TY - GEN
T1 - Stress Assessment and Mitigation using fNIRS and Binaural Beat Stimulation
AU - Al-Shargie, Fares
AU - Katmah, Rateb
AU - Tariq, Usman
AU - Babiloni, Fabio
AU - Al-Mughairbi, Fadwa
AU - Al-Nashash, Hasan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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%.
AB - 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%.
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U2 - 10.1109/SMC52423.2021.9658623
DO - 10.1109/SMC52423.2021.9658623
M3 - Conference contribution
AN - SCOPUS:85124317867
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2678
EP - 2683
BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Y2 - 17 October 2021 through 20 October 2021
ER -