%J IEEE Access
%L scholars13690
%I Institute of Electrical and Electronics Engineers Inc.
%A E. Alyan
%A N.M. Saad
%A N. Kamel
%A M.A. Rahman
%P 218911-218923
%X This research seeks to examine the impact of workstation types on the coupling of neural and vascular activities of the prefrontal cortex (PFC). The design of the workstations was found to impair the performance, physical and mental health of employees. However, the mechanism underlying cognitive activity involved during workstation design-related stress effects in the PFC has not been fully understood. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to simultaneously measure electrical activity and hemoglobin concentration changes in the PFC. The multimodal signal was collected from 23 healthy adult volunteers who completed the Montreal imaging stress task in ergonomic and non-ergonomic workstations. A supervised machine learning method based on temporally embedded canonical correlation analysis (tCCA) was utilized to obtain the association between neural activity and local changes in hemoglobin concentrations to enhance localization and accuracy. The results showed deactivation in alpha power rhythms and oxygenated hemoglobin, as well as declined activation pattern of the fused data in the right PFC at the non-ergonomic workstation. Additionally, all participants at the non-ergonomic workstation experienced a substantial rise in salivary alpha-amylase activity in comparison with the ergonomic workstation, indicating the existence of high-stress levels. The proposed tCCA approach obtains excellent results in discriminating workstation types achieving accuracies of 98.8 and a significant improvement of 8.0 ( p < 0.0001 ) and 9.4 ( p < 0.0001 ) over EEG-only and fNIRS-only, respectively. Our study suggests the use of functional neuroimaging in designing the workplace as it provides critical information on the causes of workplace-related stress. © 2013 IEEE.
%K Electroencephalography; Electrophysiology; Ergonomics; Functional neuroimaging; Hemoglobin; Infrared devices; Learning systems; Near infrared spectroscopy; Neurons; Supervised learning, Canonical correlation analysis; Electrical activities; Functional near-infrared spectroscopy (fnirs); Hemoglobin concentration; Neuro-vascular coupling; Oxygenated hemoglobin; Salivary alpha amylase; Supervised machine learning, Occupational risks
%V 8
%T Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress
%O cited By 8
%D 2020
%R 10.1109/ACCESS.2020.3040540