A green’s function-based Bi-dimensional empirical mode decomposition

Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

“…These results generally comply, in terms of activated regions, with results from an analysis of fMRI data which was taken jointly with our data [7, 62]. This means that these neurons are more active than others which also responded to the contour integration task.…”

“…Fig 8 presents an illustrative comparison of a saggital view of VIMF1 and ERM5 extracted from the late ERP N200. The VIMFS were extracted by using a new variant of a two dimensional empirical mode decomposition called GiT-BEEMD [63]. Hence, the superior precision in spatial localization of activity blobs corroborates the potential of EEMD/2DEEMD when analyzing functional neuroimages.…”

“…This means that these neurons are more active than others which also responded to the contour integration task. In [7, 62], the significant activation differences are highlighted in different regions like occipital, bilateral parietal, temporal and frontal regions (the test has been done using the same p-value, p = 0.001, for all). The fact that, comparing both modalities, occasionally different brain regions are involved in contour and non-contour processing renders the comparison suitable for further analysis.…”

Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation

“…Because H and C are constants, we know that logE|L(t+∆t)−L(t)| and log t ∆ have a linear relationship through formula (20). In Cartesian coordinates, (log…”

“…The surface interpolation is also an important guarantee for the bi-dimensional inherent modal function of image decomposition. Therefore, for the bi-dimensional empirical mode decomposition of the interpolation algorithm, many scholars put forward the corresponding solutions, such as the following: In 2005, at the first university in Paris, Nunes proposed a radial basis method, that can be the interpolation fit, but it has low interpolation efficiency and low precision [1]; In 2007, at the Chinese Academy of Sciences Institute of Automation Liu Zhongxuan proposed the cubic interpolation method, which improved the interpolation accuracy, but still had low interpolation efficiency [18]; In 2011, at the Chongqing University Mathematics and Statistics College of Deng Lei proposed B-spline interpolation method, although the interpolation accuracy has been improved, but did not solve the problem of interpolation efficiency [19]; In 2015, the University of Regensburg, in Germany Saad et al [20] proposed a fast interpolation algorithm based on the Green function, but the interpolation effect was not ideal; In 2016, at the China University of Geosciences Institute of Automation Xu proposed the Kriging envelope interpolation method, which attempted to solve the interpolation problem and was applied to geochemical identification. But the computational efficiency was still low [21].…”