Alexey Ossadtchi
- Director:Institute for Cognitive Neuroscience / Centre for Bioelectric Interfaces
- Leading Research Fellow:Institute for Cognitive Neuroscience / Centre for Cognition & Decision Making
- Professor:Faculty of Computer Science / School of Data Analysis and Artificial Intelligence
- Alexey Ossadtchi has been at HSE University 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 in Autonomous Information and Management Systems
Bauman Moscow State Technical University
Student Term / Thesis Papers
- Bachelor
A. Schepinova, Riemannian Manifold Informed Projections for Debiasing Multiple Data Covariance Matrices against Source Correlations. Faculty of Mathematics, 2020
I. Nurislamova, Non-Invasive Mapping of Correlated Sources Using Modified Beamformer Approach. Faculty of Social Sciences, 2019
M. Monina, Decoding Upper Limb Trajectory Based on Invasively and Non-invasively Measured Electrical Activity of the Brain. HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE), 2019
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
A. Paltarzhytskaya, The Influence of the Musical Piece Familiarity Factor on Subjective Perception of its Duration. Institute for Cognitive Neuroscience, 2020
A. Paltarzhytskaya, The Influence of the Musical Piece Familiarity Factor on Subjective Perception of its Duration. Institute for Cognitive Neuroscience, 2020
A. Belinskaia, The Effect of Feedback Signal Presentation Latency on the Effectiveness of Training in Neurofeedback Paradigm. Institute for Cognitive Neuroscience, 2019
V. Bulgakova, The Feasibility of Methods for Solving the Inverse Problem in Electrocortigraphy Data Analysis. Institute for Cognitive Neuroscience, 2019
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
E. Kalenkovich, Solving the EEG and MEG Inverse Problem Using Physiologically Plausible Priors. Faculty of Social Sciences, 2017
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. 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
Courses (2020/2021)
- Mathematical Aspects of EEG and MEG Based Neuroimaging (Master’s programme; Institute for Cognitive Neuroscience; 1 year, 3 module)Eng
- Past Courses
Courses (2019/2020)
Courses (2018/2019)
Courses (2015/2016)
Research Seminar "Data Mining and Analysis" (Bachelor’s programme; Faculty of Computer Science; "Алгоритмика"; field of study "01.03.02. Прикладная математика и информатика"; 3 year, 1-4 module)Rus
- Research Seminar "Data Mining and Analysis" (Bachelor’s programme; Faculty of Computer Science; 4 year, 1-3 module)Rus
- Research Seminar "Data Mining and Analysis 1" (Bachelor’s programme; Faculty of Computer Science; 2 year, 1-4 module)Rus
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
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| A novel non-invasive experimental and computational paradigm for presurgical magnetoencephalographic mapping of speech cortex
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Conferences
- 2016IEEE 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
- 2014International 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
Publications43
- Chapter Petrosyan A., Lebedev M., Ossadtchi A. Decoding Neural Signals with a Compact and Interpretable Convolutional Neural Network, in: Advances in Neural Computation, Machine Learning, and Cognitive Research IV. Selected Papers from the XXII International Conference on Neuroinformatics, October 12-16, 2020, Moscow, Russia. Springer, 2021. doi P. 420-428. doi
- Article Petrosyan A., Sinkin M., Lebedev M., Ossadtchi A. Decoding and interpreting cortical signals with a compact convolutional neural network // Journal of Neural Engineering. 2021. Vol. 18. No. 2. Article 026019. doi
- Chapter Petrosyan A., Lebedev M., Ossadtchi A. Linear Systems Theoretic Approach to Interpretation of Spatial and Temporal Weights in Compact CNNs: Monte-Carlo Study, in: Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020. Proceedings of the 11th Annual Meeting of the BICA Society. Springer, 2021. doi P. 365-370. doi
- Article Gorin A., Klucharev V., Ossadtchi A., Zubarev I., Moiseeva V., Shestakova A. MEG signatures of remote effects of agreement and disagreement with the majority // Scientific Reports. 2021. Vol. 11. No. 1 . P. 1-10. doi
