Alexey Ossadtchi

Director:Centre for Bioelectric Interfaces
Leading Research Fellow:Centre for Cognition & Decision Making
Professor:Faculty of Computer Science / School of Data Analysis and Artificial Intelligence

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
Degree
N.E. Bauman Moscow State Technical University

Student Term / Thesis Papers

Bachelor
V. Minkov «Neurophysiological Correlates of Efficient Learning in the Neurofeedback Paradigm». Faculty of Social Sciences, 2018
V. Orlov «Software for Application Control via Brain-computer Interface». Faculty of Computer Science, 2017
Master
I. Dubyshkin «Advanced Signal Processing and Machine Learning Techniques for Unraveling Relations Between Various Functional Brain Imaging Modalities». Faculty of Social Sciences, 2018
A. Petrosyan «Application of Neural Networks for Decoding Kinematic Motion Parameters by Ecog Signals». Faculty of Computer Science, 2018
A. Markina «Improving Efficiency of the Ideomotor Brain Computer Interface (BCI) in the Neurofeedback Paradigm». Faculty of Social Sciences, 2018
A. Kuznetsova «Adaptive Spatial Filtering for Brain Event-Related Potentials Analysis». Faculty of Computer Science, 2017
R. Koshkin «Working Memory and Attention during Simultaneous Language Interpreting: An EEG Study». Faculty of Social Sciences, 2017
E. Kalenkovich «Solving the EEG and MEG Inverse Problem Using Physiologically Plausible Priors». Faculty of Social Sciences, 2017
E. Okorokova «Operant Conditioning of the Sensori-Motor Rhythm in the Neurofeedback Paradigm for Endogenous Enhancement of Motor-Imagery Brain-Computer Interface». Faculty of Social Sciences, 2016

Full list of of student term / thesis papers

Courses (2018/2019)

Digital Signal Processing (Master’s programme; Institute of Cognitive Neuroscience; programme "Cognitive Sciences and Technologies: From Neuron to Cognition")Eng
Past Courses

Courses (2017/2018)

Digital Signal Processing (Master’s programme; Institute of Cognitive Neuroscience; programme "Cognitive Sciences and Technologies: From Neuron to Cognition"; 1 year, 3 module)Eng
Scientific Seminar ''Intelligent Systems and Structural Analysis'' (Master’s programme; Faculty of Computer Science; programme "Data Science"; 1 year, 1-4 module)Eng

