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The original experimental scheme was developed to investigate athletes' functional states (FS) dynamics. The procedure allowed modeling various FS important for predicting the professional success of athletes: psychological and physiological stress, fatigue, and optimal FS (OFS). There were two main criteria for differentiation of the FS under study: efficiency rates and the psychological and physiological costs of the achieved efficiency level. Analysis of the FS-dependent psychophysiological changes showed significant interindividual differences on a number of parameters. Thus, no single indicator could be used as effective diagnostics for the FS criteria. A minimum number of indicators need to be recorded included cardiovascular indicators (heart rate, ECG), respiration, muscle tension (EMG), and brain activity (EEG) in the range of alpha and beta waves. The main problem can be artifacts induced by movement and muscle tension. The special procedure for artifact rejection and reduction of the artifacts was developed. It allowed recording EEG, ECG, and EOG signals simultaneously. Another problem was related to the development of the mathematical algorithm to analyze individual data and differentiate patterns of the signals recorded from the athletes. An original approach to differentiate the FS -the k-means clustering algorithmwas offered based on seven psychophysiological indicators. Results of clustering showed that the k-means algorithm for seven-component vectors allows one with confidence to differentiate state of quiet wakefulness, states of psychological and physiological stress. As the number of parameters used is attenuated from seven to four (without the EEG parameters) the accuracy of distinguishing

in his famous methodological essay "The historical meaning of psychological crisis" (1928) emphasized the importance of studying any psychological process or state as a "whole"-that is, as characterized from the subjective and objective sides at the same time. This position is fully relevant for studying the human functional states (FSes). Today the objective psychophysiological diagnostics of human FSes in activities associated with a high risk of technological disasters (in nuclear-power plants, transportation, the chemical industry) are extremely relevant and socially important. This article reviews some new psychophysiological methods of FS assessment that are being developed in Russia and abroad and discusses different aspects of developing integral psychophysiological FS assessment. The emphasis is on distant methods of FS diagnostics: the bioradiolocation method, laser Doppler vibrometry, eye tracking, audio and video recordings, infrared thermography. The possibilities and limitations of the most popular emotion atlases-the Facial Affect Scoring Technique (FAST) and the Facial Action Coding System (FACS)-in developing distant visual-range and infrared-range systems for automated classification of facial expressions are analyzed. A special section of the article concentrates on the problem of constructing an integral psychophysiological FS index. Mathematical algorithms that provide a partition of FS indicators into different FS types are based on various methods of machine learning. We propose the vector approach for construction of complex estimations of the human FSes.

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