Geodesic
Reconstruction of the human brain functional structure based on the electroencephalography data
Matematičeskaâ biologiâ i bioinformatika, Tome 15 (2020) no. 1, pp. 106-117.

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New method for the data analysis was proposed, making it possible to transform multichannel time series into the spatial structure of the system under study. The method was successfully used to investigate biological and physical objects based on the magnetic field measurements. In this paper we further develop this method to analyze the data of the experiments where the electric field is measured. The brain activity in the state of subject “eyes closed” was registered by the 19-channel electric encephalograph, using the 10-20 scheme. The electroencephalograms were obtained in resting state and with arbitrary hands motions. Detailed multichannel spectra were obtained by the Fourier transform of the whole time series. All spectral data revealed the broad alpha rhythm peak in the frequency band 9-12 Hz. For all spectral components in this band the inverse problem was solved, and the 3D map of the brain activity was calculated. The inverse problem was solved in elementary current dipole model for one-layer spherical conductor without any restrictions for the source position. The combined analysis of the magnetic resonance image and the brain functional structure leads to the conclusion that this structure generally corresponds to the modern knowledge about the alpha rhythm. The 3D map of the vector field of the dominating directions of the alpha rhythm sources was also generated. The proposed method can be used to study the spatial distribution of the brain activity in any spectral band of the electroencephalography data. 
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M. N. Ustinin; S. D. Rykunov; A. I. Boyko; O. A. Maslova. Reconstruction of the human brain functional structure based on the electroencephalography data. Matematičeskaâ biologiâ i bioinformatika, Tome 15 (2020) no. 1, pp. 106-117. https://geodesic-test.mathdoc.fr/item/MBB_2020_15_1_a1/

[1] H. H. Jasper, “Report of the committee on methods of clinical examination in electroencephalography”, Electroencephalography and Clinical Neurophysiology, 10:2 (1958), 370–375 | DOI

[2] D. Goldman, “The clinical use of the “average” reference electrode in monopolar recording”, Electroencephalogr. Clin. Neurophysiol., 2 (1950), 209–212 | DOI

[3] C. M. Michel, D. Brunet, “EEG Source Imaging: A Practical Review of the Analysis Steps”, Front. Neurol., 10 (2019), 325 | DOI

[4] P. Krauss, A. Schilling, J. Bauer, K. Tziridis, C. Metzner, H. Schulze, M. Traxdorf, “Analysis of Multichannel EEG Patterns During Human Sleep: A Novel Approach”, Front. Hum. Neurosci., 12 (2018), 121 | DOI

[5] S. J. M. Smith, “EEG in the diagnosis, classification, and management of patients with epilepsy”, Journal of Neurology, Neurosurgery Psychiatry, 76 (2005), ii2–ii7 | DOI

[6] H. Bin Yoo, E. O.d. l. Concha, D. De Ridder, B. A. Pickut, S. Vanneste, “The Functional Alterations in Top-Down Attention Streams of Parkinson-s disease Measured by EEG”, Sci. Rep., 8 (2018), 10609 | DOI

[7] U. Smailovic, V. Jelic, “Neurophysiological Markers of Alzheimer's Disease: Quantitative EEG Approach”, Neurol. Ther., 8 (2019), 37–55 | DOI

[8] A. V. Bocharov, G. G. Knyazev, A. N. Savostyanov, T. N. Astakhova, S. S. Tamozhnikov, “EEG dynamics of spontaneous stimulus-independent thoughts”, Cogn. Neurosci., 10:2 (2019), 77–87 | DOI

[9] H. Berger, “Über das elektroenkephalogramm des menschen”, Arch. Psychiatr. Nervenkrankh., 87 (1929), 527–570 | DOI

[10] M. Halgren, I. Ulbert, H. Bastuji, D. Fabó, L. Erőss, M. Rey, O. Devinsky, W. K. Doyle, R. Mak-McCully, E. Halgren, L. Wittner, P. Chauvel, G. Heit, E. Eskandar, A. Mandell, S. S. Cash, “The generation and propagation of the human alpha rhythm”, PNAS, 116:47 (2019), 23772–23782 | DOI

[11] E. Basar, “A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology”, Int. J. Psychophysiol., 86:1 (2012), 1–24 | DOI

