Comparative study of methods for solving the correspondence problem in EMD applications

Abstract

Abstract:We address the correspondence problem which arises when applying empirical mode decomposition (EMD) to multi-trial and multi-subject data. EMD decomposes a signal into a set of narrow-band components named intrinsic mode functions (IMFs). The number of IMFs and their signal properties can be different between trials, channels and subjects. In order to assign IMFs with similar characteristics to each other, we compare two assignment methods, unbalanced assignment and k-cardinality assignment and two clustering algorithms, namely hierarchical clustering and density-based spatial clustering of applications with noise based on heart rate variability data of children with temporal lobe epilepsy.

Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science

“…), we found that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [6] is very effective to accomplish the clustering tasks for our visualization analysis. Surprisingly, there are very limited applications of this algorithm for 3D datasets so far [7][8][9][10]. Therefore, we have explored thoroughly, for the first time, this algorithm for its ability in detecting/identifying 3D features and creating visualization from large volumetric datasets.…”