Year: 2014
Type: conference-paper
Abstract: Identifying the location and spatial extent of several highly correlated and simultaneously active brain sources from elec-troencephalographic (EEG) recordings and extracting the cor-responding brain signals is a challenging problem. In a recent comparison of source imaging techniques, the VB-SCCD algorithm, which exploits the sparsity of the variational map of the sources, proved to be a promising approach. In this paper, we propose several ways to improve this method. In order to adjust the size of the estimated sources, we add a reg-ularization term that imposes sparsity in the original source domain. Furthermore, we demonstrate the application of ADMM, which permits to efficiently solve the optimization problem. Finally, we also consider the exploitation of the temporal structure of the data by employing L1,2-norm regu-larization. The performance of the resulting algorithm, called L1,2-SVB-SCCD, is evaluated based on realistic simulations in comparison to VB-SCCD and several state-of-the-art tech-niques for extended source localization. Index Terms — EEG, extended source localization, ADMM, sparsity 1.
Source: HAL
Authors: Hanna Becker, Laurent Albera, Pierre Comon, Isabelle Merlet, Centre Inria, Rennes Bretagne Atlantique
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2395444532