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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Single trial ERP reading based on Parallel Factor Analysis

Psychophysiology. 2013. Vol. 50. P. 97–110.
Vanderperren K., Mijovic B., Novitskiy N., Vanrumste B., Stiers P., Van den Bergh B., Lagae L., Wagemans J., Sunaert S., Van Huffel S., De Vos M.

The extraction of task-related single trial ERP information has recently gained much interest, not only in studies on ERPs alone, but also in simultaneous EEG-fMRI applications. The investigation of these single trial data, however, requires a specific decomposition to retrieve the task-related activity from the originally acquired raw data. In this study, this is achieved with source extraction based on parallel factor analysis (PARAFAC). We show that differences between distinct task-related conditions can be captured in the trial signatures of specific PARAFAC components when applied to single trial ERP data arranged in channels x time x trials arrays. The performance of this method is illustrated for data from a visual detection task, acquired in normal circumstances and simultaneously with fMRI. We also checked whether the obtained trial signatures correlated with the fMRI data, but with this approach no significant results were found.

Research target: Psychology Basic Medicine Health Studies
Language: English
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Keywords: cognitionEEGfMRIEvent-Related Potentials (ERPs)multivariate statistical methods
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