학술논문

Brain source localization using a physics-driven structured cosparse representation of EEG signals
Document Type
Conference
Source
2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on. :1-6 Sep, 2014
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Signal Processing and Analysis
Electroencephalography
Abstracts
Europe
Physiology
Legged locomotion
Brain source localization
EEG
cosparsity
synchronous current activities
Language
ISSN
1551-2541
2378-928X
Abstract
Localizing several potentially synchronous brain activities with low signal-to-noise ratio from ElectroEncephaloGraphic (EEG) recordings is a challenging problem. In this paper we propose a novel source localization method, named CoRE, which uses a Cosparse Representation of EEG signals. The underlying analysis operator is derived from physical laws satisfied by EEG signals, and more particularly from Poisson's equation. In addition, we show how physiological constraints on sources, leading to a given space support and fixed orientations for current dipoles, can be taken into account in the optimization scheme. Computer results, aiming at showing the feasability of the CoRE technique, illustrate its superiority in terms of estimation accuracy over dictionary-based sparse methods and subspace approaches.