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An approach to evaluation of the psychoemotional human state is discussed. The approach is based on an analysis of simultaneously registering infrared (IR) and terahertz (THz) emissions of the human body. Simultaneous registration of IR - THz images of a human face and psycho physiological indicators is carried out in situations of physical stress (short-term intensive physical exercise - functional squat test), electrical stimulation and informational stress (cognitive load - simple arithmetic mental calculations). Using the special analysis of IR - THz images, subjects can be divided into classes according to the type of reaction under stressful conditions.

The procedure of thresholding for graph construction is one of the common steps in calculating networks of brain connections. However, this procedure can lead to incomparable results from different studies. In the present study we aim to test the effect of thresholding or algorithmic reduction of the number of connected nodes on the construction of a set of widely used connectivity graph metrics derived from EEG data. 164 people took part in our study. Participants were recruited via social networks. EEG was recorded during resting state. At the beginning of the procedure each participant was asked to relax and not to think about anything. Source reconstruction was performed using standard source localization pipeline from MNE-package. Desikan-Killiany Atlas was used for cortical parcellation with 34 ROI per hemisphere. Synchronization was estimated with weighted phase lag index in 4–30 Hz frequency range for eyes closed and eyes open separately. We have found that All metrics except average participation coefficient vary monotonously as a function of density level (moreover, we have found, that for Cluster Coefficient, more than 95% and for the Characteristic Path Length ∼50% of the variance is related to thresholding cut-off). The different data-driven approaches to the network construction leads to significant changes in the group-level graph metrics and can eliminate the variance in the data that can be crucial for individual differences studies.