- Article Kuznetsova A., Nurislamova Y., Ossadtchi A. Modified covariance beamformer for solving MEG inverse problem in the environment with correlated sources // Neuroimage. 2021. Vol. 228. Article 117677. doi
- Article Ossadtchi A., Lebedev M. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies // Brain. 2020. Vol. 143. No. 6. P. 1674-1685. doi
- Article Smetanin N., Belinskaya A., Lebedev M., Ossadtchi A. Digital filters for low-latency quantification of brain rhythms in real-time // Journal of Neural Engineering. 2020. Vol. 17. No. 4. P. 1-14. doi
- Chapter Lebedev M., Ossadtchi A., Okorokova L., Erlichman J. S., Rupasov V. I., Linderman M. Generating Handwriting from Multichannel Electromyographic Activity, in: Brain–Computer Interface Research. A State-of-the-Art Summary 8. Springer, 2020. doi P. 11-23. doi
- Article Belinskaya A., Lebedev M., Smetanin N., Ossadtchi A. Short-delay neurofeedback facilitates training of the parietal alpha rhythm // Journal of Neural Engineering. 2020. Vol. 17. No. 6. P. 066012. doi
- Article Lebedev M., Ossadtchi A., Urpí N. A., Mill N. A., Cervera M. R., Nicolelis M. A. Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics. // Scientific Reports. 2019. Vol. 9. No. 1. P. 1-14. doi
- Article Lebedev M., Ossadtchi A. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist) // Brain. 2019. P. 1674-1685. doi
- Article Volkova K., Lebedev M., Kaplan A., Ossadtchi A. Decoding Movement From Electrocorticographic Activity: A Review // Frontiers in Neuroinformatics. 2019. No. 13. P. 1-20. doi
- Chapter Volkova K., Petrosyan A., Дубышкин И., Ossadtchi A. Decoding movement time-course from ecog using deep learning and implications for bidirectional brain-computer interfacing, in: Актуальные проблемы психологической науки: Сборник статей и выступлений международной научной конференции / Под общ. ред.: Е. С. Горбунова. Научно-инновационный центр, 2019. doi doi
- Preprint Lebedev M., Ossadtchi A. What, if anything, is the true neurophysiological significance of “rotational dynamics”? / Cold Spring Harbor Laboratory. Series http://dx.doi.org/ "BioRxiv". 2019. doi
- Article Синкин М. В., Осадчий А. Е., Лебедев М. А., Волкова К. В., Кондратова М. С., Трифонов И. С., Крылов В. В. Пассивное речевое картирование высокой точности во время операций по поводу глиом доминантного полушария // Нейрохирургия. 2019. Т. 21. № 3. С. 37-43. doi (in press)
- Chapter Lebedev M., Ossadtchi A. Bidirectional neural interfaces, in: Brain–Computer Interfaces Handbook. CRC Press, 2018. P. 701-720. doi
- Article Ossadtchi A., Lebedev M. Commentary: Injecting Instructions into Premotor Cortex // Frontiers in Cellular Neuroscience. 2018. Vol. 12. No. 65. P. 1-3. doi
- Article Ossadtchi A., Lebedev M. Commentary: Spatial Olfactory Learning Contributes to Place Field Formation in the Hippocampus // Frontiers in Systems Neuroscience. 2018. Vol. 12. No. 8. P. 1-5. 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 Smetanin N., Volkova K., Zabodaev S., Lebedev M., Ossadtchi A. NFBLab - a versatile software for neurofeedback and brain-computer interface research // Frontiers in Neuroinformatics. 2018. Vol. 12. No. 100. P. 1-18. doi
- Article Lebedev M., Пимашкин А. С., Ossadtchi A. Navigation Patterns and Scent Marking: Underappreciated Contributors to Hippocampal and Entorhinal Spatial Representations? // Frontiers in Behavioral Neuroscience. 2018. Vol. 12. No. 98. P. 1-8. doi
- Article Ossadtchi A., Altukhov D., Jerbi K. Phase shift invariant imaging of coherent sources (PSIICOS) from MEG data. // Neuroimage. 2018. Vol. 183. P. 950-971. doi
- Article Koshkin R., Shtyrov Y., Myachykov A., Ossadtchi A. Testing the Efforts Model of Simultaneous Interpreting: An ERP Study // Plos One. 2018. Vol. 10. No. 13. P. 1-18. doi
- 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
Artificial Neurons Help Decode Cortical Signals
Russian scientists have proposed a new algorithm for automatic decoding and interpreting the decoder weights, which can be used both in brain-computer interfaces and in fundamental research. The results ofthe study were published in the Journal of Neural Engineering.
