Neuroimaging Methods Group
Group Leader - Alexei Ossadtchi
Our ongoing projects include the following:
I. Methods for source analysis and source space connectivity analysis of MEG, EEG data for cognitive and clinical uses
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.
- 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
- D. Altukhov and A. Ossadtchi, GO-PSIICOS: Globally Optimized Power and Shift Independent Imaging of Coherent Sources, Brain connectivity workshop (BCW 2015), Sand Diego, CA, June 2015
- A. Ossadtchi, Interaction Space RAP-MUSIC for estimation of transient networks from MEG data, Proceedings of the 19th international conference on biomagnetism, Halifax, Canada, 2014
- A. Ossadtchi, R. Greenblatt, Cross-term deprived covariance approach as an extension of DICS for detection of cross-frequency coupling, Proceedings of the 18th international conference on biomagnetism, Paris, France, 2012
- A. Ossadtchi, P. Pronko, S. Baillet, M. Pflieger, T. Stroganova, Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping , Frontiers in Neuroinformatics, January, 2014, doi: 10.3389/fninf.2013.00053
- R.E. Greenblatt, M.E. Pflieger, A. Ossadtchi, Connectivity measures applied to human brain electrophysiological data, Journal of Neuroscience Methods 207 (2012) 1– 16
- R. van Lutterveld, S. D. Houlihan, P. Pal, M. D. Sacchet, C. McFarlane-Blake, P. R. Patel, J. S. Sullivan, Alex Ossadtchi, S. Druker, C. Bauer, J. A. Brewer, Source-space EEG neurofeedback links subjective experience with brain activity during meditation, Neuroimage (under review)
- 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
- S. L. Shishkin, A. A. Fedorova, Y. O. Nuzhdin, I. P. Ganin, A. E. Ossadtchi, B. B. Velichkovskiy, A. Y. Kaplan, B. M. Velichkovsliy. Towards high-speed eye-brain-computer interfaces: Combining the “single stimulus” paradigm and saccades to stimulus location, Vestnik Moskovskogo Universiteta, Seriya 14. Psikhologiya. 2013. N4
- 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).
III. Brain-Computer Interface
We have developed and tested the EEG-based Brain Computer Interface, which can easily classify up to 6 real and imaginary movements: the arms, the legs, the tongue and the resting state. The Interface is based on a well-established principle of the body parts representations in the servomotor cortex, which can help to infer patterns of electrical activity, differentiating various motor states.
|Alex Ossadtchi, PhD||Group leader|
|Dmitriy Altukhov (from 11.2015)||Ph.D. student|
|Nikolay Dagaev||research trainee|
|Nikolay Smetanin||junior researcher|
|Ksenia Volkova||research trainee|
|Eugene Kalenkovitch||M.Sc. student (2nd year)|
|Georgiy Sapozhnikov||M.Sc. student (2nd year)|
|Egor Selivanov||B.Sc. student|
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