Study of the perception of written speech using functional tomography based on electroencephalography data

M. N. Ustinin ; S. D. Rykunov ; A. I. Boyko ; E. F. Tarasov ; I. V. Zhuravlev ; M. A. Polikarpov ; T. A. Ryabov ; I. A. Filatov ; A. Yu. Yurenya ; V. Ya. Panchenko

Matematičeskaâ biologiâ i bioinformatika, Tome 16 (2021) no. 1, pp. 1-14.

Résumé

The spectral and spatial characteristics of the electroencephalograms recorded during the perception of written speech were studied. For the experimental study, four groups were formed, each containing 100 words: words with a positive emotional rating, words with a negative emotional rating, words with concrete meanings, and words with abstract meanings. A separate experiment was conducted for each group with the subjects. Words were represented by white text on a black background, each word was presented for 1000 ms, after the presentation of the stimulus there was a pause of 500 ms. Brain activity was recorded using an electroencephalograph with 19 leads, arranged according to the 10–20 scheme. For detailed quantitative analysis of this activity, method of functional tomography of the brain, based on electroencephalography data, was used. This method is based on the Fourier transform of multichannel encephalographic data and the localization of individual spectral components. The method makes it possible to single out and stably localize in space various spectral features of the brain activity studied in experiments on speech research. The frequency band from 8 to 30 Hz was analyzed; for all spectral components in this band, the inverse problem was solved in the approximation of an equivalent current dipole in a single-layer spherical conductor, without any restrictions on the position of the source. As a result, three-dimensional maps of activity were built - the functional structures of the brain. The presentation of these functional structures on magnetic resonance imaging allows one to study the frequency and spatial characteristics of responses to various speech stimuli.

Bibliographie

[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, 21:1 (1995), 116–133 | DOI

[2] Forster K.I., Chambers S.M., “Lexical access and naming time”, Journal of Verbal Learning Verbal Behavior, 12:6 (1973), 627–635 | DOI

[3] Oldfield R.C., Wingfield A., “Response latencies in naming objects”, The Quarterly Journal of Experimental Psychology, 17:4 (1965), 273–281 | DOI

[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, 33 (2001), 73–79 | DOI

[5] Leontev A.N., “Opyt strukturnogo analiza tsepnykh assotsiativnykh ryadov (eksperimentalnoe issledovanie)”: Leontev A.N., Izbrannye psikhologicheskie proizvedeniya, v. II, Pedagogika, M., 1983, 50–71

[6] Petrenko V.F., Kucherenko V.V., Nistratov A.A., “Vliyanie affekta na semanticheskuyu organizatsiyu znachenii”, Tekst kak psikholingvisticheskaya realnost, Institut yazykoznaniya RAN, M., 1982, 60–80

[7] Shakhovskii V.I., Lingvisticheskaya teoriya emotsii, Gnozis, M., 2008, 416 pp.

[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, 10 (2016), 506 pp. | DOI

[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, no. 3, 288–304 | DOI

[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, 113:40 (2016), 11366–11371 | DOI

[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, 14:1 (2002), 11–23 | DOI

[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, 13:6 (2001), 844–853 | DOI

[14] Naatanen R., Paavilainen P., Tiitinen H., Jiang D., Alho K., “Attention and MMN”, Psychophysiology, 30 (1993), 436–450 | DOI

[15] Kropotov Yu.D., Kolichestvennaya EEG, kognitivnye VP mozga cheloveka i neiroterapiya, Donetsk, 2010, 512 pp.

[16] Revenok E.V., Gnezditskii V.V., Korepina O.S., “Vyzvannye potentsialy v otsenke stareniya i dementsii”, Opyt primeneniya vyzvannykh potentsialov v klinicheskoi praktike, eds. V.V. Gnezditskii, A.M. Shamshinova, AOZT «Antidor», M., 2001, 160–182

[17] Pfurtscheller G., “Mapping event-related potentials and tool derivation”, EEG and Clin. Neurophysiol, 70 (1988), 190–193 | DOI

[18] Polich J., Squire L.R., “P300 from amnesic patients with bilateral hippocampal lesions”, EEG and Clin. Neurophysiol., 86 (1993), 408–417 | DOI

[19] Naatanen R., “MMN: a powerful tool for cognitive neuroscience”, Ear and Hearing, 16:1 (1995), 6–18 | DOI

[20] Coles M.G., Rugg M.D., Event-related brain potentials: An introduction, Oxford University Press, 1995

[21] Handy T.C., Event-related potentials: A methods handbook, MIT Press, 2005, 416 pp.

