Methods for magnetic encephalography data analysis in MathBrain cloud service
Matematičeskaâ biologiâ i bioinformatika, Tome 12 (2017) no. 1, pp. 176-185.

Voir la notice de l'article provenant de la source Math-Net.Ru

MathBrain is a cloud-based application, distributed under Software as a Service model. This application provides access to several algorithms of the multichannel encephalography data analysis. Spectral methods include direct and inverse Fourier transforms and quantitative analysis. Statistical methods involve principal component analysis and independent component analysis. The field maps of elementary components can be used to solve the magnetic encephalography inverse problem and to display the result at the magnetic resonance image. The application is designed to be used in human brain studies without mathematical training.
@article{MBB_2017_12_1_a11,
     author = {S. D. Rykunov and E. S. Oplachko and M. N. Ustinin and R. R. Llin\'as},
     title = {Methods for magnetic encephalography data analysis in {MathBrain} cloud service},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {176--185},
     publisher = {mathdoc},
     volume = {12},
     number = {1},
     year = {2017},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/MBB_2017_12_1_a11/}
}
TY  - JOUR
AU  - S. D. Rykunov
AU  - E. S. Oplachko
AU  - M. N. Ustinin
AU  - R. R. Llinás
TI  - Methods for magnetic encephalography data analysis in MathBrain cloud service
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2017
SP  - 176
EP  - 185
VL  - 12
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2017_12_1_a11/
LA  - en
ID  - MBB_2017_12_1_a11
ER  - 
%0 Journal Article
%A S. D. Rykunov
%A E. S. Oplachko
%A M. N. Ustinin
%A R. R. Llinás
%T Methods for magnetic encephalography data analysis in MathBrain cloud service
%J Matematičeskaâ biologiâ i bioinformatika
%D 2017
%P 176-185
%V 12
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2017_12_1_a11/
%G en
%F MBB_2017_12_1_a11
S. D. Rykunov; E. S. Oplachko; M. N. Ustinin; R. R. Llinás. Methods for magnetic encephalography data analysis in MathBrain cloud service. Matematičeskaâ biologiâ i bioinformatika, Tome 12 (2017) no. 1, pp. 176-185. http://geodesic.mathdoc.fr/item/MBB_2017_12_1_a11/

[1] Dietsch G., “Fourier-analyse von elektroenzephalogrammen des menschen”, Pflügers Arch. Ges. Physiol., 230 (1932), 106–112 | DOI

[2] Jansen B. J., Bourne J. R., Ward J. W., “Spectral decomposition of EEG intervals using Walsh and Fourier transforms”, IEEE Trans. Biomed. Eng., 28 (1981), 836–838 | DOI

[3] Halliday D. M., Rosenberg J. R., Amjad A. M., Breeze P., Conway B. A., Farmer S. F., “A frame work for the analysis of mixed time series point process data-theory and application to study of physiological tremor, single motor unit discharges and electromyogram”, Prog. Biophys. Mol. Biol., 64 (1995), 237–238 | DOI

[4] Cooley J. W., Tukey J. W., “An algorithm for the machine calculation of complex fourier series”, Math. Comput., 19 (1965), 297–301 | DOI | MR

[5] Miyashita T., Ogawa K., Itoh H., Arai H., Ashidagawa M., Uchiyama M., Koide Y., Andoh T., Yamada Y., “Spectral analyses of electroencephalography and heart rate variability during sleep in normal subjects”, Auton. Neurosci., 103 (2003), 114–120 | DOI

[6] Weiss S., Rappelsberger P., “Long-range EEG synchronization during word encoding correlates with successful memory performance”, Cogn. Brain Res., 9 (2000), 299–312 | DOI

[7] Jarvis M. R., Mitra P. P., “Sampling properties of the spectrum and coherency of sequences of acton potentials”, Neural Comput., 13 (2001), 717–749 | DOI

[8] Delorme A., Makeig S., “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis”, J. Neurosci. Methods, 134 (2004), 9–21 | DOI

[9] Garcia-Rill E., Moran K., Garcia J., Findley W. M., Walton K., Strotman B., Llinás R. R., “Magnetic sources of the M50 response are localized to frontal cortex”, Clin. Neurophysiol., 119 (2008), 388–398 | DOI

[10] Muthuraman M., Galka A., Deuschl G., Heute U., Raethjen J., “Dynamical correlation of non-stationary signals in time domain — a comparative study”, Biomed. Signal Process. Control, 5 (2010), 205–213 | DOI | MR

[11] Llinás R. R., I of the Vortex. From Neurons to Self, MIT Press, Cambrige, MA, 2001

[12] 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

[13] Llinás R. R., Ustinin M. N., “Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization”, Frontiers in Neural Circuits, 29:8 (2014), 43 | DOI

[14] 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, 9 (2015), 373 | DOI

[15] 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, 11:1 (2016), 127–140 (in Russ.) | DOI

[16] Ustinin M. N., Sychev V. V., Walton K. D., Llinás R. R., “New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn”, Mathematical Biology and Bioinformatics, 9:2 (2014), 464–481 | DOI

[17] Oostenveld R., Fries P., Maris E., Schoffelen J. M., “FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data”, Computational Intelligence and Neuroscience, 2011 (2011) | DOI

[18] Delorme A., Mullen T., Kothe C., Akalin Acar Z., Bigdely-Shamlo N., Vankov A., Makeig S., “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, 2011 (2011), 130714 | DOI

[19] 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

[20] Oplachko E. S., Ustinin D. M., Ustinin M. N., “Cloud Computing Technologies and their Application in Problems of Computational Biology”, Mathematical Biology and Bioinformatics, 8:2 (2013), 449–466 (in Russ.) | DOI

[21] Oliphant T. E., “Python for Scientific Computing”, Computing in Science Engineering, 9 (2007), 10–20 | DOI

[22] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V. et al., “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, 12 (2011), 2825–2830 | MR

[23] Python wrapper for FFTW3 library, (accessed 02.05.2017) https://github.com/pyFFTW/pyFFTW

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

[25] Nelder J. A., Mead R., “A Simplex Method for Function Minimization”, The Computer Journal, 7 (1965), 308–313 | DOI | MR

[26] Sarvas J., “Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem”, Physics in Medicine and Biology, 32 (1987), 11–22 | DOI

[27] Karhunen K., “Uber lineare Methoden in der Wahrscheinlichkeitsrechnung”, Ann. Acad. Sci. Fennicae. Ser. A. I. Math.-Phys., 37 (1947), 1–79 | MR

[28] Loeve M., Probability theory II, Graduate Texts in Mathematics, 46, Springer-Verlag, 1978 | DOI | MR

[29] Hyvärinen A., Oja E., “Independent component analysis: algorithms and applications”, Neural Networks, 13:4–5 (2000), 411–430 | DOI