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Universal Design for Learning (UDL) has been proposed to facilitate the training processes and improve the learning outcome. The article reviews the theories in neuroscience-based learning and evidences of brain imaging studies that relates to the principles and design of UDL. Neuroimaging based assessment strategy is also reviewed as it may provide an objective means of evaluating learning outcomes...
We have previously reported a new sparse neuroimaging method (i.e. VB-SCCD) using the L1-norm optimization technology to solve EEG inverse problems. The new method distinguishes itself from other reported L1-norm methods since it explores the sparseness in a transform domain rather than in the original source domain. In the present study, we conducted a Monte Carlo simulation study to compare the...
This paper proposes the application of the directed transfer function (DTF) to a set of time-varying coefficients obtained through the use of a multivariate adaptive autoregressive (MVAAR) model. We define this time-varying measure of causality as the adaptive directed transfer function (ADTF) and compare its ability to discern changes in the causal interaction pattern as compared with the conventional...
This article discusses different approaches that have been proposed for multimodal neuroimaging, with special emphasis on the integration of electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI), and functional MRI (fMRI). Some applications will be shown to illustrate the efficacy and importance of these techniques in clinical and neuroscience studies. Finally,...
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