Researchers Expand the Capabilities of Magnetoencephalography
Researchers from the HSE Institute for Cognitive Neuroscience have proposed a new method to process magnetoencephalography (MEG) data, which helps find cortical activation areas with higher precision. The method can be used in both basic research and clinical practice to diagnose a wide range of neurological disorders and to prepare patients for brain surgery. The paper describing the algorithm was published in the journal NeuroImage.
How Modern Robots Are Developed
Today, neuroscience and robotics are developing hand in hand. Mikhail Lebedev, Academic Supervisor at HSE University’s Centre for Bioelectric Interfaces, spoke about how studying the brain inspires the development of robots.
Scholars Provide Monkeys with a Virtual Hand
The virtual hand is capable of tactile perception
Center for Bioelectric Interfaces presents at OHBM 2019
We have made four poster presentations at OHBM-2019, a major neuroscience conference held in Rome and sponsored by the Organization for Human Brain Mapping.
Conference «MEG Nord 2019»
CBI Director Prof. Alex Ossadtchi has made a presentation at MEG Nord 2019 to have taken place May 8-10, in Jyväskylä, Finland.
CBI team at Skolkovo Robotics 2019
On April 16, 2019, Alex Ossadtchi and Mikhail Lebedev have made a keynote speech at the Skolkovo Robotics 2019 event. Nickolay Smetanin has also demoed a neurointerface designed at the CBI to control an exoskeleton (made by the Russian company ExoAtlet).
CBI is featured on Russia-24 channel
Russia-24 aired a story in which Alex Ossadtchi, Ksenia Volkova, Nickolay Smetanin, Alexander Belyayev and Alexandra Kuznetsova talk about the CBI and its research: an exoskeleton-controlling neurointerface, myographic interface to control a hand avatar and the main project to develop an invasive neurointerface to be used in clinical settings.
BCI-controlled exoskeleton: neurorehabilitation for patients with impaired lower limb function
At the international symposium ExoRehab Spotlights 2018 held on December 5 in Moscow, researchers of the Center for Bioelectric Interfaces Nikolai Smetanin, Aleksandra Kuznetsova and Alexei Ossadtchi presented Russia's first EEG-based neural interface that uses lower limb motor imagery for exoskeleton control. Alexei Ossadtchi also made a presentation "BCI for walk decoding". This work has been a collaboration with the Russian company ExoAtlet.
Megagrant #14.641.31.0003 "Bi-directional ECoG BCIs for contol, stimulation and communication". Lead scentist: Mikhail Lebedev.
Successful ECoG decoding in real time!
Today marks the successful completion of the first year of our research project funded by Megagrant #14.641.31.0003 "Bi-directional ECoG BCIs for control, stimulation and communication" (lead scientist: Mikhail Lebedev).
Our Center on the international conference Brain-Computer Interface: Science and Practice in Samara
Russian ministry of Education and Science Government grant ag. No 14.641.31.0003, Megagrant to Mikhail Lebedev.
Alexei Ossadtchi made presentation "Towards zero-latency neurofeedback" about the algorithms developing in our Center. Mikhail Lebedev gave a talk "Decoding, but what?" about the invasive neurointerfaces. Moreover, our research assistants Julia Nurislamova, Ignatii Dubishkin and Alexander Belyaev won prizes for best posters.
