A.A. Nigrei – Post-graduate Student, Omsk State Transport University

E-mail: aa.nig@yandex.ru

S.S. Zhumazhanova – Post-graduate Student, Radio Engineering Faculty, Omsk State Technical University

E-mail: samal_shumashanova@mail.ru

A.E. Sulavko – Ph.D. (Eng.), Associate Professor, Department of Integrated Information Security;  Senior Research Scientist, Omsk State Technical University E-mail: sulavich@mail.ru

Formulation of the problem. The psychophysiological state (PPS) of a person directly affects his ability to lead labor activity. The key states in the work are considered: stress, sleep (all phases and stages), drowsiness (falling asleep), alcohol intoxication. These states are the most important from the point of view of the need for their timely identification in the process of professional activity of employees whose work is associated with a high concentration of attention and increased danger. Since the presence of these conditions in an employee can lead to an accident at work.

Purpose of work. This work is devoted to an analytical-synthetic study of the problem of automatic assessment (recognition) of a person's psychophysiological state according to the parameters of electroencephalograms (EEG).

Results. The regularities of changes in EEG signals depending on the condition of the subject, as well as the key parameters of the EEG characterizing the state of a person are revealed, described and generalized (including patterns of changes in the rhythmic activity of the EEG). Methods of inducing stress in laboratory conditions are presented, their advantages and disadvantages are indicated. The physiology of stress is examined and stimuli causing stress are given. The stages of sleep and the process of their classification by a specialist are described. The features of each stage and their EEG markers are presented. This paper also highlights the classification features of the stage of alcohol intoxication and correlating signs. Also shown are the actual electrode location systems for acquiring an EEG signal. The existing methods and approaches to the determination of psychophysiological conditions by EEG parameters are analyzed and generalized.

Practical value. The achieved results on the identification of stress, sleep, drowsiness, alcohol intoxication using the methods of machine learning and pattern recognition are presented (including support vector machine, neural networks, nearest neighbors, Bayesian classification, fuzzy logic and others). The main problems in this area (lack of criteria for an accurate assessment of the condition of the subject, the presence of artifacts on the EEG) are identified and further development prospects are identified.

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