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Third Generation MEEG Source Connectivity Analysis Toolbox (BC-VARETA 1.0) and Validation Benchmark
Eduardo Gonzalez-Moreira,Deirel Paz-Linares,Ariosky Areces-Gonzalez,Rigel Wang,Pedro A. Valdes-Sosa +4 more
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This paper presents a new toolbox for MEEG source activity and connectivity estimation: Brain Connectivity Variable Resolution Tomographic Analysis version 1.0 (BC-VARETA 1.1), which relies on the third generation of nonlinear methods for the analysis of resting state MeeG Time Series.Abstract:
This paper presents a new toolbox for MEEG source activity and connectivity estimation: Brain Connectivity Variable Resolution Tomographic Analysis version 1.0 (BC-VARETA 1.0). It relies on the third generation of nonlinear methods for the analysis of resting state MEEG Time Series. Into the state of the art of MEEG analysis, the methodology underlying our tool (BC-VARETA) brings out several assets. First: Constitutes a Bayesian Identification approach of Linear Dynamical Systems in the Frequency Domain, grounded in more consistent models (third generation). Second: Achieves Super-Resolution, through the iterative solution of a Sparse Hermitian Source Graphical Model. Third: Tackles efficiently in High Dimensional and Complex set up the estimation of connectivity. Fourth: Incorporates priors at the connectivity level by penalizing the groups of variables, corresponding to the Gray Matter anatomical segmentation, and including a probability mask of the anatomically plausible connections. Along with the implementation of our method, we include in this toolbox a benchmark for the validation of MEEG source analysis methods, that would serve for the evaluation of sophisticated methodologies (third generation). It incorporates two elements. First: A realistic simulation framework, for the generation of MEEG synthetic data, given an underlying source connectivity structure. Second: Sensitive quality measures that allow for a reliable evaluation of the source activity and connectivity reconstruction performance, based on the Spatial Dispersion and Earth Movers Distance, in both source and connectivity space.read more
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Graphical Models In Applied Multivariate Statistics
TL;DR: The graphical models in applied multivariate statistics is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
Journal Article
Partial directed coherence: a new concept in neural structure determination
Koichi Sameshima,Luiz A. Baccalá +1 more
TL;DR: A new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models is introduced.
Journal ArticleDOI
An Age-Adjusted EEG Source Classifier Accurately Detects School-Aged Barbadian Children That Had Protein Energy Malnutrition in the First Year of Life.
Maria Luisa Bringas Vega,Maria Luisa Bringas Vega,Yanbo Guo,Qin Tang,Fuleah A. Razzaq,Ana Calzada Reyes,Peng Ren,Deirel Paz Linares,Deirel Paz Linares,Lídice Galán García,Arielle G. Rabinowitz,Janina R. Galler,Jorge Bosch-Bayard,Jorge Bosch-Bayard,Pedro Valdes Sosa,Pedro Valdes Sosa +15 more
TL;DR: The findings indicate that the PEM group showed a significant decrease in alpha activity, suggestive of a delay in brain development, and may have far-reaching applicability in low resource settings.
Posted Content
supFunSim: : spatial filtering toolbox for EEG
TL;DR: The supFunSim library is a new Matlab toolbox which generates accurate EEG forward models and implements a collection of spatial filters for EEG source reconstruction, including linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum- Variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions.
Posted ContentDOI
Are Sources of EEG and MEG rhythmic activity the same? An analysis based on BC-VARETA
TL;DR: This issue is addressed by comparing eyes open EEG source spectra recorded from 70 subjects from the Cuban Human Brain Mapping project with the MEG of 70 Subjects from the Human Connectome Project, and finding out no differences.
References
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