fNIRS time series. Five-fold cross validation, two LSTM layers, as well as many trainable characteristics were used to construct the network. The induced mental stress increased the level of salivary alpha amylase significantly (p<0.001) by 32.09%. Likewise, we found that LSTM classified mental stress with an average accuracy, sensitivity, and specificity, equal to 72.5%, 72%, and 73% respectively. The findings indicated that the developed LSTM could be used to effectively classify mental stress using fNIRS time series.

This paper investigates binaural beat stimulation (BBs) on mitigating mental stress levels at the workplace. We developed an experimental protocol to induce stress levels by performing Stroop Color-Word Task (SCWT) under time pressure and negative feedback. Then, we mitigated the levels of stress using 16 Hz BBs. The level of stress was assessed by utilizing Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, and behavioral responses. We quantified the level of stress using statistical analysis, functional connectivity based on Phase Locking Value (PLV), and support vector machines (SVM) classifier. We found that BBs has significantly improved the accuracy of target detection by 27.35 %, (p<0.005) and reduced the cortisol level. The classification results showed that the SVM technique with PLV features differentiates between three levels of mental states (control, stress and mitigation) with an average accuracy of 65.22%, and sensitivity of 81.79% and specificity of 80.10%.