Results 1 to 10 of about 17,095 (204)
A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI [PDF]
Frontiers in Psychology, 2023 IntroductionComputational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging.
Henry W. Chase
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Second derivative General Linear Method in Nordsieck form
Journal of Numerical Analysis and Approximation Theory, 2019 This paper considers the construction of second derivative general linear methods (SD-GLM) from hybrid LMM and their transformation to Nordsieck GLM. How the Runge-Kutta starters for the methods can be derived are given.
Robert I. Okuonghae +1 more
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Frontiers in Neuroscience, 2022 The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation.
Parviz Ghaderi +8 more
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The DD$^G$-classifier in the functional setting [PDF]
, 2016 The Maximum Depth was the first attempt to use data depths instead of multivariate raw data to construct a classification rule. Recently, the DD-classifier has solved several serious limitations of the Maximum Depth classifier but some issues still ...
Cuesta-Albertos, Juan A. +2 more
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Second order transport from anomalies [PDF]
, 2013 We study parity odd transport at second order in derivative expansion for a non-conformal charged fluid. We see that there are 27 parity odd transport coefficients, of which 12 are non-vanishing in equilibrium.
Bhattacharyya, Sayantani +2 more
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Frontiers in Neuroscience, 2016 Conventional analysis of functional magnetic resonance imaging (fMRI) data using the general linear model (GLM) employs a neural model convolved with a canonical hemodynamic response function (HRF) peaking 5s after stimulation. Incorporation of a further
Fabien Cignetti +8 more
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A New SDIMSIM Methods with Optimized Region of Stability and Sustainable Development with Applications On Neutrosophic Data [PDF]
Neutrosophic Sets and SystemsGeneral Linear Methods (GLMs) were introduced as logical extensions of the conventional Runge–Kutta and linear multistep methods. In the scenario when second derivatives, in addition to first derivatives, can be computed, a GLM modification known as ...
A.Y. J. Almasoodi +3 more
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Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts
Brain Informatics, 2017 This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task.
Satoshi Suzuki
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SMART: A statistical framework for optimal design matrix generation with application to fMRI [PDF]
, 2009 The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from ...
Baumgartner, Richard +5 more
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Stability in quadratic torsion theories [PDF]
, 2017 We revisit the definition and some of the characteristics of quadratic theories of gravity with torsion. We start from the most general Lagrangian density quadratic in the curvature and torsion tensors.
Cembranos, Jose A. R. +3 more
core +4 more sources