Russian version English version
Volume 14   Issue 1   Year 2019
Rykunov S.D.1, Rykunova E.D.1, 2, Boyko A.I.1, Ustinin M.N.1

VirtEl - Software for Magnetic Encephalography Data Analysis by the Method of Virtual Electrodes

Mathematical Biology & Bioinformatics. 2019;14(1):340-354.

doi: 10.17537/2019.14.340.

References

 

  1. Su D.K., Ojemann J.G. Electrocorticographic Sensorimotor Mapping. Clin. Neurophysiol. 2013;124(6):1044-1048. doi: 10.1016/j.clinph.2013.02.114
  2. Richner T.J., Thongpang S., Brodnick S.K., Schendel A.A., Falk R.W., Krugner-Higby L.A., Pashaie R., Williams J.C. Optogenetic micro-electrocorticography for modulating and localizing cerebral cortexactivity. Journal of Neural Engineering. 2014;11(1):016010. doi: 10.1088/1741-2560/11/1/016010
  3. Aleksandrov M.V., Ulitin A.J. Intraoperative electrocorticography: possibility and prospect. Bulletin of the Russian Military Medical Academy. 2012;4(4):245-254.
  4. Roessler K., Heynold E., Buchfelder M., Stefan H., Hamerb H.M. Current value of intraoperative electrocorticography (iopECoG). Epilepsy & Behavior. 2019;91:20-24. doi: 10.1016/j.yebeh.2018.06.053
  5. Yue L., Zhang F., Lu X., Wan Y., Hu L. Simultaneous Recordings of Cortical Local Field Potentials and Electrocorticograms in Response to Nociceptive Laser Stimuli from Freely Moving Rats. J. Vis. Exp. 2019;143. Article No. e58686. doi: 10.3791/58686
  6. Nurmikko A.V., Donoghue J.P., Hochberg L.R., Patterson W.R., Song Y.-K., Bull Ch.W., Borton D.A., Laiwalla F., Park S., Ming Y., Aceros J. Listening to Brain Microcircuits for Interfacing With External World-Progress in Wireless Implantable Microelectronic Neuroengineering Devices: Experimental systems are described for electrical recording in the brain using multiple microelectrodes and short range implantable or wearable broadcasting units. Proceedings of the IEEE. 2010;98:375-388. doi: 10.1109/JPROC.2009.2038949
  7. Collinger J.L., Wodlinger B., Downey J.E., Wang W., Tyler-Kabara E.C., Weber D.J., McMorland A.JC., Velliste M., Boninger M.L., Schwartz A.B. High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet. 2013;381:557-564. doi: 10.1016/S0140-6736(12)61816-9
  8. Katyal K.D., Johannes M.S., Kellis S., Aflalo T., Klaes Ch., McGee T.G., Para M.P., Shi Y., Lee B., Pejsa K. et al. A collaborative BCI approach to autonomous control of a prosthetic limb system. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2014:1479-1482. doi: 10.1109/SMC.2014.6974124
  9. Wheeler J.J., Baldwin K., Kindle A., Guyon D., Nugent B., Segura C., Eskandar E.N. An implantable 64-channel neural interface with reconfigurable recording and stimulation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015:7837-7840 doi: 10.1109/EMBC.2015.7320208
  10. Wheeler J.J. Brain-Computer Interfaces using Electrocorticography and Surface Stimulation: Phd Dissertation. Washington University Open Scholarship, 2018. doi: Cite to nonCR doi: 10.7936/tzsm-qh09
  11. Romanelli P., Piangerelli M., Ratel D., Gaude C., Costecalde T., Puttilli C., Picciafuoco M., Benabid A., Torres N. A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface. Journal of Neurosurgery. 2018:1-14. doi: 10.3171/2017.10.JNS17400
  12. Ferris C.F., Tenney J. Functional Magnetic Resonance Imaging in Epilepsy: Methods and Applications Using Awake Animals. In: Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics. Eds. Carl L. Faingold, Hal Blumenfeld. Academic Press, 2014. P. 37-54. doi: 10.1016/B978-0-12-415804-7.00003-4
