Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence

While different neuroimaging modalities have been proposed to detect mental stress, each modality experiences certain limitations. This study proposed novel approaches to detect stress based on fusion of EEG and fNIRS signals in the feature-level using joint independent component analysis (jICA) and canonical correlation analysis method (CCA) and predected the level of stress using machine-learning approach. The jICA and CCA were then developed to combine the features to detect mental stress. The jICA fusion scheme discovers relationships between modalities by utilizing ICA to identify sources from each modality that modulate in the same way across subjects. The CCA fuse information from two sets of features to discover the associations across modalities and to ultimately estimate the sources responsible for these associations. The study further explored the functional connectivity (FC) and evaluated the performance of the fusion methods based on their classification performance and compared it with the result obtained by each individual modality. The jICA fusion technique significantly improved the classification accuracy, sensitivity and specificity on average by +3.46% compared to the EEG and +11.13% compared to the fNIRS. Similarly, CCA method improved the classification accuracy, sensitivity and specificity on average by +8.56% compared to the EEG and +13.03% compared to the fNIRS, respectively. The overall performance of the proposed fusion methods significantly improved the detection rate of mental stress, p<0.05. The FC significantly reduced under stress and suggested EEG and fNIRS as a potential biomarker of stress.