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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.
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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
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Language Localization From Magnetoencephalography (MEG) Beta-Power Dynamics During Sentence Completion

European Journal of Neuroscience. 2025. Vol. 62. No. 8. Article e70282.
Mariya Protopova, Bolgina T., Arutiunian V., Dragoy O.

For noninvasive language mapping, the choice of imaging method, task, and baseline remains an area of active research. While the sentence completion task is a recommended option for fMRI studies, the indirect nature of the signal is a limitation of the imaging method. This study presents a sentence completion paradigm for group- and individual-level language localization and lateralization based on beta power (17–25 Hz) modulations. MEG recordings of 21 neurologically healthy native Russian speakers were used to test whether the task would elicit beta desynchronization in canonical language regions during sentence completion. In addition to the traditional passive (no-task) control condition, an active (syllable repetition) control condition was used to further control for nonrelevant processes. The paradigm revealed the engagement of anterior and posterior language-related brain areas using both active and passive control conditions. However, the active control condition provided more widespread activity patterns, suggesting its superior suitability for further individual presurgical language mapping. Despite the individual variability in the results, their general agreement with the current understanding of the language-associated brain topography supports the potential of the developed MEG paradigm for presurgical language mapping.

Research target: Philology and Linguistics
Language: English
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DOI
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Keywords: Magnetoencephalography (MEG)sentence completionlanguage localizer paradigmbeta power
Publication based on the results of:
Neural foundations of language and its impairments: From basic research to clinical practice (2025)
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