Brain Source Localization using Constrained Low Rank Canonical Polyadic Decomposition

N. Taheri (1) , X. Han (1) , Ahmad Karfoul (1) , K. Ansari-Asl , I. Merlet (1) , Lotfi Senhadji (2, 1) , Laurent Albera (1) , A. Kachenoura (1)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 CRIBS - Centre de Recherche en Information Biomédicale sino-français

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. © 2018 IEEE.

Keywords: Numerical methods; Brain source localization; Canonical polyadic decompositions; Numerical results; Rank estimations; Source localization; Structural constraints; Structured sparsities; Tensor decomposition; Tensors

Domains: Life Sciences [q-bio]; Bioengineering

N. Taheri, X. Han, Ahmad Karfoul, K. Ansari-Asl, I. Merlet, et al.. Brain Source Localization using Constrained Low Rank Canonical Polyadic Decomposition. 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Oct 2018, Pacific Grove, United States. pp.811-815, ⟨10.1109/ACSSC.2018.8645475⟩. ⟨hal-02088222⟩