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.

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced by negative, positive, and neutral expressions on human faces. Three methods were used to study brain reactions: power spectral density, detrending moving average (DMA), and coherence analysis. Using electroencephalogram (EEG) recordings from 48 subjects while presenting facial image stimuli from the International Affective Picture System, the topographic representation of the evoked responses was acquired and evaluated to disclose the specific EEG-based activity patterns in the cortex. The theta and beta systems are two key cognitive systems of the brain that are activated differently on the basis of gender. The obtained results also demonstrate that the DMA method can provide information about the cortical networks’ functioning stability, so it can be coupled with more prevalent methods of EEG analysis.

In this paper, the importance of implementing good acoustic conditions in classrooms using sound amplification systems is investigated to support more effective English education for elementary school children. To date, the failure of educating English as a second language at Japanese schools has been demonstrated by poor English conversation ability of those who completed a compulsory six-year English language course at Japanese junior-high and high schools (age 12–18). To amend the situation, teaching English became compulsory at grade three (age 8–9) and above at most Japanese elementary schools in the 2020 academic year. We conducted acoustic measurements of two types of sound amplification systems, a pair of PC loudspeakers and another with a loudspeaker array, in a typical classroom at an elementary school in Japan. We also analysed English listening test results of 216 Japanese native children (age 11–12) who were learning English in their usual classes in Japan, to compare the effects of those two systems. Results of logistic regression analysis adjusted by the discrimination difficulty of word pairs demonstrated the statistically significant association between correct answer rate of the English tests and classroom acoustic factors. Although, on average, upgrading the sound amplification system had positive effects on the correct answer rate, it also had a negative impact when the word pairs had English phoneme contrasts that do not appear in Japanese phoneme structure. Combined with the acoustic measurements’ results, it was also revealed that heterogeneous sound fields that depend on seat positions could be compensated using sound amplification systems with loudspeaker arrays. Our findings suggest that improvement of both acoustic quality and teaching methods is required for children to acquire English communication skills effectively in their classroom.

This paper presents a new approach to analyzing the components of centrifuge rotor vibrograms using a 2D trajectory fractal analysis based on the Detrended Moving Average method. The method identifies the different noise oscillatory behavior of the rotor depending on the rotation frequencies, ranging from non-stationary unbounded and 1/f pink noise to correlated and uncorrelated noise. Fractal characteristics of the vibrograms were computed for the first time and demonstrated differences for rotation frequencies close to the eigenfrequencies and far from them. This paper also discusses the influence of gyroscopic effects on the natural frequencies of centrifuge oscillations and the excitation of second harmonics when the centrifuge rotates at higher frequencies. The main cause of rotor vibration is identified as the mass imbalance of the rotors, and this paper proposes a vibration classification according to source nodes to diagnose serviceable and faulty technical systems. Finally, the possibility of anisotropy of the vibrogram is discussed, and the oriented fractal scaling components analysis method is applied to pave the way for further investigation.

This work is aimed to study the changes of the emotion-induced variations in the brain's electrical activity. We focused on revealing alterations in cognitive mechanisms caused by the presentation of neutral human face stimuli among faces with either negative or positive expressions. To access the changes, Fourier Power Spectral Density (PSD) and the scaling exponent values obtained from the detrended moving average analysis (DMA) were compared in seven frequency ranges for the series when emotionally neutral stimuli were presented after positive and negative images of human faces. As a result, the cortical mapping of the evoked responses was obtained and analyzed to reveal the manifestations of the specific brain activity. Sex-related differences consisted of activating two major cognitive networks of the brain: emotional or theta-network and cognitive beta-network. As for cortical topography, PSD changes occurred in the antero-central areas of the cortex in the $\boldsymbol{\theta 1}$ -subband (extension of working memory) and almost generally within all the cortical areas in the $\boldsymbol{\beta 1}$ -subband (activation of external attention). Analysis conducted between male and female groups showed that the most significant changes were found in the female group during the processing of neutral images under the negative stimuli aftereffect. The DMA analysis revealed a greater synchronization of oscillations in the 01-range in the left frontal-temporal zones in the case of neutral image presentation preceded by positive stimuli. Apart from that, in the series of neutral image presentation against the negative emotional background, the female group was characterized by maintaining a high level of long-range spatio-temporal correlations of the electroencephalogram signal in the central and right temporal-occipital areas.