### Speaker

Mrs
Ekaterina Oplachko
(Keldysh Institute of Applied Mathematics)

### Description

There is a cloud based resource MathBrain which provides users with tools for time series analysis. The most of methods is dedicated to magnetic- and electro encephalography (MEG, EEG) analysis which originally contains big amount of data to calculate. These methods of brain analysis are noninvasive, and the process looks like a registration of electro-magnetic activity. During the procedure, magnetic encephalograph device registers a magnetic field for several minutes, in hundreds of channels. Thus, as a result of these experiments specialists have big amount of data with complex structure. The resource suggests spectral methods, quantitative analysis, principal component analysis, independent component analysis and inverse problem solution [1]. From mathematical prospective the analysis is based mostly on Fourier transform method [2]. From technical side, Software as a Service platform gives such advantages as operating system independency, hardware capacity and opens a way for revising solutions in data-handling problems field [3, 4]. These pros are available, because the tool is provided as a “thin” client and user doesn’t have to install any application on local computer. The architecture of this resource contains several layers of abstraction which help to share hardware between tasks and can be used for balancing the load. The engine of the resource which handle data is written on Python language. The task queue works on JSON-RPC listener/task-dispatcher scripts. Such approach allows not to overload the hardware during the complex calculations.
References
1. Rykunov S.D., Oplachko E.S., Ustinin M.N., Llinás R.R. Methods for magnetic encephalography data analysis in MathBrain cloud service. Mathematical Biology and Bioinformatics. 2017. V.12. № 1. P. 176–185. doi: 10.17537/2017.12.176
2. Jansen B.J., Bourne J.R., Ward J.W. Spectral decomposition of EEG intervals using
Walsh and Fourier transforms. IEEE Trans. Biomed. Eng. 1981. V. 28. P. 836–838.
3. Alex Mu-Hsing Kuo. Opportunities and Challenges of Cloud Computing to Improve Health Care Services. J Med Internet Res. 2011;13(3):e67.
4. Oplachko E.S., Ustinin D.M., Ustinin M.N. Cloud Computing Technologies and their Application in Problems of Computational Biology. Mathematical Biology and Bioinformatics. 2013;8(2):449-466 (in Russ.). doi: 10.17537/2013.8.449
This work was partially supported by the Russian Foundation for Basic Research (grants 16-0700937, 16-07-01000, 17-07-00677, 17-07-00686), by the Program I.33P for Fundamental Research of the Russian Academy of Sciences, and by the CRDF Global (USA) (grants CRDF RB1-2027 and RUB-7095-MO-13).

### Primary author

Mrs
Ekaterina Oplachko
(Keldysh Institute of Applied Mathematics)

### Co-authors

Prof.
Mikhail Ustinin
(Keldysh Institute of Applied Mathematics)
Mr
Stanislav Rykunov
(Institute of Mathematical Problems of Biology RAS - the Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences)