Changes in the Power and Coupling of Infra-Slow Oscillations in the Signals of EEG Leads during Stress-Inducing Cognitive Tasks  
[PERSON]  
1 Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; [EMAIL_ADDRESS] (E.I.B.); [EMAIL_ADDRESS] (A.N.H.); [EMAIL_ADDRESS] (E.S.D.); [EMAIL_ADDRESS] (V.I.F.); [EMAIL_ADDRESS] (Y.M.I.); [EMAIL_ADDRESS] (A.V.K.); kuruko.sas  
to stress in humans vary considerably [5; 7]. The task of stress diagnosis is complicated by the complexity and nonstationarity of the electroencephalogram (EEG) signals usually used to detect a stress state. Therefore, the quantitative assessment of human stress using biosignals remains a difficult task that requires more detailed study [5; 7].  
Couplings between different parts of the cerebral cortex and the other parts of the brain, such as pons, are important for understanding the functioning of the brain under various conditions, including stress [5; 17; 18]. For instance, in [17], the authors showed that the permanently established chemical axotomy of the locus coeruleus (LC) and dorsal raphe (DR) axon terminals in freely moving rats changed naturally occurring cortico-pontene theta (4-8 Hz) synchronization phase shift. Similarly, DR or LC chemical axotomy induces a new mode of coupling between sigma (10-15 Hz in rats) and theta EEG oscillations tight to non-rapid-eye-movement (NREM) sleep, suggesting that loss of monoaminergic signaling interferes with NREM sleep consolidation [18].  
Methods have been proposed for detecting a stress state based on coherence analysis [19; 20; 21; 22; 23], Pearson's correlation coefficient [24], cross-covariance [25], auto-covariance [24], mutual information [26; 27; 28], partial directed coherence [29; 30], generalized partial directed coherence [31], and directed transfer function [32]. However, among a great number of known results are mutually contradictory conclusions, depending on the method of analysis, the type of stress, and the analyzed frequency range [5].  
Generally, the study of brain activity during changes in the psycho-emotional state is carried out in the following frequency ranges: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (30-50 Hz). At the same time, the analysis of infra-slow oscillations of brain potentials with a frequency of less than 0.5 Hz can provide important additional information for diagnosing the development of stress. It has been shown that the infra-slow oscillations of brain potentials are associated with the activity of autonomic control centers [3; 33; 34; 35]. Moreover, it is known that the autonomic nervous system plays an important role in the body's response to stress, changing the activity of its elements under stressful conditions [7; 16; 36]. The infra-slow oscillations in EEG signals were analyzed in the studies [16; 37; 38; 39; 40; 41; 42; 43], which dealt with cognitive task-solving. The infra-slow oscillations of brain potentials correlate with human behavior [40] and change during sleep [10; 11; 12; 41; 42; 43].  
Thus, the infra-slow oscillations of brain potentials may reflect a change in the psychophysiological state of a subject and the development of stress. The aim of this study was to identify biomarkers of the psychophysiological state during stress-inducing cognitive tasks. We analyzed the power spectra of infra-slow oscillations in EEG signals. Additionally, we studied the phase coherence coefficients and indices of directional coupling between infra-slow oscillations in the signals of different EEG leads. We revealed that the analysis of individual and collective dynamics of infra-slow oscillations of brain potentials is promising for the detection of stress.