[12] P. L. Nunez, B. M. Wingeier, R. B. Silberstein, “Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks”, Hum. Brain. Mapp., 13 (2001), 125–164 | DOI

[13] E. Barzegaran, V. Y. Vildavski, M. G. Knyazeva, “Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior”, Sci. Rep., 7 (2017), 8249 | DOI

[14] E. Olejarczyk, P. Bogucki, A. Sobieszek, “The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation”, Front. Neurosci., 11 (2017), 506 | DOI

[15] R. R. Llinás, M. N. Ustinin, Precise Frequency-Pattern Analysis to Decompose Complex Systems into Functionally Invariant Entities, US Patent App. Publ. 20160012011 A1 01/14/2016

[16] R. R. Llinás, M. N. Ustinin, “Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization”, Front. Neural Circuits, 8 (2014), 43 | DOI

[17] R. R. Llinás, M. N. Ustinin, S. D. Rykunov, A. I. Boyko, V. V. Sychev, K. D. Walton, G. M. Rabello, J. Garcia, “Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data”, Front. Neurosci., 9 (2015) | DOI

[18] R. R. Llinás, M. Ustinin, S. Rykunov, K. D. Walton, G. M. Rabello, J. Garcia, A. Boyko, V. Sychev, “Noninvasive muscle activity imaging using magnetography”, PNAS, 117:9 (2020), 4942–4947 | DOI

[19] R. R. Llinás, M. Ustinin, S. Rykunov, K. D. Walton, G. M. Rabello, J. Garcia, A. Boyko, V. Sychev, “Data from “Noninvasive muscle activity imaging using magnetography””, Open Science Framework, 2019 | DOI

[20] Project BCI EEG motor activity data set: Brain Computer Interface research at NUST Pakistan, (accessed 12.04.2020) https://sites.google.com/site/projectbci/

[21] C. J. Holmes, R. Hoge, L. Collins, R. Woods, A. W. Toga, A. C. Evans, “Enhancement of MR images using registration for signal averaging”, J. Comput. Assist. Tomogr., 22:2 (1998), 324–33 | DOI

[22] F. Tadel, S. Baillet, J. C. Mosher, D. Pantazis, R. M. Leahy, “Brainstorm: A User-Friendly Application for MEG/EEG Analysis”, Computational Intelligence and Neuroscience, 2011 (2011), 879716 | DOI

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

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

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

[26] M. N. Ustinin, S. D. Rykunov, A. I. Boiko, O. A. Maslova, K. D. Volton, R. R. Linas, “Otsenka napravlenii elementarnykh istochnikov alfa-ritma metodom funktsionalnoi tomografii mozga cheloveka po dannym magnitnoi entsefalografii”, Matematicheskaya biologiya i bioinformatika, 13:2 (2018), 426–436 | DOI

[27] V. M. Verkhlyutov, Schuchkin Yu.V, V. L. Ushakov, V. B. Strelets, Yu. A. Pirogov, “Otsenka lokalizatsii i dipolnogo momenta istochnikov alfa i teta ritmov EEG s ispolzovaniem klasternogo analiza v norme i u bolnykh shizofreniei”, Zhurn. vyssh. nervn. deyat., 56:1 (2006), 47–55

[28] V. Montes-Restrepo, P. Van Mierlo, G. Strobbe, S. Staelens, S. Vandenberghe, H. Hallez, “Influence of skull modeling approaches on EEG source localization”, Brain Topography, 27 (2014), 95–111 | DOI

[29] Y. Huang, L. C. Parra, S. Haufe, “The NewYork Head — a precise standardized volume conductor model for EEG source localization and tES targeting”, NeuroImage, 140 (2016), 150–162 | DOI

[30] Y. Céspedes-Villar, J. D. Martinez-Vargas, G. Castellanos-Dominguez, “Influence of patient-specific head modeling on EEG source imaging”, Computational and Mathematical Methods in Medicine, 2020, 5076865 | DOI

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

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

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

[34] S. D. Rykunov, E. D. Rykunova, A. I. Boiko, M. N. Ustinin, “Programmnyi kompleks «VirtEl» dlya analiza dannykh magnitnoi entsefalografii metodom virtualnykh elektrodov”, Matematicheskaya biologiya i bioinformatika, 14:1 (2019), 340–354 | DOI