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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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Hardware powered ultra low latency (HarPULL) brain-state dependent TMS technology

P. 189–195.
Fedosov N., Julia Nekrasova, Makarova M., Shevtsov O., Daria Medvedeva, Alexei Ossadtchi

The paper introduces a novel hardware-software complex for the low latency real time phase-dependent transcranial
magnetic stimulation (TMS) neurofeedback. The software with key elements implemented on the board of the EEGrecording device accurately tracks the sensorimotor rhythm phase with minimal time-delay and immediately sends the trigger to the TMS-device. An accurate phase estimation is achieved by proper Kalman filter-based state-space modeling of brain rhythm. Our experiments demonstrate the system’s viability and its almost instant reaction on the target brain event occurrence. Our results potentially make neurofeedback, cortical mapping, therapeutic TMS-interventions and other applications more reliable and efficient.

Language: English
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Keywords: TMSнейрообратная связьneurofeedbackMotor evoked Potentials
Publication based on the results of:
Fundamentals and hardware-software neurotechnologies for diagnosis, rehabilitation, and support of cognitive activity (2023)

In book

2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB), 28-30 Sept. 2023
IEEE, 2023.
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