Russian version English version
Volume 16   Issue 1   Year 2021
Ustinin M.N.1, Rykunov S.D.1, Boyko A.I.1, Tarasov E.F.2, Zhuravlev I.V.2, Polikarpov M.A.3, Ryabov T.A.3,4, Filatov I.A.3,4, Yurenya A.Yu.3,4, Panchenko V.Ya.3,4

Study of the Perception of Written Speech Using Functional Tomography Based On Electroencephalography Data

Mathematical Biology & Bioinformatics. 2021;16(1):1-14.

doi: 10.17537/2021.16.1.

References

  1. Morrison C.M., Ellis A.W. Roles of word frequency and age of acquisition in word naming and lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1995;21(1):116–133. doi: 10.1037/0278-7393.21.1.116
  2. Forster K.I., Chambers S.M. Lexical access and naming time. Journal of Verbal Learning & Verbal Behavior. 1973;12(6):627–635. doi: 10.1016/S0022-5371(73)80042-8
  3. Oldfield R.C., Wingfield A. Response latencies in naming objects. The Quarterly Journal of Experimental Psychology. 1965;17(4):273–281. doi: 10.1080/17470216508416445
  4. Bird H., Franklin S., Howard D. Age of acquisition and imageability ratings for a large set of words, including verbs and function words. Behavior Research Methods, Instruments, & Computers. 2001;33:73–79. doi: 10.3758/BF03195349
  5. Leont'ev A.N. In: Izbrannye psikhologicheskie proizvedeniia (Selected psychological works). Moscow, 1983. V. II. P. 50–71 (in Russ.).
  6. Petrenko V.F., Kucherenko V.V., Nistratov A.A. In: Tekst kak psikholingvisticheskaia real'nost' (Text as a psycholinguistic reality). Moscow, 1982. P. 60–80.(in Russ.).
  7. Shakhovskii V.I. Lingvisticheskaia teoriia emotsii (Linguistic theory of emotions). Moscow, 2008. 416 p. (in Russ.).
  8. Liebenthal E., Silbersweig D.A., Stern E. The Language, Tone and Prosody of Emotions: Neural Substrates and Dynamics of Spoken-Word Emotion Perception. Frontiers in Neuroscience. 2016;10:506. doi: 10.3389/fnins.2016.00506
  9. Bradley M.M., Lang P.J. Affective norms for English words (ANEW): Instructionmanual and affective ratings: Technical Report C-1, The Center for Research in Psychophysiology, University of Florida. 1999.
  10. Bressler, S.L. Large-scale cortical networks and cognition. Brain Research Reviews. 1995. 20(3):288–304. doi: 10.1016/0165-0173(94)00016-I
  11. Piai V., Anderson K.L., Lin J.J., Dewar C., Parvizi J., Dronkers N.F., Knight R.T. Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proceedings of the National Academy of Sciences. 2016;113(40):11366–11371. doi: 10.1073/pnas.1603312113
  12. Fiebach C.J., Friederici A.D., Müller K., von Cramon D.Y. fMRI Evidence for Dual Routes to the Mental Lexicon in Visual Word Recognition. Journal of Cognitive Neuroscience. 2002;14(1):11–23. doi: 10.1162/089892902317205285
  13. Mayall K., Humphreys G.W., Mechelli A., Olson A., Price C.J. The Effects of Case Mixing on Word Recognition: Evidence from a PET Study. Journal of Cognitive Neuroscience. 2001;13(6):844–853. doi: 10.1162/08989290152541494
  14. Naatanen R., Paavilainen P., Tiitinen H., Jiang D., Alho K. Attention and MMN. Psychophysiology. 1993;30:436–450. doi: 10.1111/j.1469-8986.1993.tb02067.x
  15. Kropotov Iu.D. Kolichestvennaia EEG, kognitivnye VP mozga cheloveka i neiroterapiia (Quantitative EEG, cognitive EP of the human brain and neurotherapy). Donetsk, 2010. 512 p. (in Russ.).
  16. Revenok E.V., Gnezditskii V.V., Korepina O.S. In: Opyt primeneniia vyzvannykh potentsialov v klinicheskoi praktike (Experience in the use of evoked potentials in clinical practice). Eds. V.V. Gnezditskii, A.M. Shamshinova. Moscow, 2001. P. 160–182 (in Russ.).
  17. Pfurtscheller G. Mapping event-related potentials and tool derivation. EEG and Clin. Neurophysiol. 1988;70:190–193.
  18. Polich J., Squire L.R. P300 from amnesic patients with bilateral hippocampal lesions. EEG and Clin. Neurophysiol. 1993;86:408–417.
  19. Naatanen R. MMN: a powerful tool for cognitive neuroscience. Ear and Hearing. 1995;16(1):6–18. doi: 10.1097/00003446-199502000-00002
  20. Coles M.G., Rugg M.D. Event-related brain potentials: An introduction. Oxford University Press, 1995. doi: 10.1093/acprof:oso/9780198524168.003.0001
  21. Handy T.C. Event-related potentials: A methods handbook. MIT Press, 2005. 416 p. ISBN: 9780262083331.
  22. Luck S. An introduction to the event-related potential technique. MIT Press, 2014. 416 p. ISBN: 9780262525855.