Editorial board membership

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

Grants

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

Conferences

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

Publications²⁵

Article Ossadtchi A., Лебедев М. А. Commentary: Injecting Instructions into Premotor Cortex // Frontiers in Cellular Neuroscience. 2018. Vol. 12. No. 65 doi
Article Ossadtchi A., Лебедев М. А. Commentary: Spatial Olfactory Learning Contributes to Place Field Formation in the Hippocampus // Frontiers in Systems Neuroscience. 2018. Vol. 12. No. 8 doi
Article Dagaev N., Volkova K., Ossadtchi A. Latent variable method for automatic adaptation to background states in motor imagery BCI // Journal of Neural Engineering. 2018. Vol. 15. No. 1. P. 1-14. doi
Article Лебедев М. А., Пимашкин А. С., Ossadtchi A. Navigation patterns and scent marking: underаppreciated contributors to hippocampal and entorhinal spatial representations? // Frontiers in Behavioral Neuroscience. 2018 doi (in print)
Article Ossadtchi A., Altukhov D., Jerbid K. Phase shift invariant imaging of coherent sources (PSIICOS) from MEG data // Neuroimage. 2018 doi (in print)
Chapter Ossadtchi A., Kulachenkov N., Chuchelov D., Pazgalev A., Petrenko M., Vershovskii A. Towards magnetoencephalography based on ultrasensitive laser pumped non-zero field magnetic sensor, in: International Conference Laser Optics 2018 (ICLO 2018).St. Petersburg, Russia, 4 - 8 June, 2018. Proceedings. NY, Red Hook : IEEE, 2018. doi
Article Zubarev I., Shestakova A., Klucharev V., Ossadtchi A., Moiseeva V. MEG Signatures of a Perceived Match or Mismatch between Individual and Group Opinions. // Frontiers in Neuroscience. 2017. Vol. 10. No. 11. P. 1-9. doi
Article Ossadtchi Alexei, Shamaeva T., Okorokova E., Moiseeva V., Lebedev M. A. Neurofeedback learning modifies the incidence rate of alpha spindles, but not their duration and amplitude // Scientific Reports. 2017. Vol. 7. No. 3772. P. 3772-1-3772-12. doi
Article Волкова К. В., Дагаев Н. И., Киселёв А., Касумов В., Александров М., Осадчий А. Е. Интерфейс мозг-компьютер: опыт построения, использования и возможные пути повышения рабочих характеристик // Журнал высшей нервной деятельности им. И.П. Павлова. 2017. Т. 67. № 4. С. 504-520.
Article Волкова К. В., Дагаев Н. И., Киселев А., Касумов В., Александров М. В., Осадчий А. Е. Интерфейс мозг-компьютер: опыт построения, использования и возможные пути повышения рабочих характеристик. // Журнал высшей нервной деятельности им. И.П. Павлова. 2017. Т. 67. № 4. С. 504-520.
Article Горшков А. А., Осадчий А. Е., Фрадков А. Л. Регуляризация обратной задачи ЭЭГ/МЭГ локальным кортикальным волновым паттерном // Информационно-управляющие системы. 2017. Т. 5. № 90 doi
Article Remko v. L., Houlihan S. D., Prasanta P., Sacchet M. D., McFarlane-Blake C., Patel P. R., Ossadtchi A., Druker S., Bauer C., Brewer J. A. Source-space EEG neurofeedback links subjective experience with brain activity during effortless awareness meditation // Neuroimage. 2016. Vol. 4 C. No. 3 doi
Article Elizaveta Okorokova, Linderman M., Ossadtchi A., Lebedev Mikhail. A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings // Frontiers in Neuroscience. 2015. Vol. 9. No. 389. P. 1-15. doi
Article Kozunov V., Ossadtchi A. GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings // Frontiers in Neuroscience. 2015. Vol. 9. No. 107 doi
Preprint Zubarev I., Ossadtchi A., Klucharev V., Shestakova A. MEG signature of social conformity: evidence from evoked and induced responses. / Центр Нейроэкономики и когнититвных исследований. Series 1 "1". 2014.
Article Pronko P., Baillet S., Pflieger M., Stroganova T., Ossadtchi A. Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping // Frontiers in Neuroinformatics. 2014. Vol. 7
Article Alexei Ossadtchi, Pronko P. K., Baillet S., Pflieger M., Stroganova T. The use of mutual information for selection of event-related components in ICA. Application to eloquent motor cortex mapping // Frontiers in Neuroinformatics. 2014. Vol. 7. No. January. P. Article 53.
Article Пронько П. К., Прокофьев А. О., Осадчий А. Е., Чернышев Б. В., Строганова Т. А. Функциональное разделение частей «сенсомоторного комплекса» коры мозга человека методом магнитоэнцефалографии // Журнал высшей нервной деятельности им. И.П. Павлова. 2014. Т. 64. № 2. С. 218-230.
Article 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. 2013. Vol. 7. No. 421. P. 1-10.
Article Shestakova A., Rieskamp J., Tugin S., Krutitskaya J., Klucharev V., Ossadtchi A. Electrophysiological precursors of social conformity // Social Cognitive and Affective Neuroscience. 2013. Vol. 8. No. 7. P. 756-763.
Chapter Zubarev I., Shestakova A., Klucharev V., Ossadtchi A. MEG study of social conformity, in: The 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM), June 16-20, 2013 at the Washington State Convention Center in Seattle, WA, USA. Сиэттл : [б.и.], 2013.
Chapter Shestakova A., Klucharev V., Zubarev I., Ossadtchi A. Resting state brain activity predicts individuals’ conformity, in: Society for Neuroscience Annual Meeting, November 9-13, 2013, San Diego, California. San Diego : , 2013.
Chapter Zubarev I., Shestakova A., Ossadtchi A., Rieskamp J., Klucharev V. The modification of judgments in a group situation: MEG correlates of conformity, in: Society for Neuroeconomics Annual Meeting, 27-29 September at EPFL, 2013, Lausanne, Switzerland. Lausanne : , 2013.
Article Ossadtchi A. Connectivity measures applied to human brain electrophysiological data // Journal of Neuroscience Methods. 2012. Vol. 207. No. 1. P. 1-16.
Article Шестакова А. Н., Буторина А., Осадчий А. Е., Штыров Ю. Ю. Магнитоэнцефалография – новейший метод функционального картирования мозга человека // Экспериментальная психология. 2012. Т. 5. № 2. С. 119-134.

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 9 : 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 Parsegov	Post-doctoral fellow
Eugene Kalenkovitch	M.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

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