Xun Chen

TL;DR: This paper proposes a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA).

TL;DR: Zhang et al. as mentioned in this paper introduced a perceptual generative adversarial network (PGAN) to achieve high-quality reconstructions for inverse scattering problems (ISPs) by decoupling the full-wave reconstruction model into two steps, including coarse imaging of dielectric profiles by the back-propagation scheme, and a resolution enhancement of coarse results as an image-to-image translation task solved by a novel perceptual generator.

TL;DR: The results demonstrate that the proposed joint BSS method can effectively remove muscle artifacts while preserving EEG signals successfully, and is evaluated through numerical simulations in which EEG recordings are contaminated with muscle artifacts.

TL;DR: In this paper, a blind source separation framework was proposed by combining noise-assisted multivariate EMD (NAMEMD) and multiset canonical correlation analysis (MCCA).

TL;DR: This study proposes an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction, providing a promising solution for EEG-based seizure prediction.