## 2 Overview of Selected Domestic and Foreign Research Studies
[PERSON] and others discussed the use of machine learning techniques to analyze data on neuropsychological reactions, helping identify individual differences in employees' ability to cope with emotional stress [14]. The results of these studies could be useful for creating personalized mental health support plans in professional environments. [PERSON] and [PERSON] investigated the impact of task complexity on pilot workload using pulse oximetry, electroencephalography, and the NASA-TLX questionnaire [15]. The results confirmed the effectiveness of combining objective and subjective methods of workload assessment. [PERSON] et al. used functional near-infrared spectroscopy (fNIRS) to assess the mental workload of unmanned aerial vehicle (UAV) cadets [16]. The study showed that the duration of peak oxyhemoglobin concentration can serve as an indicator of mental workload. [PERSON] et al. proposed a multi-modal approach to stress detection by combining analysis of facial features and biometric signals [17]. The model achieved an accuracy of 98.38%, indicating the potential of such methods for monitoring psychophysiological states. [PERSON] et al. conducted a systematic review of sensory systems for monitoring mental fatigue [18]. They highlighted the effectiveness of using electroencephalography (EEG) and other biosensors to detect fatigue in real time. [PERSON] et al. developed an AI-driven stress detection system that analyzed data from various sources, including facial expressions, heart rate, and computer interaction [19]. The system showed high accuracy in classifying stress levels, demonstrating the effectiveness of the multi-modal approach. It is also important to note that sound quality and its psychoacoustic perception can play a role in mental fatigue and overall comfort in the workplace. In this regard, [PERSON] and others presented a psychoacoustic model that correlates subjective and objective sound assessments, which can be adapted for flight simulators and aviation environments [20]. Furthermore, the monograph by [PERSON] and others [21] provides an extensive overview of psychoacoustic principles and the acoustic design of products, forming a valuable foundation for integrating acoustic comfort considerations into instructor workspaces and flight simulation facilities. Equally important is the development of approaches for the comprehensive assessment of psychophysiological states in high-stress environments, as seen in workers in extreme conditions such as aviation or rescue services [22]. [PERSON] and [PERSON] examined the integration of various types of data, including psychological tests and physiological indicators, enabling the creation of a model to assess workers' readiness to perform highly complex tasks [23]. This not only helps identify potentially dangerous conditions but also provides early warnings about possible stress situations before they occur. The development of such models opens new opportunities for preventing psychophysiological crises, which can significantly enhance performance in complex and hazardous professions. The analysis of the studies indicates a lack of a comprehensive approach to evaluating levels of mental health using methods of intelligent knowledge analysis and fuzzy set theory. Furthermore, there has been no in-depth research aimed at determining the psychophysiological workload levels of flight instructors. This highlights the relevance of further research in this area. The structure of the present paper is as follows. The formal problem statement and the presented intellectual-analytical platform for assessing the psychophysiological workload of flight instructors are provided in Section 3. Section 4 describes the verification of the intellectual-analytical platform using real data, as well as an example of the calculations. The results of this study, its advantages, and its limitations are discussed in Section 5. This research concludes with Section 6, where conclusions and future research ideas are formulated.