  13. Blumenthal‐Dramé A., Malaia E. Shared neural and cognitive mechanisms in action and language: The multiscale information transfer framework. WIREs Cogn. Sci. 2019;10. Article No. e1484. doi: 10.1002/wcs.1484
  14. Szaflarski J.P., Gloss D., Binder J.R., Gaillard W.D., Golby A.J., Holland S.K., Ojemann J., Spencer D.C., Swanson S.J., French J.A., Theodore W.H. Practice guideline summary: Use of fMRI in the presurgical evaluation of patients with epilepsy. Neurology. 2017;88(4):395-402. doi: 10.1212/WNL.0000000000003532
  15. Belyaev A., Peck Kyung K., Brennan N., Holodny ΐ. Clinical application of functional magnetic resonance imaging. Russian Electronic Journal of Radiology. 2014;4(1):14-24.
  16. Litvak V., Eusebio A., Jha A., Oostenveld R., Barnes G.R., Penny W.D., Zrinzo L., Hariz M.I., Limousin P., Friston K.J. et al. Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients. Neuroimage. 2010;50(4-3):1578-1588. doi: 10.1016/j.neuroimage.2009.12.115
  17. Pascual-Marqui R.D. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods. Find. Exp. Clin. Pharmaco. 2002;24(D):5-12.
  18. Llinás R.R., Ustinin M.N., Rykunov S.D., Boyko A.I., Sychev V.V., Walton K.D., Rabello G.M., Garcia J. Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data. Frontiers in Neuroscience. 2015;9. Article No. 373. doi: 10.3389/fnins.2015.00373
  19. Llinás R.R., Ustinin M.N. Precise Frequency-Pattern Analysis to Decompose Complex Systems into Functionally Invariant Entities: U.S. Patent. US Patent App. Publ. 20160012011 A1. 01/14/2016.
  20. Llinás R.R., Ustinin M.N. Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization. Front. Neural Circuits. 2014;8. Article No. 43. doi: 10.3389/fncir.2014.00043
  21. Belouchrani A., Abed-Meraim K., Cardoso J.-F., Moulines E. A blind source separation technique using second-order statistics. IEEE Trans. Signal Processing. 1997;45:434-444. doi: 10.1109/78.554307
  22. Sarvas J. Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 1987;32:11-22. doi: 10.1088/0031-9155/32/1/004
  23. Rykunova E.D., Rykunov S.D., Boyko A.I., Ustinin M.N. Software for the analysis of magnetic encephalography data by the method of virtual electrodes. In: Proceedings of the International Conference “Mathematical Biology and Bioinformatics”. Ed. V.D. Lakhno. Vol. 7. Pushchino: IMPB RAS, 2018. Paper No. e37. doi: 10.17537/icmbb18.91
  24. Rykunov S.D., Ustinin M.N., Polyanin A.G., Sychev V.V., Llinás R.R. Software for the Partial Spectroscopy of Human Brain. Mathematical Biology and Bioinformatics. 2016;11(1):127-140. doi: 10.17537/2016.11.127
  25. Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A., Jolicoeur P., Baillet S. OMEGA: The Open MEG Archive. Neuroimage. 2015;124:1182-1187. doi: 10.1016/j.neuroimage.2015.04.028
  26. Baçar E. A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology. International Journal of Psychophysiology. 2012;86(1):1-24. doi: 10.1016/j.ijpsycho.2012.07.002
  27. Cohen D., Givler E. Magnetomyography: magnetic fields around the human body produced by skeletal muscles. Appl. Phys. Lett. 1972;21(3):114-116. doi: 10.1063/1.1654294
Table of Contents Original Article
Math. Biol. Bioinf.
2019;14(1):340-354
doi: 10.17537/2019.14.340
published in Russian

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)
References

 

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