  23. Pfurtscheller G., Da Silva F.L. Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology. 1999;110(11):1842–1857. doi: 10.1016/S1388-2457(99)00141-8
  24. Woodman G.F. A brief introduction to the use of event related potentials in studies of perception and attention. Attention, Perception, & Psychophysics. 2010;72(8):2031–2046. doi: 10.3758/BF03196680
  25. Arnaud L., Sato M., Ménard L., Gracco V.L. Repetition suppression for speech processing in the associative occipital and parietal cortex of congenitally blind adults. PLoS One. 2013;8(5). Article No. e64553. doi: 10.1371/journal.pone.0064553
  26. Grill-Spector K., Henson R., Martin A. Repetition and the brain: Neural models of stimulus-specific effects. Trends in Cognitive Sciences. 2006;10(1):14–23. doi: 10.1016/j.tics.2005.11.006
  27. Henson R.N. Neuroimaging studies of priming. Progress in Neurobiology. 2003;70(1):53–81. doi: 10.1016/S0301-0082(03)00086-8
  28. Mayrhauser L., Bergmann J., Crone J., Kronbichler M. Neural repetition suppression: Evidence for perceptual expectation in object-selective regions. Frontiers in Human Neuroscience. 2014;8:225. doi: 10.3389/fnhum.2014.00225
  29. Summerfield C., Trittschuh E.H., Monti J.M., Mesulam M.-M., Egner T. Neural repetition suppression reflects fulfilled perceptual expectations. Nature Neuroscience. 2008;11(9):1004. doi: 10.1038/nn.2163
  30. Llinás R.R., Ustinin M., Rykunov S., Walton K.D., Rabello G.M., Garcia J., Boyko A., Sychev V. Noninvasive muscle activity imaging using magnetography. PNAS. 2020;117(9):4942–4947. doi: 10.1073/pnas.1913135117
  31. 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
  32. 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. Front. Neurosci. 2015;9. Article No. 373. doi: 10.3389/fnins.2015.00373
  33. Ustinin M.N., Rykunov S.D., Boyko A.I., Maslova O.A. Reconstruction of the Human Brain Functional Structure Based on the Electroencephalography Data. Math. Biol. Bioinf. 2020;15(1):106-117. doi: 10.17537/2020.15.106
  34. Holmes C.J., Hoge R., Collins L., Woods R., Toga A.W., Evans A.C. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 1998;22(2):324–33. doi: 10.1097/00004728-199803000-00032
  35. Tadel F., Baillet S., Mosher J.C., Pantazis D., Leahy R.M. Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience. 2011;2011. ID 879716. doi: 10.1155/2011/879716
  36. Frigo M., Johnson S.G. The Design and Implementation of FFTW3. Proceedings of the IEEE. 2005;93(2):216–231. doi: 10.1109/JPROC.2004.840301
  37. 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
  38. Mosher J.C., Leahy R.M., Lewis P.S. EEG and MEG: forward solutions for inverse methods. IEEE Transactions on Biomedical Engineering. 1999;46(3):245–259. doi: 10.1109/10.748978
  39. Kielar A., Panamsky L., Links K.A., Meltzer J.A. Localization of electrophysiological responses to semantic and syntactic anomalies in language comprehension with MEG. NeuroImage. 2015;105:507–524. doi: 10.1016/j.neuroimage.2014.11.016
  40. Montes-Restrepo V., Van Mierlo P., Strobbe G., Staelens S., Vandenberghe S., Hallez H. Influence of skull modeling approaches on EEG source localization. Brain Topography. 2014;27:95–111. doi: 10.1007/s10548-013-0313-y
  41. Huang Y., Parra L.C., Haufe S. The NewYork Head – a precise standardized volume conductor model for EEG source localization and tES targeting. NeuroImage. 2016;140:150–162. doi: 10.1016/j.neuroimage.2015.12.019
  42. Céspedes-Villar Y., Martinez-Vargas J.D., Castellanos-Dominguez G. Influence of patient-specific head modeling on EEG source imaging. Computational and Mathematical Methods in Medicine. 2020;2020. Article ID 5076865. doi: 10.1155/2020/5076865
  43. Koessler L., Maillard L., Benhadid A., Vignal J.P., Braun M., Vespignani H. Spatial localization of EEG electrodes. Neurophysiol Clin. 2007;37(2):97–102. doi: 10.1016/j.neucli.2007.03.002
  44. Chen S., He Y., Qiu H., Yan X., Zhao M. Spatial Localization of EEG Electrodes in a TOF+CCD Camera System. Front. Neuroinform. 2019;13. Article No. 21. doi: 10.3389/fninf.2019.00021
  45. Taberna G.A., Marino M., Ganzetti M., Mantini D. Spatial localization of EEG electrodes using 3D scanning. J. Neural. Eng. 2019;16(2):026020. doi: 10.1088/1741-2552/aafdd1
Table of Contents Original Article
Math. Biol. Bioinf.
2021;16(1):1-14
doi: 10.17537/2021.16.1
published in Russian

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

 

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