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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Разработка и апробация портативной системы с биологической обратной связью по данным кожно-гальванической реакции для управления психоэмоциональным состоянием

Теоретическая и экспериментальная психология. 2025. Т. 18. № 4. С. 237–256.
Барыкина А. Н., Бондарчук Е. А., Ivanov I., Глотова Г. А., Polikanova I.

Background. In the context of a fast-paced lifestyle and high stress levels, assessing and managing psycho-emotional states has become an important task. Traditional methods, such as questionnaires, have limited accuracy due to the subjectivity of data, whereas physiological approaches, including the measurement of galvanic skin response (GSR), provide an objective assessment. However, existing devices for GSR recording are bulky and expensive, which limits their application. This article explores the possibility of using a portable biofeedback system (BFS) based on GSR. Objective. Development and testing of a portable biofeedback system (BFS) based on GSR, which combines sufficient measurement accuracy, ease of use, and the ability to integrate into daily life. Study Participants. A total of 43 individuals participated in the study. Methods. Comparative and correlational analyses were performed on data obtained from a portable GSR sensor and a laboratory EEG (NVX36). Data acquisition took place during a psycho-emotional task. A study was conducted to determine the opti mal sensor placement using descriptive and comparative methods. Regression analy sis was performed to configure the mobile application algorithm. A visualization was developed within the application to assess the user’s psycho-emotional state. Data vi sualization and graph analysis were employed at all stages. Results. The developed sensor demonstrated results comparable to laboratory equip ment, despite a slight delay in response. The fingers were identified to be the optimal placement for the sensor, as the signal from them is clearer and more stable. The mo bile application algorithm effectively aids in training emotional self-regulation. Conclusions. The portable BOS system based on GSR demonstrates sufficient accura cy and convenience, making it promising for everyday use. The results of this work can be applied to further research in the field of portable devices aimed at self-regulation training.

Research target: Electronics and Electrical Engineering Psychology Medical Technologies
Language: Russian
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Keywords: биологическая обратная связьBiofeedbackБОСмобильное приложениеMobile Applicationgalvanic skin responseкожно-гальваническая реакцияportable devicesпсихоэмоциональное состояние человекаpsycho-emotional stateemotional self-regulationКГРпортативное устройствоэмоциональная саморегуляция
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