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May 15, 2026
Preserving Rationality in a Period of Turbulence
The HSE International Laboratory for Logic, Linguistics and Formal Philosophy studies logic and rationality in a transformed world characterised by a diversity of logical systems and rational agents. The laboratory supports and develops academic ties with Russian and international partners. The HSE News Service spoke with the head of the laboratory, Prof. Elena Dragalina-Chernaya, about its work.
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Ilya Venediktov graduated from the Master’s programme at the HSE Tikhonov Moscow Institute of Electronics and Mathematics through the combined Master’s–PhD track and is currently studying at the HSE Doctoral School of Engineering Sciences. At present, he is undertaking a long-term research internship at the University of Science and Technology of China in Hefei, where he is preparing his dissertation. In this interview, he explains how an internship differs from an academic mobility programme, discusses his research topic, and describes the daily life of a Russian doctoral student in China.
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Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping

Frontiers in Neuroinformatics. 2014. Vol. 7.
Pronko P., Baillet S., Pflieger M., Stroganova T., Ossadtchi A.

Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis. In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information (MI) Spectrum and is based on gauging the amount of MI of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of MI. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described MI based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the MI Spectrum for the selection of task-related independent components from real MEG data. We show that the MI spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.

Research target: Mathematics Earth Sciences Biology
Language: English
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Keywords: mutual informationeloquent cortex mappingcomponents selection
Publication based on the results of:
Neurocognitive mechanisms of decision making and social influence (2014)
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