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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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Functional Brain Connectivity and Musical Training: Insights from EEG Phase Synchronization During Music Perception

Cold Spring Harbor Laboratory , 2024.
Проворова А. А., Kulikova S.
Musical training profoundly impacts brain organization, fostering functional and structural adaptations that enhance auditory, motor, and cognitive processes. This study investigates functional connectivity differences between professional musicians and non-musicians during tasks involving music perception and different graphic notations. Using EEG phase-locking value (PLV) analysis, we examined synchronization across alpha, beta, and gamma frequency bands. Results revealed that musicians exhibited enhanced functional connectivity between brain regions, reflecting their ability to integrate auditory, visual, and cognitive information efficiently. In the alpha band, strong connectivity between the occipital and temporal regions was observed during all tasks, with additional synchronization between the parietal and temporal regions in tasks lacking pitch information. In the beta band, significant frontal-temporal connectivity was detected, indicating cognitive processing, while temporal-occipital synchronization emerged in specific tasks. In the gamma band, musicians demonstrated robust frontal-temporal and frontal-frontal synchronization, reflecting emotional and structural processing of music. Non-musicians showed reduced task-specific connectivity, relying on generalized strategies. These findings underscore the role of musical training in enhancing multimodal brain integration and cognitive flexibility, contributing to advanced music perception and processing.
Research target: Psychology Education
Priority areas: sociology
Language: Russian
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Text on another site
Keywords: музыканотыmusicвосприятиеperceptionEEGЭЭГbrain functional connectivityфункциональная связностьNotes
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