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Address: Кривоколенный пер., д. 3, каб. 3-102
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SPIN-RSCI: 5631-4743
ORCID: 0000-0001-8827-9429
ResearcherID: M-9067-2013
Scopus AuthorID: 6603011121
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S. Kuznetsov
A. Shestakova
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Alexey Ossadtchi

  • Alexey Ossadtchi has been at HSE since 2013.

Education and Degrees

  • 2003

    PhD in Electrical Engineering
    University of Southern California
    Thesis Title: Noninvasive Automatic Detection of Epileptogenic Regions and Networks Using MEG Measurements

  • 1997

    N.E. Bauman Moscow State Technical University

Student Term / Thesis Papers

Full list of of student term / thesis papers

Courses (2017/2018)

Courses (2016/2017)

Courses (2015/2016)

Courses (2014/2015)

Editorial board membership

2012: Member of the Editorial Council (Review Editor), Frontiers in Human Neuroscience.


Recording and decoding system for analysis of bioelectrical activity of the human brain, Ministry of Education, 2014-2017


A novel non-invasive experimental and computational paradigm for presurgical magnetoencephalographic mapping of speech cortex



  • 2016
    IEEE International Symposium «Video and Audio Signal Processing in the Context of Neurotechnologies» (Санкт-Петербург). Presentation: MEG correlates of internalization of social influence
  • Biomag 2016 (Сеул). Presentation: Power and shift invariant imaging of coherent sources from MEG data (PSIICoS)
  • 2015

    V Международная конференция по биотехнологиям и фармацевтике ФизтехБио — 2015 (Москва). Presentation: MEG and EEG based neuroimaging of transient networks

  • Методические проблемы оценки функциональной синхронизации зон коры мозга на основании ЭЭГ-/МЭГ данных» (Москва). Presentation: МЭГ как результат активности и взаимодействия динамических сетей: метод порождающей модели
  • 2014
    International conference on biomagnetism, Biomag 2014 (Галифакс). Presentation: Interaction Space RAP-MUSIC for estimation of transient networks from MEG data
  • 9th FENS Forum of Neuroscience (Милан). Presentation: MPFC activity varies with differences in social conformity: MEG study

  • Научная сессия "Проблемы мозга" Российской Академии Наук (Москва). Presentation: Эффективное нейробиоуправление на основе пространственно-временных динамических моделей


Okorokova E., Lebedev M., Linderman M. and Ossadtchi A. (2015). A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings Front. Neurosci. 9 389 doi: 10.3389/fnins.2015.00389

Kozunov VV and Ossadtchi A (2015) GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings. Front. Neuroscience : 107. doi: 10.3389/fnins.2015.00107

A. Ossadtchi, P. Pronko, M. Pflieger, T. Stroganova, Mutual information spectrum – a new tool for detection of event related components in spatial decompositions and its application to M1 cerebral zone localization,  Frontiers in Human Neuroscience, In Press

Shtyrov, Y., Goryainova, G., Tugin, S., Ossadtchi, A., Shestakova, A., Automatic processing of unattended lexical information in visual oddball presentation: neurophysiological evidence.  Frontiers in Human Neuroscience, 7:421, doi: 10.3389/fnhum.2013.00421, 2013 .

R.E. Greenblatt, M.E. Pflieger, A. Ossadtchi, Connectivity measures applied to human brain electrophysiological data, Journal of Neuroscience Methods 207 (2012) 1– 16

A. Shestakova, J. Rieskamp, S. Tugin, A. Ossadtchi, J. Krutitskaya, and V. Klucharev., Electrophysiological precursors of social conformity. Frontiers in Decision Neuroscience, In Press, 2012

I. Ovod, A .Ossadtchi, A. Pupyshev,  A. Fradkov, Forming neurofeedback signal based on the adaptive model of the EEG observed human brain activity, Neurocomputers and applications, February, 2012 (in Russian).

A. Shestakova,  A. Ossadtchi, O. Kravtsenyuk, O. Getmanenko,  V. Klucharev, MEG and optical tomography – modern methods for investigation of cognitive development in babies and older kids., Chapter in “ Modern Methods in Neuroscience”,  (ed. Pavlov I. ), SPBSU Press, 2010, 189p., ISBN-978-5-288-05032-9,  pp. 126-141 (In Russian)

A. Ossadtchi, R.E. Greenblatt, V.L. Towle, M.H. Kohrman, K. Kamada, Inferring Spatiotemporal Network Patterns from Intracranial EEG Data, Clin, Neurophysiology,  June 2010

R.E. Greenblatt, A. Ossadtchi, L. Kurelowech, D. Lawson and J. Criado, Time-Frequency Source Estimation from MEG data,Frontiers in Neuroscience Methods, March 2010

