SaaS Platform for Time Series Data Handling

The paper is devoted to the description of MathBrain, a cloud-based resource, which works as a “Software as a Service” model. It is designed to maximize the efficiency of the current technology and to provide a tool for time series data handling. The resource provides access to the following analysis methods: direct and inverse Fourier transforms, Principal component analysis and Independent component analysis decompositions, quantitative analysis, magnetoencephalography inverse problem solution in a single dipole model based on multichannel spectral data

SaaS Platform for Time Series Data Handling

The paper is devoted to the description of MathBrain, a cloud-based resource, which works as a “Software as a Service” model. It is designed to maximize the efficiency of the current technology and to provide a tool for time series data handling. The resource provides access to the following analysis methods: direct and inverse Fourier transforms, Principal component analysis and Independent component analysis decompositions, quantitative analysis, magnetoencephalography inverse problem solution in a single dipole model based on multichannel spectral data

SaaS Platform for Time Series Data Handling

The paper is devoted to the description of MathBrain, a cloud-based resource, which works as a “Software as a Service” model. It is designed to maximize the efficiency of the current technology and to provide a tool for time series data handling. The resource provides access to the following analysis methods: direct and inverse Fourier transforms, Principal component analysis and Independent component analysis decompositions, quantitative analysis, magnetoencephalography inverse problem solution in a single dipole model based on multichannel spectral data

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

A new method for the analysis and localization of brain activity has been developed, based on multichannel magnetic field recordings, over minutes, superimposed on the MRI of the individual. Here, a high resolution Fourier Transform is obtained over the entire recording period, leading to a detailed multi-frequency spectrum. Further analysis implements a total decomposition of the frequency components into functionally invariant entities, each having an invariant field pattern localizable in recording space. The method, addressed as functional tomography, makes it possible to find the distribution of magnetic field sources in space. Here, the method is applied to the analysis of simulated data, to oscillating signals activating a physical current dipoles phantom, and to recordings of spontaneous brain activity in ten healthy adults. In the analysis of simulated data, 61 dipoles are localized with 0.7 mm precision. Concerning the physical phantom the method is able to localize three simultaneously activated current dipoles with 1 mm precision. Spatial resolution 3 mm was attained when localizing spontaneous alpha rhythm activity in ten healthy adults, where the alpha peak was specified for each subject individually. Co-registration of the functional tomograms with each subject’s head MRI localized alpha range activity to the occipital and/or posterior parietal brain region. This is the first application of this new functional tomography to human brain activity. The method successfully provides an overall view of brain electrical activity, a detailed spectral description and, combined with MRI, the localization of sources in anatomical brain space