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pro vyhledávání: '"Valdés Sosa P"'
Plenty of artifact removal tools and pipelines have been developed to correct the EEG recordings and discover the values below the waveforms. Without visual inspection from the experts, it is susceptible to derive improper preprocessing states, like
Externí odkaz:
http://arxiv.org/abs/2310.15194
Autor:
Hu, Shiang, Ruan, Jie, Langer, Nicolas, Bosch-Bayard, Jorge, Lv, Zhao, Yao, Dezhong, Valdes-Sosa, Pedro Antonio
The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, althou
Externí odkaz:
http://arxiv.org/abs/2310.11994
Autor:
Attia, Tal Pal, Robbins, Kay, Beniczky, Sándor, Bosch-Bayard, Jorge, Delorme, Arnaud, Lundstrom, Brian Nils, Rogers, Christine, Rampp, Stefan, Valdes-Sosa, Pedro, Truong, Dung, Worrell, Greg, Makeig, Scott, Hermes, Dora
Standardizing terminology to describe electrophysiological events can improve both clinical care and computational research. Sharing data enriched by such standardized terminology can support advances in neuroscientific data exploration, from single-
Externí odkaz:
http://arxiv.org/abs/2310.15173
Autor:
Vega-Hernandez, Mayrim, Galan-Garcia, Lidice, Perez-Hidalgo-Gato, Jhoanna, Ontivero-Ortega, Marlis, Garcia-Agustin, Daysi, Garcia-Reyes, Ronaldo, Bosch-Bayard, Jorge, Marinazzo, Daniele, Martinez-Montes, Eduardo, Valdes-Sosa, A, Pedro
Objective: We seek stable Electrophysiological Source Imaging (ESI) biomarkers associated with Gait Speed (GS) as a measure of functional decline. Towards this end we determine the predictive value of ESI activation and connectivity patterns of resti
Externí odkaz:
http://arxiv.org/abs/2307.11273
Kernel smooth is the most fundamental technique for data density and regression estimation. However, time-consuming is the biggest obstacle for the application that the direct evaluation of kernel smooth for $N$ samples needs ${O}\left( {{N}^{2}} \ri
Externí odkaz:
http://arxiv.org/abs/2204.07716
Akademický článek
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Autor:
Paz-Linares, Deirel, Gonzalez-Moreira, Eduardo, Areces-Gonzalez, Ariosky, Wang, Ying, Li, Min, Martinez-Montes, Eduardo, Bosch-Bayard, Jorge, Bringas-Vega, Maria L., Valdes-Sosa, Mitchel J., Valdes-Sosa, Pedro A.
Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks
Externí odkaz:
http://arxiv.org/abs/1810.01174
Autor:
González-Mitjans, A., Paz-Linares, D., Areces-Gonzalez, A., Li, M., Wang, Y., Bringas-Vega, ML., Valdés-Sosa, P. A
This paper introduces methods and a novel toolbox that efficiently integrates any high-dimensional Neural Mass Models (NMMs) specified by two essential components. The first is the set of nonlinear Random Differential Equations of the dynamics of eac
Externí odkaz:
http://arxiv.org/abs/2009.07479
The electrophysiological source imagine reconstruction is sensitive to the head model construction, which depends on the accuracy of the anatomical landmarks locations knowns as fiducials. This work describes how to perform automatic fiducials detect
Externí odkaz:
http://arxiv.org/abs/1912.07221
Autor:
Vega-Hernández, Mayrim, Sánchez-Bornot, José M., Lage-Castellanos, Agustín, Pérez-Hidalgo-Gato, Jhoanna, Palmero-Ledón, Darío, Alvarez-Iglesias, José E., García-Agustin, Daysi, Martínez-Montes, Eduardo, Valdés-Sosa, Pedro A.
Multiple penalized least squares (MPLS) models are a flexible approach to find adaptive least squares solutions required to be simultaneously sparse and smooth. This is particularly important when addressing real-life inverse problems where there is
Externí odkaz:
http://arxiv.org/abs/1911.01961