[22] Luck S., An introduction to the event-related potential technique, 2nd ed., MIT Press, 2014, 416 pp.

[23] Pfurtscheller G., Da Silva F.L., “Event-related EEG/MEG synchronization and desynchronization: Basic principles”, Clinical Neurophysiology, 110:11 (1999), 1842–1857 | DOI

[24] Woodman G.F., “A brief introduction to the use of event related potentials in studies of perception and attention”, Attention, Perception, Psychophysics, 72:8 (2010), 2031–2046 | DOI

[25] Arnaud L., Sato M., M?nard L., Gracco V.L., “Repetition suppression for speech processing in the associative occipital and parietal cortex of congenitally blind adults”, PLoS One, 8:5 (2013), e64553 | DOI

[26] Grill-Spector K., Henson R., Martin A., “Repetition and the brain: Neural models of stimulus-specific effects”, Trends in Cognitive Sciences, 10:1 (2006), 14–23 | DOI

[27] Henson R.N., “Neuroimaging studies of priming”, Progress in Neurobiology, 70:1 (2003), 53–81 | DOI

[28] Mayrhauser L., Bergmann J., Crone J., Kronbichler M., “Neural repetition suppression: Evidence for perceptual expectation in object-selective regions”, Frontiers in Human Neuroscience, 8 (2014), 225 pp. | DOI

[29] Summerfield C., Trittschuh E.H., Monti J.M., Mesulam M.-M., Egner T., “Neural repetition suppression reflects fulfilled perceptual expectations”, Nature Neuroscience, 11:9 (2008), 1004 | DOI

[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, 117:9 (2020), 4942–4947 | DOI

[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, 8 (2014), 43 | DOI

[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, 9 (2015), 373 | DOI

[33] Ustinin M.N., Rykunov S.D., Boiko A.I., Maslova O.A., “Rekonstruktsiya funktsionalnoi struktury mozga cheloveka po dannym elektroentsefalografii”, Matematicheskaya biologiya i bioinformatika, 15:1 (2020), 106–117 | DOI

[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., 22:2 (1998), 324–33 | DOI

[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), 879716 | DOI

[36] Frigo M., Johnson S.G., “The Design and Implementation of FFTW3”, Proceedings of the IEEE, 93:2 (2005), 216–231 | DOI

[37] Belouchrani A., Abed-Meraim K., Cardoso J.-F., Moulines E., “A blind source separation technique using second-order statistics”, IEEE Trans. Signal Processing, 45 (1997), 434–444 | DOI

[38] Mosher J.C., Leahy R.M., Lewis P.S., “EEG and MEG: forward solutions for inverse methods”, IEEE Transactions on Biomedical Engineering, 46:3 (1999), 245–259 | DOI

[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, 105 (2015), 507–524 | DOI

[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, 27 (2014), 95–111 | DOI

[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, 140 (2016), 150–162 | DOI

[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), 5076865 | DOI

[43] Koessler L., Maillard L., Benhadid A., Vignal J.P., Braun M., Vespignani H., “Spatial localization of EEG electrodes”, Neurophysiol Clin., 37:2 (2007), 97–102 | DOI

[44] Chen S., He Y., Qiu H., Yan X., Zhao M., “Spatial Localization of EEG Electrodes in a TOF+CCD Camera System”, Front. Neuroinform, 13 (2019), 21 | DOI

[45] Taberna G.A., Marino M., Ganzetti M., Mantini D., “Spatial localization of EEG electrodes using 3D scanning”, J. Neural. Eng., 16:2 (2019), 026020 | DOI