This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload.

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

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Summary. One of the phenomena of personal deformation is emotional burnout (EB). Emotional burnout syndrome (EBS) can occur during studying in institution (university) and become an obstacle in its process. EBS affects up to 40% (students) of young people studying. Components of learning: social comparison and evaluation, dissatisfaction with the process and the result of learning, feelings of injustice in the assessment of knowledge, unjustified hopes, difficulties in communication are factors of emotional burnout. Aim. To determine the presence and influence of negative factors on the possibility of emotional burnout in students of higher educational institutions. Materials and methods. A one-step (transverse) study was conducted. Social networks popular among students – Telegram, Instagram – were chosen to distribute the questionnaire. In particular, the survey form was sent in such chats as NMU, KT-18, LNTU students, conversations of students of NMU named after O.O. Bogomolets, National University “Lviv Polytechnic” and Borys Hrinchenko Kyiv University. Results and discussion. When analyzing the data according to the specialization of the higher educational institution and the year of study, attention is drawn to the increase in the percentage of students studying medicine, dentistry, pharmacy according to the years of study who force themselves to work (learn subjects) despite fatigue. These are the students of second grade – 8.33%, third – 23.86%, and fifth one 25%. The number of such students studying philology tends to decrease from 100% in the third study year to 25% in the fourth one. The number of third-year students who try to study despite fatigue, in the field of economics, philology and management and marketing is greater than among students who study in the field of medicine, dentistry and pharmacy. 33.3%, 100%, 28.5% against 23.8%, respectively. Conclusions. Based on the results obtained, it can be concluded that with each study year, the percentage of students who feel depressed due to learning difficulties, who feel tired and unwilling to study in senior grades, is growing. It was also detected that in senior grades among students who experience chronic fatigue, the use of psychoactive substances is highly spread. Among the surveyed students, there is a high percentage of probable risk of development of the syndrome of emotional burnout (SEB). It is specified that the percentage of probable risk of SEB increases with each year of study. It was found that medical students tend to feel chronic fatigue and the need for additional motivation to study earlier than other students. Computer science students were the least likely to develop emotional burnout.