R.E. Greenblatt, A. Ossadtchi, M.E. Pflieger, Non-target interference in MEG beamformer time series estimation,  International Congress Series, v. 1300, June 2007, pp. 137-140

R.E. Greenblatt, A. Ossadtchi, M.E. Pflieger, Local Linear Estimators for the Bioelectromagnetic Inverse Problem, IEEE Trans Signal Proc. 2005. v.53/9, 2005

R. E. Greenblatt, A. Ossadtchi, M.E. Pflieger and  D.C. Rojas, Local linear estimators and a statistical    framework for event related field analysis, Intl J Bioelectromagnetism, v. 7/2, 2005

A. Ossadtchi, J.C. Mosher, W.W. Sutherlin, R.E. Greenblatt, R.M. Leahy, Hidden Markov modeling of spike propagation from interictal MEG data, Phys. Med. Biol. 50 3447-3469, 2005

A. Ossadtchi, S. Baillet, J.C. Mosher, D. Thyerlei, W.W. Sutherling and R.M. Leahy,  Automated interictal spike  detection and source localization in MEG using ICA and spatial-temporal clustering.  Clin. Neurophysiology, 2004; 115/3, 508-522.

D. Thyerlei, A. Ossadtchi, T. Maleeva, A.N. Mamelak and W.W. Sutherling, Using intracranial depth electrode  stimulation as a reference source for reconstruction from simultaneous scalp-EEG.  NeuroImage 2003;

A. Khan, A. Ossadtchi, R.M. Leahy and D. Smith, Error-correcting microarray design.  Genomics2003;     81(2), 157-165

V. Dribinski, A. Ossadtchi, V. Mandelshtam and H. Reisler, Reconstruction of Abel-transformable images: The Basis-Set Expansion Abel Transform, Method.  Rev. Sci. Inst., 2002; 73.  

V.M. Brown, A. Ossadtchi, A.H. Khan, S. Yee, W.P. Lacan G, Melega, S.R. Cherry, R.M. Leahy  and D.J. Smith, Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson's disease.      Genome Res 2002;12:868-884.

T.A. Leil, A. Ossadtchi, J. Cortes, R.M. Leahy and D.J. Smith,  Finding new candidate genes for learning and  memory.  J Neurosci Res 2002; 68:127-137.

A. Ossadtchi, V.M. Brown, A.H. Khan, S.R. Cherry, R.M. Leahy, T. Nichols, D.J. Smith,  Statistical analysis of  multiplex brain gene expression images. Neurochem Res 2002; 27: 1113-1121.

V.M. Brown, A. Ossadtchi, A.H. Khan, S.S. Gambhir, S.R. Cherry, R.M. Leahy and D.J. Smith, Gene expression  tomography.  Phys. Genomics 2002; 8:159-167.

V.M. Brown, A. Ossadtchi, A.H. Khan, S.R. Cherry, R.M. Leahy and D.J. Smith,  High-throughput imaging of  brain gene expression.  Genome Res. 2002; 12:244-254.

Elizaveta Okorokova M.Sc. student (2nd year)
Sergey ParsegovPost-doctoral fellow
Eugene KalenkovitchM.Sc. student (1st year)

MEG and EEG-based brain-imaging technology allows us to visualize and study neuronal processes non-invasively, with very high temporal and reasonable spatial resolution. Currently, this method is used not only to localize neuronal activity but also to recover the dynamics of the entire neural network comprised of several distant cortical regions subserving the cognitive phenomenon at hands. The accuracy and reproducibility of such inferences crucially depend on the methods and computational approaches used for analysis of the MEG and EEG sensor data. Development of the novel methods and verification of the existing approaches allow us to ensure that the information present in the data is utilized with maximal efficiency. The use of the subject-specific probabilistic models obtained via data fusion form various modalities including fMRI, DTI, Optical tomography, TMS bear a promise of improving the non-invasively achieved resolution and further open "the non-invasive window" into the brain's function. 

The clinical portion of this work is done in close collaboration with the Moscow MEG center, Burdenko Institute of Neurosurgery and the Institute for problems of Mechanical Engineering, RAS, and is supported by an RFFI grant.

Real-time EEG: neurofeedback and neurointerfaces

Neurofeedback is a promising tool for non-pharmacological therapy for depression, ADHD, epilepsy and other psychiatric diseases and syndromes. This technique is also used for peak performance training and in relaxation practices. Within the neurofeedback paradigm a person is put into a closed loop where his/her brain state is presented as a feedback signal via one of the sensory modalities and is used to consciously (or not so consciously) modify the dynamics of his/her brain rythms. In this project we will consider this closed-loop system from the standpoint of control theory (both continuous and discrete) and will focus on developing novel model-based techniques to improve the efficiency and spatial specificity of neurofeedback therapy. This research will pave the way towards a new generation of more efficient and more natural brain-computer interfaces

Timetable for today

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