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The aim of the present study was to investigate whether EEG resting state connectivity correlates with intelligence. One-hundred and sixty five participants took part in the study. Six minutes of eyes closed EEG resting state was recorded for each participant. Graph theoretical connectivity metrics were calculated separately for two well-established synchronization measures [weighted Phase Lag Index (wPLI) and Imaginary Coherence (iMCOH)] and for sensor- and source EEG space. Non-verbal intelligence was measured with Raven’s Progressive Matrices. In line with the Neural Efficiency Hypothesis, path lengths characteristics of the brain networks (Average and Characteristic Path lengths, Diameter and Closeness Centrality) within alpha band range were significantly correlated with non-verbal intelligence for sensor space but no for source space. According to our results, variance in non-verbal intelligence measure can be mainly explained by the graph metrics built from the networks that include both weak and strong connections between the nodes.

The choice of the tools and programming technologies for information systems creation is relevant. For every projected system, it is necessary to define a number of criteria for development environment, used libraries and technologies. The paper describes the choice of technological solutions using the example of the developed web-based platform of the Russian Academy of Education. This platform is used to provide information support for the activities of psychologists in their research (including population and longitudinal researches). There are following system features: large scale and significant amount of developing time that needs implementation and ensuring the guaranteed computing reliability of a wide range of digital tools used in psychological research; ensuring functioning in different environments when conducting mass research in schools that have different characteristics of computing resources and communication channels; possibility of services scaling; security and privacy of data; use of technologies and programming tools that would ensure the compatibility and conversion of data with other tools of psychological research processing. Some criteria were introduced for the developed system. These criteria take into account the feature of the functioning and life cycle of the software. A specific example shows the selection of appropriate technological solutions., 12 pages

This article is devoted to the development of a model of an artificial neural network for predicting the level of nonverbal intelligence according to the EEG of the brain. Cognitive functioning relies on the synchronization between different brain structures. However, it is still unclear how individual differences in intelligence are related to the global characteristics of information transmission in brain networks. Resting-state functional connectivity studies show the association of patterns of interactions between brain regions from people and different levels of nonverbal intelligence. In this study, we present a process of development of a neural network model used to predict the level of nonverbal intelligence based on EEG data of the brain. We have developed a fully-connected neural network to predict the level of nonverbal intelligence.

The procedure of thresholding for graph construction is one of the common steps in calculating networks of brain connections. However, this procedure can lead to incomparable results from different studies. In the present study we aim to test the effect of thresholding or algorithmic reduction of the number of connected nodes on the construction of a set of widely used connectivity graph metrics derived from EEG data. 164 people took part in our study. Participants were recruited via social networks. EEG was recorded during resting state. At the beginning of the procedure each participant was asked to relax and not to think about anything. Source reconstruction was performed using standard source localization pipeline from MNE-package. Desikan-Killiany Atlas was used for cortical parcellation with 34 ROI per hemisphere. Synchronization was estimated with weighted phase lag index in 4–30 Hz frequency range for eyes closed and eyes open separately. We have found that All metrics except average participation coefficient vary monotonously as a function of density level (moreover, we have found, that for Cluster Coefficient, more than 95% and for the Characteristic Path Length ∼50% of the variance is related to thresholding cut-off). The different data-driven approaches to the network construction leads to significant changes in the group-level graph metrics and can eliminate the variance in the data that can be crucial for individual differences studies.

This paper considers the design and development of a web-based platform for conducting psychological online research. As a result, a scalable multicomponent platform architecture is formed, implying the separation into a public and private Intranet parts. To organize communication between components, it is intended to use the REST API. The basic unit of data transmission for experiments is a package that allows ensuring the autonomy of the online tool in conditions of poor Internet connection. A technological stack is selected, which includes the use of JavaScript and AngularJS 1 for the client part, Node.JS and Loopback for the server.

A web platform for psychological research needs a single format of questionnaire tests to ensure interaction between its components. The study proposes a general test structure, variant questions, variations in response types, and an embedded domain-specific language for computations. The use of JSON is proposed and justified to store the hierarchical structure of the questionnaire test, and JSON Schema is defined as a technology suitable for the formation of the standard. From the considered validation instruments for compliance with the standard described in JSON Schema, ajv was defined as the most applicable to the task. To build the documentation, Doca is relevant, but this tool needs to be modified to meet the requirements of the task.

In this article we presented the computerized tools for psychological studies of memory. The importance of implementing computerized automated tools for psychological studies is discussed. It has been shown that this tools can be used both for cross-cultural and genetically informative studies. The validity of these tools for cross-cultural and genetically informative studies of memory can be seen as the first step to use automated computerized tools for big data collection in psychology.

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 fc-means clustering algorithm - was offered based on seven psychophysiological indicators. Results of clustering showed that the fc-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 FS is significantly reduced. To construct a complete and accurate differentiation of an athlete's FS one should collect some statistical data on the dynamics of each FS in different time periods of the person's life - in the process of training, after successful competition, and after losing competition.