Материалы Международной научной конференции "Аналитические и вычислительные методы в теории вероятностей и ее приложениях", Data mining in predicting neuro-developmental scores from EEG data during coma due to cerebral malaria, 2017, стр. 380 -383
In this research we compare the performance of different data mining techniques in the analysis of electroencephalogram (EEG) data. We study the question od predicting post-comatose neuro-developmental scores based mainly on statistical features of the EEG recordings. We compare results from applying different data mining techniques, such as the Elastic Net, Lasso, Gaussian Support Vector Regression and Random Forest Regression. We also compare the results produced with different matrix completion methods.
Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.
Homogeneous and isotropic with respect to horizontal variables random fields are useful for study of geophysical (in particular, meteorological) functions of spatial-temporal variables. The following horizontal scale (30 — 3000 km), which is induced by the spatial scale of the observing grid for the Earth’s atmosphere and by the power of modern computers for solutions of the system of hydrothermodynamics equations, which included water phase transformations etc, is important for the weather forecast problems.
The correlation functions (CFs) of the random fields may be applied for the following goals:
1) For the optimal interpolation of the meteorological information from the points of observation into the points of a regular finite-difference grid, as well as (for the checking of some observations by other ones) into another point of the observation.
2) For the models’ testing, if a climate model simulates adequately not only mean fields, but the fields of the relative dispersions and CFs, too, then we should consider the climate model as a certain one.
The CFs are evaluated by the global checked archive of meteorological observations by meteorological sounds. A special regularization procedure provides the strong positive definiteness of the CFs. The areas in the Earth atmosphere, where the isotropy hypothesis is essentially not fulfilled, were localized by a special algorithm.
Let us consider an algorithm, which can construct atmospheric fronts that separate so named homogeneous synoptic atmospheric volumes. Then we can evaluate separately CFs for the ensemble of the pairs of points, which are in a unite volume and CFs for the ensemble of the pairs of points, which are in a various volumes. We can see the difference between the different CFs. The difference will be more for a better algorithm. So, we obtain a quality criterion for such algorithms. The statistical approach given possibility to optimize the algorithm with respect to a lot of numerical parameters. The optimal algorithm was exploited in the operative regime in Hydrometeorological Center of Russia. The similar algorithms of numerical construction of boundaries between homogeneous volumes by a discrete set of observations can be realized for various physical media.
We are proud to present the set of nal accepted papers for the fourth edition of the ITISE 2017 conference "International work-conference on Time Series" held in Granada (Spain) during September, 18-20, 2017. The ITISE 2017 (International work-conference on Time Series) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc, in the eld of time series analysis and forecasting. The aims of ITISE 2017 is to create a friendly environment that could lead to the establishment or strengthening of scientic collaborations and exchanges among attendees, and therefore, ITISE 2017 solicits high-quality original research papers (including signicant work-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation, and use of knowledge and new computational techniques and methods on forecasting in a wide range of elds.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEg and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
The paper analyzes storage peculiarities of satellite Earth remote sensing data time series. We propose methods for their compression based on the discovered peculiarities exploiting different schemes of Huffman coding. One of the proposed methods reaches 6% increase in the compression ratio (93%) in contrast to the deflate method used in Java SE6 (87%), for a time series of aerosol optical thickness derived from MODIS radiometer of TERRA satellite. Further improvement can be achieved by using the entropy coding of floating point numbers.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEG and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.