학술논문

Brain Source Localization using Constrained Low Rank Canonical Polyadic Decomposition
Document Type
Conference
Source
2018 52nd Asilomar Conference on Signals, Systems, and Computers Signals, Systems, and Computers, 2018 52nd Asilomar Conference on. :811-815 Oct, 2018
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Estimation
Electroencephalography
Brain modeling
Loading
Scalp
Optimization
Language
ISSN
2576-2303
Abstract
A new tensor-based source localization algorithm is presented in this paper. It is a single-step algorithm in which tensor decomposition with an efficient rank estimation and source localization are performed in only one single step. Contrary to the previous single-step tensor-based STS-SISSY (Space-Time-Spike Source Imaging based on Structured Sparsity) method recently proposed by our group, the proposed method is robust to tensor over-factoring and gives more accurate results. In addition to the structural constraints on the sources required for their localization, group sparsity constraints on the loading over-estimated matrices of the constructed STS tensor is used to estimate its rank. The numerical results show the efficiency of the proposed method over the STS-SISSY one.