Brain Source Localization using Constrained Low Rank Canonical Polyadic Decomposition

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...

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Bibliographic Details
Main Authors: Taheri, Nasrin, Han, Xu, Karfoul, Ahmad, Ansari-A.S.L., Karim, Merlet, Isabelle, Senhadji, Lotfi, Albera, Laurent, Kachenoura, Amar
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Electroencephalography
Estimation
Loading
Optimization
Scalp
Online Access:Request full text
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Summary: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.
ISSN:2576-2303
DOI:10.1109/ACSSC.2018.8645475